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Follow-up Care Strategies After Treatment for Breast Cancer
Caprice C. Greenberg, MD, MPH; Heather B. Neuman, MD, MS; Jessica R. Schumacher, PhD; Menggang Yu, PhD; David Vanness, PhD
All investigators are affiliated with the University of Wisconsin, Madison.
Institution Receiving the PCORI award: Alliance for Clinical Trials in Oncology Original Project Title: Post-treatment Surveillance in Breast Cancer: Addressing an Urgent Need for Evidence PCORI Award Number: CE-1304-6543 HSRProj ID: HSRP20143518 ClinicalTrials.gov: NCT02171078
_______________________________ To cite this document, please use: Greenberg CC, Neuman HB, Schumacher JR, Yu M, Vanness D. (2019). Follow-up Care Strategies After Treatment for Breast Cancer. Washington, DC: Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/03.2020.CE.13046543
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Table of Contents
ABSTRACT ............................................................................................................................. 4
BACKGROUND ....................................................................................................................... 6
Overview ......................................................................................................................................... 6
Post-treatment Surveillance in Breast Cancer ................................................................................ 7
Table 1. National Guideline Recommendations for Surveillance to Detect Recurrence Following Active Treatment for Breast Cancer ..............................................................................................................7
PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS IN THE DESIGN AND CONDUCT OF RESEARCH AND DISSEMINATION OF FINDINGS .................................................................... 10
Types and Numbers of Stakeholders Involved ............................................................................. 10
Balance of Stakeholder Perspectives ............................................................................................ 10
Methods Used to Identify and Recruit Stakeholder Partners ...................................................... 11
Figure 1. Stakeholder topic identification process for the DEcIDE Cancer Consortium ......................... 12
Perceived or Measured Impact of Engagement ........................................................................... 12
METHODS ........................................................................................................................... 14
Study Overview ............................................................................................................................. 14
Table 2. Tumor Biology Groups: Types of Breast Cancer as Defined by Receptor Status ...................... 14
Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities ............... 15
Table 3. Modern-Era Adjuvant Therapies Used in Alliance Trials ........................................................ 16
Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body Imaging ...... 18
Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Post-treatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials ..................................................................................................... 24
Changes to the Original Study Protocol ........................................................................................ 26
RESULTS .............................................................................................................................. 27
Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities ............... 27
Table 4. Characteristics of Patients Enrolled in Alliance Trials ............................................................ 28
Figure 2. Annual hazards of breast cancer first recurrence by stage at diagnosis ................................ 29
Table 5. Cumulative Probability of Recurrence at 5 Years From Diagnosis .......................................... 29
Table 6. Relation Between Molecular Subtype, Tumor Size, Nodal Status, and Age on Time to First Recurrence ...................................................................................................................................... 23
Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body Imaging ...... 25
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Table 7. Characteristics of Stage II to III Breast Cancer Patients With Distant Recurrence by How Recurrence Was Detected (Sign/Symptom vs Asymptomatic Imaging Detected), Overall Population (Unweighted) ................................................................................................................................... 26
Table 8. Unweighted and Weighted Association Between Asymptomatic vs Symptom Detected Distant Recurrences and Time to Death by Molecular Subtype Risk Group for Women Diagnosed With Stage II to III Breast Cancera .......................................................................................................................... 25
Table 9. Percentage of Patients Surviving Until Years 3 to 4 and Median Survival for Patients With Triple Negative and HER2+ Stage II to III Breast Cancer, Propensity Weighted Based on Receipt of Surveillance Within 3 Years Of Diagnosis .......................................................................................... 25
Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Post-treatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials ..................................................................................................... 26
Figure 3. Alliance Breast Committee support for clinical trial ............................................................. 27
Figure 4. Stakeholder-derived decision support tool design considerations ........................................ 28
Figure 5. Base inputs and outputs for decision support tool .............................................................. 29
Figure 6. Initial page of data entry for clinicians in the decision support tool ..................................... 31
Figure 7. Output page in the decision support tool ............................................................................ 32
DISCUSSION ........................................................................................................................ 33
Context for Study Results ............................................................................................................. 33
Generalizability of Findings ........................................................................................................... 34
Implementation of Study Results ................................................................................................. 34
Subpopulation Considerations ...................................................................................................... 35
Study Limitations .......................................................................................................................... 35
Future Research ............................................................................................................................ 36
CONCLUSIONS ..................................................................................................................... 38
REFERENCES ........................................................................................................................ 39
ACKNOWLEDGMENTS .......................................................................................................... 43
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ABSTRACT Background: Breast cancer is a heterogeneous disease, with side effects, recurrence, and survival varying based on biological characteristics such as receptor status (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]—each positive [+] or negative [–]), stage at presentation, and type of treatment received. Yet, clinical guidelines for surveillance are not tailored to account for this variation in risk. As a result, the clinical application of surveillance strategies varies widely following active treatment for breast cancer.
Objectives and Aims: This project had the following 2 main objectives: (1) Develop an approach to personalize the timing of surveillance visits, including a decision support tool and evidence to inform guideline development; and (2) inform the design of future prospective randomized trials in breast cancer surveillance by identifying the highest-value subpopulations and surveillance tests with the greatest potential to improve outcomes. To do this, we proposed 3 specific aims: (1) Use data from legacy clinical trials to evaluate how recurrence varies by patient and cancer characteristics; (2) use existing and abstracted data from institutions accredited by the Commission on Cancer (CoC; these hospitals provide care for approximately 70% of all newly diagnosed cancers in the United States) to evaluate the effectiveness of surveillance imaging for distant recurrence; and (3) engage cancer survivors, providers, and researchers in developing an improved patient-centered approach to guide post-treatment care and in designing high-priority prospective trials.
Methods: To optimize surveillance strategies, the team examined cancer recurrence and survival for patients enrolled in 17 Alliance for Clinical Trials in Oncology trials. We also collected imaging and recurrence information from more than 10 000 randomly selected patients from 1200 CoC-accredited facilities across the United States and compared survival for patients whose recurrence was identified on imaging with that of patients whose cancer was found when they developed symptoms.
Results: Recurrence rates were lowest for ER+ or PR+ cancers (7.7%) and HER2+ cancers (15.2%) after 5 years for stage II and III, respectively, and highest for triple negative cancers (ER– and PR–: 11% and HER2: 38%; p < 0.001). The utilization of surveillance systemic imaging in our cohort was lower than that reported in previous publications, whereas the underutilization of recommended breast imaging was consistent with that of previous reports. Recurrence detected on surveillance imaging rather than by signs and symptoms was not associated with a survival advantage for ER+ or PR+, HER2– disease, which represents most breast cancers; however, recurrence detected on surveillance imaging was associated with a statistically significant improvement in survival for 2 other subtypes of cancer (ER– and PR–, HER2– as well as HER2+), which represent higher-risk disease.
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Conclusions: Using primary data collection, we found that the use of surveillance imaging for systemic disease was not as high as previous studies suggest; however, our observation of a survival advantage for certain subgroups of breast cancer when metastatic cancer is detected on surveillance imaging supports the need for a prospective trial. In the meantime, our results have informed development of a clinician-facing decision support tool that can facilitate shared decision-making regarding an individualized surveillance strategy by providing estimates of the expected risk and timing of recurrence and mortality, based on known patient risk factors. This tool will be tested in future prospective studies.
Study Limitations: Study limitations include those related to available data and medical records. For example, we were unable to assess family history, including genetic predispositions. In addition, patients enrolled in clinical trials may not reflect the general population in terms of race and competing risks. In our assessment of the role of surveillance imaging on survival, we used a data set of patients diagnosed in 2006-2007 to allow for the collection of 5-year follow-up information. HER2 status was not recorded consistently, and trastuzumab (Herceptin), the targeted therapy given to patients who have HER2+ tumors, was not routinely administered to them during these years. The magnitude of the survival advantage observed for this subgroup is, therefore, likely overestimated. Last, this observational study could not completely control for differences between asymptomatic and symptom-detected recurrences and could not consider biological differences that may confer differences in survival after different types of recurrences. However, findings were robust in sensitivity analyses, including models restricted by site of recurrence and to patients who received treatment for their recurrence.
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BACKGROUND
Overview
More than 15 million Americans have completed a course of treatment for cancer and
require surveillance for recurrence.1 With continued advances in cancer treatments and
increasing life expectancy, this number is expected to rise, reaching more than 20 million by the
year 2026.1 At the same time, health care costs are growing at unsustainable rates,2 leading to
recent calls for a re-evaluation of common medical tests and treatments that are expensive but
that lack high-quality data regarding risks and benefits for patients.3 As a result, advanced
radiographic imaging studies to monitor for cancer recurrence, such as computerized
tomography (CT) or positive emission tomography (PET), have come under scrutiny.4
Clinicians follow patients after active treatment for cancer for many reasons. These
include (1) detecting locoregional recurrence (cancer returning in the breast or adjacent lymph
nodes) or a second primary breast cancer, (2) detecting distant metastases, (3) monitoring for
treatment toxicities, (4) managing patient anxiety and fear of recurrence, and (5) assuring
continuation of primary care and other health services. However, the optimal approach to such
post-treatment surveillance is unknown.5 Although the American Society of Clinical Oncology
(ASCO) and National Comprehensive Cancer Network (NCCN) publish clinical practice
guidelines, these are based on limited data and largely reflect expert opinion. Additionally,
current guidelines do not consider individual patient variation, such as risk of recurrence or
treatment toxicity.
This issue is especially critical for breast cancer. With 5-year survival rates for
nonmetastatic breast cancer now exceeding 85% (> 98% for node-negative cancer), almost 3
million women require post-treatment surveillance annually in the United States.6
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Post-treatment Surveillance in Breast Cancer
Routine surveillance following active treatment for nonmetastatic breast cancer
includes interval history and physical examination as well as diagnostic imaging. Surveillance
can target the recurrence of breast and lymph node disease or distant metastases. Numerous
clinical practice guidelines for surveillance have been generated, most notably by ASCO and
NCCN (Table 1).7-9
Table 1. National Guideline Recommendations for Surveillance to Detect Recurrence Following Active Treatment for Breast Cancer
American Society of Clinical Oncology
National Comprehensive Cancer Network
• History and physical every 3 to 6 months for 3 years, then every 6 to 12 months for next 2 years, then annually
• Mammography no earlier than 6 months after definitive radiation, with subsequent mammograms every 6 to 12 months
• The use of laboratory testing, chest radiographs, liver ultrasounds, pelvic ultrasounds, computerized axial tomography, positron emission tomography, magnetic resonance imaging, and/or tumor markers is not recommended for routine follow-up in an otherwise asymptomatic patient with no specific findings on clinical examination.
• History and physical 1 to 4 times per year for 5 years, then annually
• Mammography every 12 months • In the absence of signs and
symptoms, there is no indication for laboratory or imaging studies for metastases screening.
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These follow-up recommendations have remained unchanged for the past 20 years,
owing mainly to a lack of new data. Evidence supporting current guidelines for nonmetastatic
breast cancer follow-up is based largely on clinical trials conducted in the 1980s, which predate
improvements in systemic imaging (CT scans, bone scans, PET scans, or magnetic resonance
imaging [MRI]) and modern systemic therapies that target specific receptors (estrogen receptor
[ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]). After
a biopsy, tumor cells are determined to be receptor positive or receptor negative, depending
on whether testing confirms the presence of ER, PR, or HER2 receptors in the cancer cells.
When hormones (estrogen or progesterone) attach to receptors or HER2 growth-promoting
proteins are present, tumor growth can be accelerated. Modern era therapies specifically target
these receptors and proteins and are consistent with appropriate clinical practice guidelines for
the management of women diagnosed with nonmetastatic breast cancer.
A 2005 Cochrane review of surveillance following treatment for early-stage breast
cancer identified 4 well-designed randomized trials that found no difference in either overall or
disease-free survival among 3055 women followed by intensive testing with liver ultrasound
and chest X-ray, compared with annual physician visits and mammography.10-14 Quality of life
was also similar for those trials in which it was measured. A 2016 updated Cochrane review
came to the same conclusion, but for the first time the authors noted that “results should be
interpreted with caution, bearing in mind that these studies were conducted almost two
decades ago and that additional trials incorporating new biological knowledge and improved
imaging technologies are needed.”15
As a result, no strong evidence supports or refutes current recommendations against
routine CT, PET, or MRI. The effectiveness of more aggressive surveillance, especially in specific
clinical situations, is unknown, as imaging techniques and targeted therapeutics have advanced
since the original trials were performed. In addition, currently no published data exist on the
frequency and duration of oncology clinic visits for history and physical examination.9 To inform
the frequency and duration of follow-up based on patient-specific risk and patterns of
detection, understanding the likelihood and timing of breast cancer recurrence is critical.
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In part because of this uncertainty, wide variation in the use of cancer follow-up care
services has been reported in practice, including both underuse and overuse of surveillance
services relative to the current guideline recommendations. Overall patient utilization of health
services is high following active treatment, with up to 30 episodes of health services per
survivor during the first year following active treatment for stage I or II breast cancer.16 Studies
suggest that only 60% to 70% of patients receive mammograms within the first year of
completing treatment (underuse), whereas upward of 60% to 80% of patients receive imaging
to survey for metastatic disease (overuse).17-20 Such variation may reflect a lack of definitive
data and the age of the studies that do exist, and underscores the significance of our proposal
and the potential for major impact on clinical practice and policy. Higher concordance with
guidelines is observed when strong scientific evidence supports the recommendation.21-23
The main goal of this study was to provide critical evidence necessary to inform the
development of a risk-stratified tailored approach to surveillance following active treatment for
breast cancer. We sought to inform a strategy that is more patient centered and effective than
the current untailored approach and that accounts for recent advances in imaging and
treatment. In particular, we had the following 2 main objectives: (1) Develop an approach to
personalize the timing of surveillance visits; and (2) inform design of future prospective
randomized trials in breast cancer surveillance by identifying the highest-value populations and
surveillance tests. To do this, we proposed 3 specific aims:
Specific aim 1. Determine the risk and patterns of recurrence according to tumor
characteristics, treatment modalities, and other patient characteristics in order to inform the
design of a patient-centered, risk-stratified, tailored approach to post-treatment surveillance.
Specific aim 2. Evaluate current utilization and comparative effectiveness of routine
surveillance breast MRI and advanced body imaging to detect recurrence and improve survival
following active treatment for stage II or III breast cancer.
Specific aim 3.Engage stakeholders to develop a patient-centered, risk-based tailored
approach to post-treatment surveillance and identify high-priority, feasible comparators to be
tested in prospective randomized trials.
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PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS IN THE DESIGN AND CONDUCT OF RESEARCH AND DISSEMINATION OF FINDINGS
Types and Numbers of Stakeholders Involved
Cancer survivors and patient advocates were engaged throughout the study through the
Alliance for Clinical Trials in Oncology Research Network (hereafter Alliance) Patient Advocate
Committee (PAC; N = 19). Three members of the PAC were also part of the core research team;
2 are breast cancer survivors. In addition, multidisciplinary breast cancer clinicians (medical,
surgical, and radiation oncologists) were engaged through the Alliance Breast Committee.
For our third component, we formed a multi-stakeholder advisory group to integrate
into our stakeholder engagement process. This group (N = 12) included patient advocates,
many of whom work with underrepresented communities, including Native American, Latina,
and other women of color. It also included oncology specialty providers (radiation oncologists,
radiologists, surgical oncologists, medical oncologists) from both community and academic
settings. This group was formed because patients and clinicians routinely described the
importance of understanding others’ perspectives regarding the content and communication of
follow-up strategies. Bringing these 2 sets of stakeholders together was critical for working
through key design considerations and interpreting and communicating results.
Balance of Stakeholder Perspectives
Feedback from team members indicated that we achieved an appropriate balance of
stakeholder perspectives. We achieved this balance partly because of semiannual meetings but
mostly because of the care with which the study team assembled the stakeholder group, as
described above. Meetings emphasized open and dynamic communication that was designed
to fully engage the diverse set of perspectives represented by the multidisciplinary stakeholder
group. Agendas were well defined, allowing all participants an opportunity to share their
perspectives. After discussing research findings, the project team often presented specific
questions to stakeholders that prompted conversation as well as solicited general feedback,
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comments, and suggestions. The team presented findings clearly and in an accessible manner,
encouraging participation from all stakeholders to make sure that everyone felt that his or her
contribution was valuable.
Methods Used to Identify and Recruit Stakeholder Partners
The process of stakeholder engagement for this study began in 2009 as a part of the
DEcIDE (Developing Evidence to Inform Decisions about Effectiveness) Cancer Consortium
funded by the Agency for Healthcare Research and Quality. Dr. Caprice Greenberg led the
stakeholders, a multidisciplinary group of patients, providers, researchers, payers, and
policymakers, in engagement activities to identify, prioritize, and operationalize topics for
comparative effectiveness research in cancer.24 Dr. Greenberg and her team identified and
recruited these stakeholders through existing relationships with professional organizations and
federal partners. The goal was to identify the topic with the greatest potential to improve the
care of cancer patients.
DEcIDE stakeholders identified post-treatment surveillance as the top priority, based on
the level of uncertainty about the optimal approach and the potential to change practice
significantly. Post-treatment surveillance also cuts across cancer types and affects survivorship,
an underrepresented component of the care continuum among the cancer comparative
effectiveness portfolio.
Figure 1 depicts the stakeholder process that led to identification of the study topic.
Toward the end of the DEcIDE engagement activities, the team recognized the need to engage a
broader group of clinical and patient stakeholders. This led investigators to partner with the
Alliance, an approach that continued throughout the design and execution of this PCORI grant,
in light of the team’s success during DEcIDE.
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Figure 1. Stakeholder topic identification process for the DEcIDE Cancer Consortium
Perceived or Measured Impact of Engagement
The following examples illustrate the impact of our stakeholder engagement; all were a
direct result of the study team’s engagement sessions:
1. Topic identification and study design: Before submission, we engaged multiple
stakeholders, as described as above, in identifying the high-priority topic. In addition,
stakeholders played a key role in determining that a randomized controlled trial was not
feasible at this time and provided key input for the design of our observational study.
Furthermore, they were essential in identifying patient-centered outcomes for inclusion
in the study and helping with the response to reviewers for our resubmission.
2. Operationalizing the study design: Based on feedback from stakeholders, we changed
the study design for aim 2 to stratified random sampling by stage rather than random
sampling across facilities to ensure robust estimates by stage at diagnosis.
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3. Designing the web-based decision support tool: We solicited and achieved stakeholder
consensus in selecting inputs, outputs, and the look and feel of the tool and in
identifying the primary audience (provider-facing to facilitate shared decision- making
rather than direct to patients). Stakeholders were adamant that the tool be flexible so
that outputs could be tailored to display only the outcomes that an individual patient
was interested in considering.
4. Finalizing variables and analytic methods: Medical and surgical oncologist stakeholders
were engaged in defining and classifying adverse events for the Alliance legacy trials in
aim 1, designing a rigorous approach for abstracting data on recurrence and defining the
intent of imaging for medical record abstraction as a part of aim 2, and selecting analytic
methods to ensure that clinicians can readily interpret findings and translate results to
the care of their patients.
5. Specifying high-priority comparators for future pragmatic trial(s) through a
combination of surveys and audience response units
6. Interpreting findings, including the implications of model results, and ensuring that
information is clearly communicated via the decision support tool
During our last stakeholder engagement session, one patient advocate who was
engaged continuously from the start to the end of the study had this to say about her
involvement:
It has been a great experience seeing the project from conception to completion. The project team was so open to our input, I always felt like a valued member of the team. The project addressed a need in the breast cancer community and addressed it in a way that took the patient perspective into consideration. It was a lot of work, and I appreciate the complexities of databases and mining data for meaningful information that matters to patients. . . . The team was always accessible to all of the advocates. . . . Providing all [stakeholders] information twice a year and asking for specific input at each meeting made us feel valued, useful and ensured that each meeting was productive.
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METHODS
Study Overview
This study has 3 specific aims, which will be considered in sequence. All 3 aims examine
breast cancer according to its biology, which is defined based on 3 receptors: ER, PR, and HER2.
Each receptor is classified as either positive or negative. Table 2 depicts the major types of
breast cancer that are included in this analysis.
Table 2. Tumor Biology Groups: Types of Breast Cancer as Defined by Receptor Status
Typea ER PR HER2 Abbreviation
Triple negative Negative Negative Negative ER/PR– HER2–
Triple positive Positivea Positivea Positive ER/PR+ HER2+
Hormone negative, HER2+
Negative Negative Positive ER/PR– HER2+
Hormone positive, HER2–
Positivea Positivea Negative ER/PR+ HER2–
HER2+ Positive or negative
Positive or negative
Positive ER/PR+ HER2+ ER/PR– HER2+
Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor. aOnly ER or PR, not necessarily both, need be positive for a cancer to be considered hormone positive. When numbers were small, HER2+ cancers were evaluated together regardless of ER/PR status.
In aim 1, we combined data from 17 trials previously conducted by the Alliance to
evaluate the risk and patterns of recurrence. The aim was to generate data to inform the
tailoring of frequency and duration of follow-up clinical visits for history and physical exams,
based on empirically derived risk estimates. This unique data set contains the most
comprehensive, detailed prospective data on locoregional breast cancer in existence.
For aim 2, cancer registrars at 1296 CoC-accredited institutions across the United States
performed primary data collection on more than 11 000 patients regarding the use of imaging
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after active breast cancer treatment. With these detailed data on the intent with which a scan
was ordered, we were able to assess whether imaging use was consistent with national
guideline recommendations. Furthermore, detailed and accurate data on recurrence allowed us
to compare survival for patients whose recurrence was detected by scan with survival for
patients whose recurrence was detected by the development of symptoms.
Finally, in aim 3, in close collaboration with our multiple stakeholders, we combined the
results from the first 2 aims to design a high-value prospective randomized trial. Data from aim
1 were also used to develop a web-based decision support tool that can inform shared decision-
making regarding surveillance strategies.
Our stakeholders informed us that a prospective randomized trial was not advisable in
this instance without a prior large-scale observational study to guide development, including
the definition of inclusion/exclusion criteria and selection of comparators, and to ensure
efficient and appropriate investment of resources. We therefore performed a retrospective
cohort analysis to investigate current practice, determine the likelihood and timing of
recurrence, and examine the effectiveness of routine advanced imaging in 2 unique national
data sets. Each aim is described in detail below.
Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities
Data Sources
We combined data from 17 legacy Alliance clinical trials that had previously enrolled
women diagnosed with stages I, II, or III breast cancer. We further restricted the sample to
women who received surgery and modern-era adjuvant therapies (Table 3) and had complete
staging, receptor status, and recurrence information (n = 10 357). This ensured that the risk and
patterns of recurrence and treatment toxicities would be assessed in a patient population
treated with modern era therapies, in which treatments are personalized based on tumor
biology.
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Table 3. Modern-Era Adjuvant Therapies Used in Alliance Trials
Receptor Status Group
Modern-era Adjuvant Therapies
Triple negative Cytotoxic chemotherapy
ER/PR+, HER2–
Endocrine therapy +/– Cytotoxic chemotherapy
HER2+ Trastuzumab-based therapy Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.
Study Setting
Aim 1 utilized data from previous clinical trials performed by the Cancer and Leukemia
Group B, North Central Cancer Treatment Group, and American College of Surgeons Oncology
Group. The study team identified all trials that enrolled patients with locoregional (stages I, II,
and III) breast cancer (N = 17). Data from each of these studies were standardized,
concatenated, and quality checked to allow for comparisons across trials. We chose to create
this unique aggregate data set because no existing data source has reliable recurrence or
survival information for an appropriately diverse and robust set of patients. These data will
foster greater understanding of the disease course to inform appropriate surveillance
recommendations.
Comparators
In this aim, we compared outcomes across types of breast cancer. The variables that
were available and consistent across trials included the following: age at diagnosis; ER status;
PR status; HER2 status; clinical and pathologic tumor stage (categorized as 0-2 cm, 2-5 cm, 5+
cm); and clinical and pathologic nodal stage (categorized as negative and positive). The
systematic collection of clinical and pathologic T (tumor size) and N (nodal) stage allows reliable
calculation of cancer stage at diagnosis. We used the categorization depicted in Table 2 for
making comparisons.
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Study Outcomes
The primary outcome is first recurrence as a binary variable. Secondary outcomes
include time to recurrence and overall and disease-specific survival. Each outcome is measured
as time from trial registration (as a proxy for diagnosis date).
Time Frame for the Study
Patients included in this study were diagnosed between 1997 and 2013. We chose to
include all data that were available rather than arbitrarily exclude trials based on year of
enrollment. We did exclude any patients who did not receive modern-era adjuvant treatment
regimens as defined in Table 3 and current standard surgical care to ensure that results would
reflect recurrence risk for the population of patients currently treated.
Analytical and Statistical Approaches
We estimated models predicting recurrence risk according to the tumor biology groups
described in Table 2 (ER/PR+, HER2–; triple negative; triple positive; ER/PR–, HER2+).25 We
assessed the relationship between receptor status and stage with time to first recurrence using
a stratified multivariable Cox proportional hazards model. The primary variable of interest was
tumor biology group, stratified by cancer stage (cancer staging for patients diagnosed with
early-stage breast cancer comprises tumor size and nodal status26). We also controlled for age
in the model. Data were censored at death, loss to follow-up, or at 5 years, whichever occurred
first.
Based on these models, we calculated cumulative probabilities of recurrence at 3 and 5
years, with associated confidence intervals, based on the hazard function for all combinations
of age, tumor biology groups, tumor size, and nodal status. These time frames correspond to
stakeholder consensus about time frames most relevant to determining risk during follow-up.
In addition, we plotted smoothed estimates of the hazards of recurrence at 1-year intervals
from the time of trial registration through the time of first recurrence. We used a log-rank test
to assess the difference in distribution of recurrence time by tumor biology group.
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Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body
Imaging
Data Sources
We used data from a national cancer registry, the National Cancer Database (NCDB),27,28
augmented with primary data collection via medical record abstraction. The NCDB is a joint
program of the American College of Surgeons, the CoC, and the American Cancer Society; it
captures 70% of newly diagnosed cancers in the United States.27,29
Facilities that are accredited by the CoC must attempt to follow all patients and report
data to the NCDB on a minimum of 90% of patients diagnosed and/or treated at their facilities,
each year from diagnosis until death, regardless of where they receive follow-up care. Trained
registrars at each facility collect information on diagnosis, first-course treatment factors, and
survival using the Facility Oncology Registry Data Standards manual.30 The CoC can use this data
collection platform to obtain additional data elements as necessary under its Special Study
mechanism, which involves the collection of specific data with the goal of improving cancer
care delivery.
Study Setting
This study utilized data collected from CoC-accredited hospitals through the NCDB. The
1231 CoC-accredited hospitals included in this study account for 70% of newly diagnosed
cancers in the United States. By randomly sampling a small number of patients at each hospital,
we were able to ensure generalizability to the entire population of patients being treated for
cancer in the United States.
Participants
The CoC identified a random sample of patients aged 18 and older with stage II and III
breast cancer at each CoC-accredited hospital. Patients with stage I breast cancer were
excluded because of their low rate of recurrence and low rate of surveillance imaging. Ten
patients (7 stage II, 3 stage III) were selected from each of 1231 facilities accredited by the CoC
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in 2006-2007 (N = 11 478). We designed the sampling strategy to achieve a similar ratio of stage
II to III patients in our sample as that observed nationally (observed ratio in 2006-2007: 73.9%
stage II, 26.1% stage III). Of facilities, 99% (N = 1217) participated and 11 360 patient records
were submitted. All but 17 facilities diagnosed and treated at least one stage III patient. We
maximized generalizability by randomly selecting patients from the census of CoC facilities
across the United States.
We excluded any patients who had delayed surgery (> 1 year from diagnosis; n = 17) or
had evidence of recurrence, developed a new primary cancer, died, or were otherwise lost to
follow-up before the end of active treatment (approximated by 10 months from diagnosis; n =
490). This left a sample size of 10 853 patients, whose medical records were abstracted by
trained registrars at each facility to assess surveillance imaging and recurrence.
Comparators
The primary explanatory variable was mode of distant recurrence detection, categorized
as (1) asymptomatic imaging either for cancer follow-up (routine imaging) or as an incidental
finding on unrelated imaging for other reason, vs (2) patient-detected sign/symptom that
prompted nonroutine doctor visit, physician-detected sign/symptom during routine visit, or
detection as part of work-up for a local/regional recurrence or new primary. Registrars were
instructed to code how the first documented distant cancer recurrence was initially detected
and not the diagnostic procedures or imaging that might have followed. Registrars were also
instructed to consult high-yield locations in the medical record (operative reports, pathology
reports, radiology/imaging reports, and notes from clinic and consult visits). We also provided
examples of symptoms with which a patient might present that could lead to a diagnosis of
distant recurrence and showed how surveillance-detected recurrences might be documented in
the medical record. We assessed recurrences within the first 5 years of diagnosis.
We considered systemic imaging scans (CT or MRI of the chest, abdomen, pelvis, and/or
head; bone scan; PET/CT) to be for surveillance when registrars classified them as “surveillance
imaging in the absence of new signs or symptoms (asymptomatic).” Imaging determined not to
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be for the purpose of surveillance included imaging performed as “follow-up for a new
sign/symptom,” “follow-up for a suspicious finding on other imaging, or “imaging performed in
response to a newly detected malignancy.” Registrars went to specific high-yield locations in
medical records inside and outside their facilities for this information.
We summarized use of surveillance systemic imaging as a dichotomous variable (receipt
of surveillance imaging within first 3 years: yes/no). We used this variable as the outcome
variable in a propensity model to account for differences between patients who received and
did not receive surveillance systemic imaging during follow-up.
Models controlled for appropriate factors, such as sociodemographics, age, coexisting
conditions, and treatment factors. Other factors included race, Hispanic ethnicity, zip code,
level of education, household income, insurance status, and rural/urban residence. Each
patient’s Charlson/Deyo clinical comorbidity score,31,32 categorized as 0 or 1+, was also
available. First-course treatment factors included surgery type, receipt of radiation, and
systemic therapies recorded as 3 separate yes/no dichotomous variables (chemotherapy,
hormone, and HER2 targeted therapy). Facility type was also included (community cancer
program/other, comprehensive community cancer program, academic/research program).
We stratified the analysis by type of breast cancer. We combined ER, PR, and HER2
status to create 3 tumor biology groups as described in Table 2: ER/PR+ HER2–; triple negative;
and HER2+.25 We further stratified the tumor biology type by stage II and stage III.
Study Outcomes
The primary outcome variable was the number of days from the initial breast cancer
diagnosis to death (as opposed to the time of recurrence to death), to reduce the potential for
lead-time bias.
Time Frame for the Study
Women diagnosed with stage II or III breast cancer who received an operation at a CoC-
accredited institution in 2006-2007 were included. We chose these 2 years to allow 5 years of
21
follow-up through the end of 2013, the most recent data available at the beginning of the
analysis.
Data Collection and Sources
This analysis used existing data from the NCDB as described previously. Additional data
elements, including indications for and results of systemic imaging as well as recurrences and
how they were first detected, were abstracted by trained registrars at each facility as part of a
CoC Special Study initiative to determine current surveillance practice and recurrence outcomes
for colorectal, breast, and lung cancers. Trained registrars are on staff at each facility to enter
the required data elements for the NCDB. For our study, these same registrars reviewed patient
records from their own and any other institutions or practice settings where a patient received
treatment or imaging studies, beginning at the end of active treatment and continuing until the
end of the fifth year after diagnosis or death, whichever occurred first.
Registrars recorded complete follow-up care information, including systemic imaging
studies conducted (chest CT, abdomen/pelvis CT/MRI, head CT/MRI, bone scan, PET/CT), the
intent of the scans (asymptomatic surveillance imaging: yes/no), recurrences and how they
were detected (asymptomatic imaging vs patient- or provider-detected signs and/or
symptoms), and death. Registrars used a standardized abstraction manual developed as a part
of this PCORI-funded study, and they entered data via the same secure web platform used for
the standard NCDB data collection.
Cancer registrars abstracted recurrence because recurrence information is unreliable
and missing for 18.2% of patients diagnosed with breast cancer.33 HER2 status was not routinely
collected in 2006-2007, and so we included it as a data element. The registrars confirmed key
fields (eg, date of death) at the time of chart abstraction. Data elements collected during the
study were merged with a patient’s corresponding existing record in the NCDB. The registrars
completely deidentified all data before providing them to the investigators.
22
Pilot Study and Reliability Analysis
To ensure the quality of the data, we performed both a pilot study and a reliability
study. For the pilot study, we piloted full study procedures at 18 CoC-accredited facilities with
180 randomly sampled patients (10 per facility) who met inclusion criteria. To assess feasible
and valid abstraction of the data fields related to recurrence, we ensured that at least one
randomly selected patient from each of the pilot facilities had a documented recurrence.
We conducted a reliability study to ensure that the cancer registrars could abstract
intent of imaging and recurrence from medical records. Specifically, we selected a random 5%
sample of patients who received their diagnosis and/or elements of first-course treatment at
multiple facilities (n = 537). Registrars at the second outside facility were assigned the same
patient to assess reliability of abstraction, particularly intent of scan. This was possible because
registrars at CoC facilities are required to track patients who receive follow-up care at multiple
facilities in order to retain accreditation.27 The primary facility was the one where the patient
received most of her cancer-related care; registrars at these sites had direct access to the most
complete follow-up information through that facility’s medical records. The registrar at the
second facility had to request a greater number of records. Records abstracted at the second
facility were used only for this reliability study.
From the 537 pilot patients, cancer registrars abstracted 1240 scans. Of these scans, 418
were systemic imaging scans. The observed percentage agreement for surveillance vs symptom
or follow-up advanced imaging was 79.4% (expected = 51.0%); the kappa was 0.6 (Z = 11.9; p <
0.001), indicating moderate to high agreement. These reliability estimates likely represent an
underestimate of reliability given that primary facilities by definition had direct access to more
complete medical information.
Analytical and Statistical Approaches
In our preliminary data analyses, we noticed potential bias in who received
asymptomatic imaging and, who therefore, was more likely to have her recurrence detected on
asymptomatic imaging. For example, women with a distant recurrence detected by symptoms
23
as compared with asymptomatic imaging were more likely to have had their index cancer be
lobular, treated with mastectomy, and treated in community cancer centers as compared with
academic facilities. To adjust for this bias, we constructed 2 propensity score weights. First, we
constructed propensity score models based on a patient’s receipt of asymptomatic systemic
imaging in the first 3 years following diagnosis, under the assumption that accounting for
differences between patients who do vs do not receive asymptomatic systemic imaging best
reflects the randomization point that would be used in a clinical trial. “Any imaging” was
modeled because only 12% of patients received 2 or more imaging studies of the same type
over the follow-up period.34 This approach is also consistent with best practices for constructing
propensity score models that include only pretreatment factors.35,36 We fit separate models for
each of the tumor subtype groups. We constructed a second set of propensity score weights
based on how a patient’s distant recurrence was first detected (asymptomatic imaging vs
patient-/provider-detected signs/symptoms) to ensure consistent findings. Propensity score
models included the full set of sociodemographic and tumor/treatment factors described above
and were constructed using the approach outlined by Xu et al (2010) to obtain stabilized
inverse propensity score weights.37 Patients were censored at the time they were lost to follow-
up or died.
The study team then calculated the number and proportion of patients who received
surveillance systemic imaging in the first 3 years from diagnosis. We specifically compared
sociodemographic, diagnosis, and treatment factors between the asymptomatic surveillance
imaging and sign-/symptom-detected recurrence groups, using t tests for continuous variables
and chi-square tests for categorical variables. We estimated this descriptive analysis
unweighted, then weighted by the propensity scores, to ensure comparable groups within each
of the tumor subtype groups. We then assessed the relationship between mode of recurrence
detection and days from initial cancer diagnosis to death, using propensity-weighted
multivariable Cox proportional hazards regression, stratified by tumor subtype group, given the
marked recurrence risk differences observed by these groups.25 Based on this model, we
estimated median survival within 5 years of diagnosis. Patients were censored at the time they
were lost to follow-up or died.
24
Sensitivity analyses assessed the potential for length bias, which is the concern that
faster-growing tumors and aggressive cancers may be detected as interval cancers by
signs/symptoms as opposed to asymptomatic imaging detecting slowly progressing cases. The
presence of length bias would result in an overestimated survival benefit for patients with
surveillance imaging-detected recurrences. As the potential for this bias would be greatest for
the group of patients with the most aggressive disease (triple negative), we re-estimated
models, excluding the following groups from this cohort: (1) patients who recurred with brain
metastases; (2) patients not treated with systemic therapy at the time of distant recurrence;
and (3) patients who recurred within 4 months of the start of surveillance. The study team
conducted analyses using SAS v9 (https://www.sas.com/en_us/software/sas9.html).
Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Posttreatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials
The 2 primary deliverables associated with aim 3 are the following: (1) identification of
priority comparator strategies for prospective randomized trials, and (2) creation of a decision
support tool to facilitate clinician communication with patients regarding risks during follow-up.
Identification of High-Priority Comparators
Over the course of the 3-year study, the research team engaged patients, clinicians, and
the multi-stakeholder group to finalize study methods and to help with interpreting results. In
addition to holding engagement sessions, we conducted an anonymous survey of Alliance
Breast Committee members (N = 27) to assess support for a prospective trial of surveillance
imaging. The survey asked providers about (1) their willingness to enroll patients in a clinical
trial of systemic imaging, and (2) the priority level of a clinical trial assessing the impact of
systemic imaging. In addition, the study team used an audience response system after the
presentation of study findings at the Alliance PAC meeting to assess feasibility. The audience
response system allowed presenters to poll attendees regarding the following topics: (1)
willingness to enroll in a trial and (2) whether the attendee considered the research to be of
high priority.
25
Creation of Decision Support Tool
The study team engaged multiple stakeholder groups (patient, clinicians, multi-
stakeholder group) and achieved consensus that, whereas survivorship care plans exist,
currently no decision support tool helps providers assess a patient’s longer-term risk of
recurrence and death. The single tool identified by the team was a patient management and
follow-up planning flow sheet endorsed by ASCO.38 This generic template, however, cautions
that patient variation is not taken into consideration; moreover, it provides no guidance on how
to tailor follow-up based on recurrence or toxicity risks that patients face given their unique
diagnosis and treatment characteristics.39
Stakeholders were engaged during the 3-year project to reach consensus about the
goals of the decision support tool; its key design requirements and features; and its content,
including inputs (patient, tumor, and treatment characteristics) and outcome measures. These
engagement sessions yielded consensus about the elements for the development of the initial
tool. To obtain this feedback, throughout the 3-year period the study team conducted separate
engagement sessions with the Alliance PAC and the multi-stakeholder group of clinicians and
patients.
The study programming team developed the style and layout template using Bootstrap
(https://getbootstrap.com/), an open-source, front-end framework (library) that offers a cost-
effective approach to designing websites. The framework includes HTML, CSS, and JavaScript
code to help developers build web applications for both desktop and mobile environments. The
back-end, graphic information relies on Highcharts (https://www.highcharts.com/). Free for
academic and nonprofit use, Highcharts is a charting tool that allows for graphical
representation of risk information. We developed a web-based platform to facilitate integration
of the decision support tool into the workflow of oncology specialty care and medical
encounters.
26
Changes to the Original Study Protocol
The study team made 2 minor, but necessary, changes to the analysis plan for 2 reasons:
(1) lower-than-anticipated surveillance imaging rates than had been previously reported in the
literature and (2) robust findings that did not lend themselves to a value of information (VOI)
analysis. First, in the original study protocol, we designed the analytic plan to assess the
relationship between receipt of asymptomatic imaging over time and the detection of
recurrence and survival. However, we found that 30% of women received 1 or more
asymptomatic systemic imaging scans for the purpose of surveillance during follow-up, and only
12% received 2 or more asymptomatic surveillance systemic scans of the same type over
time.40 This low proportion required a shift in the analytic plan, to assess the relationship
between detection of distant recurrences by asymptomatic imaging vs patient-/provider-
detected signs/symptoms and survival, as described in aim 2.
Second, our team originally proposed a VOI analysis in aim 3 as a tool for prioritizing the
need for future prospective comparative effectiveness trials. However, robust study results in
aim 2 identified 2 subgroups of patients who may potentially benefit from surveillance systemic
imaging—namely, patients with triple negative cancers and patients with HER2+ cancers.
Stakeholders, including cancer specialists and patients, broadly endorsed these groups and
comparators, rendering a VOI analysis unnecessary.
27
RESULTS
The results are presented by aim. The Alliance trials used for aim 1 included all
locoregional breast cancers—namely, stages I, II, and III. Because aim 2 examines the use of
systemic imaging to detect distant recurrence, we restricted the analysis to stages II and III.
Stage I patients were excluded because their distant recurrence rate and use of imaging are too
low to analyze with any meaningful power.
Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities
Characteristics for the population of patients with receptor status and recurrence are
documented in Table 4 (n = 10 046). Approximately 55% of the patient population was ER/PR+,
HER2–, the mean age was 54.1 years, and 84.8% of the population was white.
Annual hazards of first recurrence by cancer stage are presented in Figure 2. Both stage
(p < 0.0001) and tumor biology type (p < 0.0001) influenced likelihood of recurrence within 5
years. The timing of recurrence varied by tumor biology type (p < 0.0001). Of recurrences, 75%
occurred by 3.3 years for ER/PR+, HER2–; 1.8 years for triple negative; 5.0 years for triple
positive; and 3.1 years for ER/PR–, HER2+. For stage III patients, triple negative tumors recurred
earlier and more often (5-year probability of recurrence was 43.1%) than did triple positive
tumors (5-year probability of recurrence was 16%), which were distributed over a longer time
frame (Table 5).
28
Table 4. Characteristics of Patients Enrolled in Alliance Trials
Patient Characteristics N = 10 046 %
Age Mean (SD)
54.1 (11.6 yrs)
Race White 8520 84.8%
Black 564 5.6%
Asian/Native Pacific Islander 619 6.2%
Other/unknown 345 3.4%
Tumor size < 2 cm 4658 46.4%
2-5 cm 4504 44.8%
> 5 cm or diffuse/ inflammatory
885 8.8%
Nodal status
Negative 5427 54.1%
1-3 positive nodes 3216 32.1%
≥ 4 positive nodes 1389 13.8%
Receptor type
ER or PR+, HER2– 5572 55.4%
ER and PR–, HER2– 1693 16.8%
ER or PR+, HER2+ 1560 15.5%
ER and PR–, HER2+ 1223 12.2% Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.
Table 6 summarizes the model and personalized risk estimates for measures with
minimal missingness that explained the greatest proportion of variance in recurrence (tumor
biology group, cancer stage, age at diagnosis). We used these models to provide estimates for
the decision support tool developed as a part of specific aim 3.
29
Figure 2. Annual hazards of breast cancer first recurrence by stage at diagnosis
Abbreviations: ER, estrogen receptor; HER2neu, human epidermal growth factor receptor 2; PR, progesterone receptor.
Table 5. Cumulative Probability of Recurrence at 5 Years From Diagnosis
Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.
Receptor Type Group Stage I Stage II Stage III
ER or PR+, HER2– 5.1% 9.1% 28.8%
ER and PR–, HER2– 7.0% 13.4% 43.1%
ER or PR+, HER2+ 0.9% 7.9% 15.9%
ER and PR–, HER2+ 3.1% 10.2% 26.5%
23
Table 6. Relation Between Molecular Subtype, Tumor Size, Nodal Status, and Age on Time to First Recurrence
ER+ or PR+, HER2– ER– and PR–, HER2–
Patient/Tumor Characteristic
Coeff Standard Error
(Coeff)
Hazard Ratio
Z P Value Coeff Standard Error
(Coeff)
Hazard Ratio
Z P Value
Nodal status
Negative nodes REF -- -- -- -- REF -- -- -- --
Positive node 1.12 0.10 3.08 11.32 <0.0001 1.54 0.16 4.67 9.45 <0.0001
Tumor size
0-2 cm REF -- -- -- -- REF -- -- -- --
2-5 cm 0.27 0.10 1.31 2.61 0.009 0.45 0.16 1.58 2.86 0.0043
5+ cm 1.08 0.14 2.94 7.73 <0.0001 1.23 0.25 3.41 4.86 <0.0001
Age group
18-44 REF -- -- -- -- REF -- -- -- --
45-64 –0.46 0.11 0.63 –4.11 <0.0001 –0.42 0.16 0.66 –2.59 0.0097
65+ –0.28 0.13 0.76 –2.06 0.04 –0.62 0.25 0.54 –2.53 0.0115
24
Table 6. Relation Between Molecular Subtype, Tumor Size, Nodal Status, and Age on Time to First Recurrence, continued
ER– and PR–, HER2+ ER+ or PR+, HER2+
Patient/Tumor Characteristic
Coeff Standard Error
(Coeff)
Hazard Ratio
Z P Value Coeff Standard Error
(Coeff)
Hazard Ratio
Z P Value
Nodal status
Negative nodes REF -- -- -- -- REF -- -- -- --
Positive node 1.49 0.30 4.43 4.89 <0.0001
1.11 0.24 3.02 4.69 <0.0001
Tumor size
0-2 cm REF -- -- -- -- REF -- -- -- --
2-5 cm –1.67 0.72 0.19 –2.31 0.0207 –0.90 0.59 0.41 –1.51 0.13
5+ cm 1.14 0.43 3.14 2.68 0.0074 0.65 1.01 1.91 0.64 0.52
Age group
18-44 REF -- -- -- --
45-64 –0.08 0.15 0.92 –0.55 0.5824 –0.05 0.16 0.95 –0.30 0.76
65+ 0.33 0.27 1.40 1.21 0.2247 0.10 0.26 1.11 0.38 0.7 Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.
25
Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body Imaging
Approximately 12% of the 10 076 women in the cohort (n = 1220) with known receptor
status had a distant recurrence detected within the first 5 years of diagnosis. Five-year distant
recurrence was 21.9% for triple negative; 13.9% for HER2+; and 10.1% for ER/PR+, HER2–
patients.
Of the 1220 women with a distant recurrence with known ER, PR, and HER2 status, 78%
were younger than 70 at diagnosis, 45% had government-provided insurance, and 17% had 1 or
more chronic conditions (Table 7). Further, 50% of patients with a distant recurrence were
ER/PR+, HER2neu –; 28% were triple negative; and 22%, HER2+. Most women were treated at
community and comprehensive community cancer program facilities (81%). Women with a
distant recurrence detected by symptoms were more likely than women with a distant
recurrence detected by asymptomatic imaging to have had their index cancer be lobular,
treated with mastectomy, and treated in community cancer program facilities rather than
academic facilities. These differences were no longer statistically significant after propensity
score weighting.
26
Table 7. Characteristics of Stage II to III Breast Cancer Patients With Distant Recurrence by How Recurrence Was Detected (Sign/Symptom vs Asymptomatic Imaging Detected), Overall Population (Unweighted)
Patient Characteristics N Overall (N = 1220)
%
Symptom Detected (n = 936)
%
Asymptomatic Imaging
Detected (n = 284)
%
P Value
Sociodemographic Characteristics
Age 0.82
< 50 401 32.9 33.2 31.7
50-69 552 45.3 44.8 46.8
≥ 70 267 21.9 22.0 21.5
Race 0.08
White 976 80.0 81.0 76.8
Black 191 15.7 14.4 19.7
Other 53 4.3 4.6 3.5
Hispanic ethnicity 0.59
No 1041 85.3 85.9 83.5
Yes 61 5.0 4.8 5.6
Unknown 118 9.7 9.3 10.9
Mean percentage in patient zip code with less than high school degree
0.37
29% or more 237 19.4 18.5 22.5
20%-28.9% 272 22.3 22.7 21.1
14%-19.9% 271 22.2 22.5 21.1
Less than 14% 387 31.7 31.5 32.4
Unknown 53 4.3 4.8 2.8
Median household income in patient zip code 0.47
<$30 000 193 15.8 15.7 16.2
$30 000-$34 999 192 15.7 15.1 18.0
27
Patient Characteristics N Overall (N = 1220)
%
Symptom Detected (n = 936)
%
Asymptomatic Imaging
Detected (n = 284)
%
P Value
$35 000-$45 999 373 30.6 31.1 28.9
$46 000+ 409 33.5 33.3 34.2
Unknown 53 4.3 4.8 2.8
Insurance status 0.41
Private insurance/ managed care
624 54.0 54.4 52.8
Medicaid 137 11.9 12.5 9.9
Medicare and other government
379 32.8 32.0 35.5
Uninsured/self-pay/ insurance status unknown
15 1.3 1.1 1.8
Urban/rural 0.68
Rural 34 2.9 2.8 3.3
Urban 1140 97.1 97.2 96.7
Clinical Characteristics
Charlson/Deyo score (SD) 0.86
0 1014 83.1 83.0 83.5
1+ 206 16.9 17.0 16.6
Tumor Characteristics
Tumor size 0.46
< 2 cm 206 16.9 16.1 19.4
2-5 cm 715 58.6 58.7 58.5
≥ 5 cm 272 22.3 23.1 19.7
Missing 27 2.2 2.1 2.5
Nodal status 0.40
Negative 258 21.2 21.2 21.1
28
Patient Characteristics N Overall (N = 1220)
%
Symptom Detected (n = 936)
%
Asymptomatic Imaging
Detected (n = 284)
%
P Value
1-3 positive 382 31.3 32.6 27.1
4-9 positive 310 25.4 24.5 28.5
+9 positive 246 20.2 20.0 20.8
Uncertain/unsampled/ unknown
24 2.0 1.8 2.5
Histology 0.06
Ductal 1034 84.8 83.4 89.1
Lobular 109 8.9 9.5 7.0
Other 77 6.3 7.1 3.9
ER, PR, HER2 risk group 0.65
ER or PR+, HER2– 610 50.0 50.1 49.7
ER and PR–, HER2– 338 27.7 27.1 29.6
Chemotherapy 0.47
No 210 17.3 17.8 15.9
Yes 1002 82.7 82.2 84.1
Unknown
HER2 targeted therapy 0.40
No 1074 89.4 88.9 90.7
Yes 128 10.7 11.1 9.3
Endocrine therapy 0.71
No 518 43.4 43.6 42.4
Yes 677 56.7 56.4 57.6
Surgery type and radiation therapy 0.03
Breast-conserving surgery alone
346 28.6 30.0 23.8
29
Patient Characteristics N Overall (N = 1220)
%
Symptom Detected (n = 936)
%
Asymptomatic Imaging
Detected (n = 284)
%
P Value
Breast-conserving surgery + radiation
43 3.6 3.8 2.9
Mastectomy alone 538 44.4 42.2 52.0
Mastectomy + radiation 284 23.5 24.1 21.4
Facility type 0.04
Community cancer program/other
331 27.1 29.1 20.8
Comprehensive community cancer program
655 53.7 52.2 58.5
Academic/research program
232 19.0 18.6 20.4
Other 2 0.2 0.1 0.4 Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.
We compared women whose recurrences were detected by asymptomatic imaging with
women whose recurrences were detected by signs or symptoms. After propensity weighting
based on receipt of surveillance systemic imaging within 3 years of diagnosis, women with triple
negative and HER2+ cancers had a reduced risk of death in 5 years (hazard ratio [HR] = 0.66,
95% CI, 0.48-0.91 and HR = 0.40, 95% CI, 0.24-0.68, respectively) if their recurrence had been
detected by asymptomatic imaging compared with if it had been detected by signs and
symptoms. By contrast, the 5-year mortality rate did not differ significantly based on whether
the distant recurrence was detected by asymptomatic imaging or by symptoms for patients
with ER/PR+ HER2– (HR = 1.2; 95% CI, 0.93-1.60) cancer (Table 8).
25
Table 8. Unweighted and Weighted Association Between Asymptomatic vs Symptom Detected Distant Recurrences and Time to Death by Molecular Subtype Risk Group for Women Diagnosed With Stage II to III Breast Cancera
Propensity Weight Based on Receipt of Surveillance
Systemic Imaging Within 3 Years of Diagnosis b
Propensity Weight Based on How Recurrences Detected (Asymptomatic Imaging vs
Signs/Symptoms)c
Tumor Subtype Risk Group
Unweighted Unweighted, Covariates
Weighted, No Covariates
Weighted, Covariates
Weighted, No Covariates
Weighted, Covariates
N HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI)
ER or PR+, HER2–
610 1.22 1.17 1.21 1.22 1.03 1.14
(0.96-1.55) (0.89-1.53) (0.93-1.56) (0.93-1.60) (0.80-1.33) (0.87-1.48)
ER and PR –, HER2–
338 0.68 0.64 0.74 0.66 0.72 0.64
(0.51-0.89) (0.46-0.89) (0.55-0.98) (0.48-0.91) (0.53-0.97) (0.47-0.88)
HER2+ 272 0.64 0.42 0.63 0.40 0.69 0.56
(0.43-0.94) (0.25-0.69) (0.41-0.98) (0.24-0.68) (0.46-1.04) (0.36-0.87) Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor. aBolded values indicate significant differences in significant hazard ratios (HRs); p < 0.05. bPropensity weight constructed using full cohort of 10 076 patients. Adjusted for propensity to receive surveillance systemic imaging within 3 years of diagnosis. cPropensity weight constructed using cohort of patients with distant recurrence within 5 years of diagnosis (n = 1220). Adjusted for propensity to have recurrence detected by asymptomatic systemic imaging vs by signs/symptoms.
25
These estimates translated to a between-group difference in weighted median survival
of 5 months for triple negative (median of 39 months for patients diagnosed on asymptomatic
imaging as compared with 34 months for patients diagnosed on signs and symptoms) and 13
months for HER2-amplified patients (median of 64 months for patients diagnosed on
asymptomatic imaging as compared with 51 months for patients diagnosed on signs and
symptoms; Table 9).
Table 9. Percentage of Patients Surviving Until Years 3 to 4 and Median Survival for Patients With Triple Negative and HER2+ Stage II to III Breast Cancer, Propensity Weighted Based on Receipt of Surveillance Within 3 Years Of Diagnosis
Tumor Subgroup N Year 3 Percentage (SD)
Year 4 Percentage (SD)
Survival in Months
Median (95% CI)
ER– and PR–, HER2–
Asymptomatic 84 54% ± 6% 36% ± 6% 39.0 (33.1-45.3)
Signs/symptoms 254 45% ± 3% 27% ± 3% 33.7 (30.2-37.7)
HER2+
Asymptomatic 59 85% ± 5% 68% ± 7% 63.6 (49.8-5+ years)a
Signs/symptoms 213 68% ± 4% 53% ± 4% 51.4 (44.2-57.9) Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor. a Greater than 50% of cohort survived beyond 5 years from diagnosis.
Sensitivity Analysis
Sensitivity analyses yielded a consistent pattern of findings for the triple negative group
after we excluded 3 groups of patients: (1) patients with brain metastasis (HR = 0.60; 95% CI,
0.42-0.85); (2) patients not treated with systemic therapy at the time of recurrence (HR = 0.49;
95% CI, 0.30-0.80); and (3) patients who had cancer that recurred within 4 months of the start
of surveillance (HR = 0.69; 95% CI, 0.47-1.02).
26
Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Post-treatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials
Our stakeholders generally agreed that 2 key findings from this study have several
immediate implications for clinical practice. First, more than two-thirds of women diagnosed
with breast cancer have ER/PR+, HER2– tumors. This group, in our study, had no demonstrated
potential benefit from current advanced imaging in the context of surveillance. Second, for the
first time, we identified subgroups of patients for whom surveillance imaging may improve
survival outcomes.
In addition to the findings above, through stakeholder engagement the study team
successfully identified high-priority comparators for a future prospective randomized trial, and
developed an initial decision support tool that provides clinicians with a risk-based tailored
decision support tool.
Identification of High-Priority Comparators
Stakeholders expressed support for a prospective randomized trial to investigate the
use of asymptomatic surveillance systemic imaging in triple negative and HER2+ patients. An
anonymous survey of Alliance Breast Committee members (N = 27) confirmed support for a
prospective trial of surveillance imaging. The following characteristics describe the
respondents: 23% were surgical oncologists, 65% were medical oncologists (currently
responsible for most surveillance for distant recurrence), 4% were radiation oncologists, and 8%
were other specialties (eg, general surgery); 30% practiced in a community setting; 85%
reported that more than one-half of their practices were dedicated to the care of women with
breast cancer; and 81% had been in practice for more than 6 years.
Of respondents, 65% supported enrolling both triple negative and HER2+ patients who
were stage II and III at diagnosis (Figure 3). In addition, a large majority of providers (81%)
indicated that a clinical trial examining the impact of systemic imaging was intermediate to high
priority (data not shown).
27
Figure 3. Alliance Breast Committee support for clinical trial
28
After presenting study findings at the Alliance PAC meeting, audience response system
data confirmed that patients viewed results as meaningful, with further research possibly or
definitely justified (84%). Further, nearly two-thirds (61%) indicated that they would be willing
to enroll in a trial in which they were randomized to either standard of care or standard of care
with the addition of surveillance imaging. These findings, coupled with engagement of our
multi-stakeholder group, indicated strong support for a clinical trial, without the need for a
formal VOI analysis, given the clear inclusion criteria of the logical next trial. A 2016 Cochrane
review assessed the role of advanced imaging on recurrence detection and survival. It
concluded that a targeted imaging trial that takes into consideration advances in biological
knowledge and diagnostic modalities is needed.15
Creation of Decision Support Tool
Through our stakeholder engagement activities, we built consensus about the goal of
the decision support tool as well as its design requirements and special features (Figure 4).
Figure 4. Stakeholder-derived decision support tool design considerations
Goal of Tool • Describe risks patients face after active treatment (toxicities, recurrence, death)
in a way that can be readily communicated. • Inform how often patients should be seen after active treatment (not
prescriptive). Design Requirements
• Target audience: Clinicians involved in follow-up care for women with diagnosis of stage I to III breast cancer
• Presentation: Web-based, including images and text (multiple formats, web, ability to print out)
• English language only • Easy-to-use interface—checkboxes and radio buttons for relevant inputs • Ability to implement in clinical encounter
Special Features • Tailored summary display of key outcome information based on identified
patient and tumor characteristics • Ability to expand to include values clarification (future research direction) • Print-out, paper-based version (brief 1-2 pages)
29
Consensus was also achieved regarding the content for the decision support tool,
including key input factors and outputs as well as future inputs that stakeholders deemed to be
important but are not currently included in available data (Figure 5).
Figure 5. Base inputs and outputs for decision support tool
Abbreviations: BMI, body mass index; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.
30
The study team used data from aim 1 to construct all key input and outcome measures.
We also successfully completed constructing a prototype web-based decision support tool with
a live active link. The initial tool is fully programmed; we developed the front-end interface and
the back-end data structure for all key end points and a graphical representation of outcome
measures. The decision support tool has 3 components:
1. An initial page in which clinicians enter patient characteristics from the time of
diagnosis (Figure 6). We made the selection of inputs after 3 years of engagement with
all stakeholder groups.
2. After selecting the “calculate” button, clinicians are taken to a screen where they can
see a graphical representation of their patient’s personalized risks of first local and first
distant recurrence and risk of death based on their input values (Figure 7).
3. Stakeholders (both clinicians and patients) consistently described the need for a tool in
which end points could be turned off for final patient display, depending on whether
patients wanted to see a given end point (eg, death). We integrated this functionality
into the decision support tool. Unchecking factors in the “prepare for print” section and
navigating to the next screen automatically removes the deselected end point from the
graphs.
31
Figure 6. Initial page of data entry for clinicians in the decision support tool
32
Figure 7. Output page in the decision support tool
33
DISCUSSION
Context for Study Results
Breast cancer is no longer considered a single disease. Biologic markers have been
integrated into the American Joint Committee on Cancer version 8 staging guidelines that were
implemented with diagnoses beginning January 1, 2018, reflecting the critical role tumor
biology plays in recurrence and mortality risk.41 Consistent with prior investigations, our
analyses in aim 1 demonstrated higher recurrence and mortality for patients diagnosed with
triple negative and ER/PR–, HER2+ cancers25 than for patients diagnosed with ER/PR+, HER2–
cancers. Furthermore, this aim demonstrates that the best outcomes exist for patients with
ER/PR+, HER2+ cancers treated with modern trastuzumab-based therapies, which are just
emerging and should be incorporated into clinical practice. Based on this information, we have
developed a decision support tool that can support individualized decision-making regarding
follow-up visits.
Prior research has not assessed the impact of modern-era surveillance imaging on
survival in these subgroups. This gap is attributable mainly to advances in surveillance imaging
and treatment strategies that have occurred in the time since definitive studies that inform the
current guidelines were conducted.10-14 Aim 2 represents the first national study to
demonstrate a survival advantage with asymptomatic detection of distant metastases for
patients, with the benefit limited to triple negative and HER2+ disease.
Cancer survivors, patient advocates, and multidisciplinary breast cancer clinicians were
engaged throughout the study, including as members of the core research team. Engaging
these stakeholders separately as well as together as part of a multi-stakeholder advisory group
was critical to the study team gaining a comprehensive understanding of the content and
communication of post-active treatment follow-up care. Bringing patients and provider
stakeholders together was also important for working through key design considerations as
well as interpreting and communicating results.
34
Generalizability of Findings
We sought to maximize generalizability through the use of 2 unique data sources: data
from past trials conducted in the Alliance and the NCDB. A significant strength of using
randomized trials is standardized data collection and data elements, including the collection of
detailed diagnosis, treatment, and recurrence information. Nevertheless, 2 limitations of
randomized trials must be acknowledged: (1) Clinical trial settings may not reflect real-world
conditions, thus limiting generalizability; (2) as with all secondary analyses, we are limited to
existing data elements. By combining multiple trials to study a question unrelated to the
primary study design of the trials, we mitigate the first limitation. In addition, we addressed
these limitations by including real-world registry data from the NCDB, thus mitigating the
second limitation. The NCDB data have been augmented by registrars to include the collection
of cancer follow-up care. The NCDB captures 70% of the cancers diagnosed in the United States
annually and contains data on more than 30 million cancer patients in the United States; thus, it
optimizes external validity and generalizability.
Implementation of Study Results
We have developed a decision support tool that will be made available on multiple
websites, including those of the Alliance and the University of Wisconsin Department of
Surgery. Placing the tool on multiple websites will facilitate the use of aim 1 findings to inform
individual clinician–patient decision-making.
In aim 2, we found no evidence that asymptomatic detection of distant recurrence
confers a survival advantage for women with ER/PR+, HER2– disease, a patient population that
comprises more than two-thirds of women diagnosed with breast cancer at a locoregional
(nonmetastatic) stage.25 This finding is consistent with overall results reported in the older
randomized trials on which current guidelines are based. This body of evidence suggests that
most women diagnosed with stage II or III breast cancer would not benefit from surveillance
systemic imaging during routine follow-up. We conclude, therefore, that no further study is
warranted and practice should remain as it is.10-14
35
We did, however, find a survival advantage for patients with stage II and III cancers that
are triple negative or HER2+. On their own, these findings, based on observational data, are
insufficient evidence to change clinical practice. Nevertheless, we identified a patient subgroup
for which a randomized trial is warranted. This conclusion is consistent with an explicit
recommendation made by the authors of the 2016 Cochrane review on the topic.15 In the
meantime, we continue to support shared decision-making and the cautious application of
existing evidence regarding limitations of surveillance imaging for breast cancer.
Subpopulation Considerations
Heterogeneity of patient risk forms the basis of our underlying theory; it is the main
premise for the study. Our team’s primary goal was to inform the design of a new evidence-
based, tailored, and patient-centered approach to surveillance, and to identify the highest-
value prospective trials to support it. We have identified significant variation in risk of
recurrence and death according to tumor biology group and stage. Furthermore, we have
shown that tumor biology groups that are associated with higher risks of recurrence and death
at 3 and 5 years may benefit from the use of asymptomatic surveillance systemic scans.25
Study Limitations
Several limitations should be considered. First, some analyses were restricted by the
data elements that were collected as part of previous clinical trial designs (aim 1) or as part of
the cancer registry, or that were abstractable on medical record review (aim 2). For example,
our pilot study demonstrated that family history, including genetic predispositions, could not be
reliably abstracted from medical records; such history information is not available
systematically across legacy clinical trials, despite the known importance of such risk factors. In
addition, our aim 2 analysis depended on the ability of registrars to reliably abstract how
distant recurrences were first detected. Our team provided detailed abstraction instructions
that were piloted extensively by trained cancer registrars at each site. To ensure consistency,
for all patients with distant recurrence, our team compared how distant recurrences were
detected with the indications for and results from the systemic imaging that preceded the
36
recurrence diagnosis. Second, to allow for sufficient follow-up, the data used in each aim were
by necessity at least 10 years old, which may not reflect the most modern therapies. Third,
patients enrolled in clinical trials may not reflect the general population in terms of race and
competing risks, limiting the generalizability of aim 1. However, the data set created as a part of
aim 1 is the only large data set of its kind with systematic collection of diagnosis factors and
detailed treatment information as well as recurrence. As cancer registries continue to explore
integrating the collection of recurrence, it will be important to use these data to confirm study
results. In aim 2, because of the need to use deidentified data based on NCDB agreements with
participating hospitals, we were not able to validate directly the abstraction of scan intent or
recurrence, although the availability of patient records abstracted from 2 facilities by different
registrars afforded a unique opportunity to assess reliability.
Two final limitations have implications for future clinical trial design, as described in aim
3. First, in aim 2, we used 2006-2007 diagnoses to allow for the collection of 5-year follow-up
information. HER2 status was not recorded consistently. Trastuzumab, the targeted therapy
given to patients who have HER2+ tumors, was not routinely administered during these years; it
was missing for 7% of patients despite reabstraction of this information. Only 39% of HER2+
patients who were sampled in 2006-2007 received HER2 targeted therapy at the time of
diagnosis. The magnitude of the survival advantage observed for this subgroup is, therefore,
likely overestimated, which emphasizes the need for a randomized controlled trial but also
underscores that this should be accounted for in power calculations. Second, observational
studies do not allow for the complete control of differences between asymptomatic and
symptom-detected recurrences, and they cannot consider biological differences between these
recurrences that may influence survival; however, our findings were robust in sensitivity
analyses, including models restricted by site of recurrence and to patients who received
treatment for recurrence of cancer.
Future Research
Our research provides further evidence that patient risk of recurrence and mortality
varies by receptor status. This finding suggests current “one-size-fits-all” follow-up guidelines
37
can be better tailored to the risks that patients face following active treatment. We designed
our initial decision support tool for integration into clinical workflow; however, future
refinement of the tool is planned to include psychosocial patient characteristics, such as risk
aversion and state-trait anxiety, as well as other important patient factors, such as genetic
predisposition and family history. Future clinical trials should integrate this information into
standard forms to aid investigation into how these factors influence the timing and magnitude
of recurrence risk. Future implementation of the decision support tool that we developed will
necessarily require usability testing, clinician and patient interviews, and a formal assessment
of key dissemination and implementation considerations (acceptability, appropriateness,
feasibility in a clinical setting, clinician update, barriers and facilitators to large-scale
implementation).
A prospective randomized controlled trial is currently being developed by the Alliance
for Clinical Trials in Oncology Breast Committee to investigate the effectiveness of
asymptomatic surveillance imaging in triple negative and HER2+ patients.
38
CONCLUSIONS
This investigation supports the importance of considering not only tumor size and nodal
status but also receptor status in staging breast cancer, as these factors influence the likelihood
of recurrence within 5 years. Given the predictable variation in the likelihood and timing of
recurrence, these data also support the need for a more personalized approach to follow-up
than current “one-size-fits-all” guidelines. We have developed a decision support tool to aid
clinicians in identifying the optimal follow-up approach based on an individual patient’s risk.
Further, this is the first national study to show a potential survival advantage with
asymptomatic detection of distant metastases for a subset of breast cancer patients following
active treatment. The benefit is limited to 2 subgroups of patients. We found no survival
advantage to surveillance imaging for distant metastatic disease in asymptomatic women with
ER/PR+, HER2– tumors, who represent nearly two-thirds of all breast cancer survivors. By
contrast, we detected a survival advantage to systemic imaging for triple negative and HER2+
breast cancer; however, this observational study can only demonstrate correlation, not
causation. Therefore, this finding should justify investment in a large prospective trial to
address this question.
39
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ACKNOWLEDGMENTS
The authors wish to thank cancer registrars and cancer physician liaisons at all
participating Commission on Cancer–accredited facilities and National Cancer Database staff for
their contributions and dedication to this project. We additionally are grateful to all
stakeholders who participated in this project. Other members of the Alliance ACS-CRP Cancer
Care Delivery Research PCORI Breast Cancer Surveillance Working Group include Karla Ballman,
PhD; Elizabeth Berger, MD; Nicole Brys, MPH; Elizabeth Burnside, MD, MPH, MS; Ronald Chen,
MD, MPH; Patrick Gavin, RPh; Bettye Green, RN; Ann Partridge, MD, MPH; Jane Perlmutter,
PhD, MBA; Rinaa Punglia, MD, MPH; Kathryn Ruddy, MD; Deborah Schrag, MD, MPH; and Ying
Zhang, PhD.
44
Copyright© 2020. The Alliance for Clinical Trials in Oncology Foundation. All Rights Reserved.
Disclaimer:
The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.
Acknowledgement:
Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#CE-1304-6543) Further information available at: https://www.pcori.org/research-results/2013/follow-care-strategies-after-treatment-breast-cancer