Materials/Methods:: We examined planning data for prostate patients (491 treatment plans), and planning and outcomes datafor lung cancer patients (219 patients), for endpoints of acute esophagitis and pneumonitis. We considered the generalizedequivalent uniform dose (gEUD) as well as recently introduced functions which represent the mean of a fraction of the dosevolume histogram: mDU(v) (mean dose to the upper (hottest) v fractional volume), and mDL (mean dose to the lower (coldest)v fractional volume). Metrics were extracted using the Computational Environment for Radiotherapy Research software andwere exported for analysis. Spearman’s rank correlation coefficient (Rs) was used to evaluate correlations. Parameters werechosen to maximize correlations with commonly used dose-volume constraints.

Results: Table 1 shows correlations vs. commonly used dose-volume metrics. For gEUD, the highest correlation was recordedfor normal lung, whereas lower correlations was observed at higher dose levels (esophagus and rectum). Parameter-tuned gEUDand mDU values show high correlations with many dose-volume metrics. Figure 1 summarizes correlations with outcomesusing mDU or mDL. In case of tumor control for isolated primary tumors (57 patients), a mid-volume dose model is preferable(v in mDL around 0.4) with Rs around 0.25 (p�0.03).

Conclusions: We have presented and analyzed mathematically efficient metrics for use in outcome-driven optimization ofIMRT treatment plans. The metrics show high correlation with their dose-volume metric counterparts. gEUD, and especiallymDU and mDL are efficient objective functions which may facilitate outcomes driven IMRT treatment planning.

This research was supported by TomoTherapy, Inc., and NIH R01 grant CA85181.

Author Disclosure: I. El Naqa, None; V.H. Clark, None; Y. Chen, None; M. Vicic, None; D. Khullar, None; S. Shimpi, None;A. Hope, None; J. Bradley, None; J.O. Deasy, None.

2848 Predictive Modeling of Lung Tumor Response to Radiation Therapy

R. M. Seibert1, C. R. Ramsey1, D. D. Scaperoth1, J. W. Hines2

1Thompson Cancer Survival Center, Knoxville, TN, 2The University of Tennessee, Knoxville, Knoxville, TN

Purpose/Objective(s): Volumetric imaging data acquired during CT-based Image Guided Radiation Therapy (IGRT) can beused to measure tumor response. Predictive Adaptive Radiation Therapy is a novel treatment technique that utilizes volumetricIGRT data to actively predict the tumor response to therapy and estimate clinical outcomes during the course of treatment. Thegoal of this study was to develop a model for predicting tumor response during treatment using serial Megavoltage CT (MVCT)imaging.

Materials/Methods: MVCT images were acquired prior to the delivery of 660 lung treatment fractions for 20 non-small celllung cancer patients. A Predictive Adaptive Radiation Therapy response model was developed using locally weightedpolynomial regression (LWPR), which is a non-parametric, memory-based technique that predicts future tumor response byprocessing a memory matrix of retrospective tumor response data. When new tumor response data is entered during treatment,the algorithm locates similar tumor responses from the memory matrix and performs a weighted polynomial regression withnearby observations. Retrospective data from 20 patients was used to build the initial memory matrix of tumor responses verseselapsed days of treatment. A genetic algorithm (GA) was used to calculate the optimal days to measure the tumor volume fromthe IGRT images. In addition, the GA was used optimize kernel bandwidths.

Results: The LWPR predictive algorithm was used to predict the tumor response and the confidence intervals for 20 patients.The GA found that predictive accuracy was the greatest for tumor response data acquired on days 0, 3, 7, 11, 12, 15, and 18during treatment. Figure 1 shows the MVCT images and tumor response model results for a typical patient. The predictivemodel was extremely accurate in predictions made near the end of a patient’s treatment. The average error for the predictions

Correlations between gEUD or mDU values and dose-volume metrics (p-values were negligible)

Structure Dose-volume Endpt. Optimal a in gEUD Rs Optimal v in mDU Rs

Lung V10 0.4 0.97 0.76 0.92Lung V20 0.8 0.96 0.95 0.96Esoph. A55 3.2 0.91 0.52 0.91Rectum V40 1.2 0.95 0.84 0.95Rectum V65 6.0 0.83 0.41 0.86Bladder V40 1.0 0.97 0.84 0.95Bladder V65 6.2 0.88 0.4 0.88Lung PTV D95 7.8 0.93 0.89 (mDL) 0.97Prostate PTV D95 29.0 0.99 0.87 (mDL) 1.00

S688 I. J. Radiation Oncology ● Biology ● Physics Volume 66, Number 3, Supplement, 2006

of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest modeluncertainty occured during the middle of treatment where the tumor response relationships are more complex and the predictorsare more varied.

Conclusions: A Predictive Adaptive Radiation Therapy model was developed that accurately predicted final tumor volumes for20 lung cancer patients. These predictions were made using imaging data from 8 fractions early in the course of treatment. Thisallowed the final tumor volume to be predicted near the beginning of treatment without having to go through the tedious processof acquiring the MVCT and contouring the tumor many times. Since the predictions are accurate with quantified uncertainty,they could eventually be used to optimize treatment.

Author Disclosure: R.M. Seibert, None; C.R. Ramsey, TomoTherapy, Inc., D. Speakers Bureau/Honoraria; TomoTherapy, Inc.,F. Consultant/Advisory Board; D.D. Scaperoth, None; J.W. Hines, None.

2849 Evaluation of Simulator Cone Beam CT Images for Radiotherapy Treatment Planning

G. V. Menon1, P. Dunscombe1,2, M. Tambasco1,2

1Tom Baker Cancer Center, Calgary, AB, Canada, 2University of Calgary, Calgary, AB, Canada

Purpose/Objective(s): Over the last decade, kilovoltage cone beam computed tomography (CBCT) has developed into apotentially significant technology for radiotherapy treatment simulation and verification. The CBCT capability on a conven-tional simulator is of added advantage if it can supplement the image acquisition task of a CT simulator (e.g., when the CT boreis too small for the patient or treatment setup). However, due to less accurate density information derived from CBCT, therehas been reluctance by clinicians to use it for 3D treatment planning. In this work, we evaluate the utility of the CBCT functionon a commercial simulator (Acuity IX) for 3D treatment planning by comparing plans based on volumetric and density dataacquired with CBCT scans with those based on CT simulator (Picker PQ5000) scans.

Materials/Methods: To assess the suitability of the CBCT simulator for such a study, we initially evaluated its imagingperformance by comparing it with a CT simulator.

Results: Tests of density resolution, temporal stability, contrast resolution, scan uniformity, and noise, showed results that werecomparable for the CBCT and the CT simulator, suggesting that the former could be used for radiotherapy treatment planningpurposes. Owing to differences in the image reconstruction processes, CT numbers generated by both imaging modalities differ.

Conclusions: Hence, we determined separate calibration curves, relating CT number to electron density for a range typicallyobserved in treatment planning, using a Catphan 600 phantom. We performed scans of a Rando anthropomorphic phantom forthree sites: head, thorax, and pelvis. For CBCT, a full fan mode was used for head scans (diameter � 15 cm) and a half fanmode for diameters � 15 cm (thorax and pelvis). Virtual target volumes of common disease areas were contoured accordingto the ICRU 62 criteria on the CBCT scan. These contours were transferred to the CT images (registered with CBCT) in orderto have the same target volumes. The scans and contours were used to generate treatment plans using typical beam arrangementsand dose prescriptions. A current constraint in using this CBCT device is the limitation in the length of the volume scan being17 cm (a pending software upgrade will reduce this limitation by allowing contiguous scans to be merged).Figure 1 displays the isodose distribution for a prostate plan generated using (a) CBCT and (b) CT data. The figure also givesan idea of the image quality obtained in these scans. For the three sites investigated, the percentage difference between the meantarget dose coverage obtained by the two modalities was � 0.5%. This small difference in dose demonstrates that CBCT datafrom an Acuity simulator is a viable alternative to CT simulation data for rendering 3D treatment plans.

Author Disclosure: G.V. Menon, None; P. Dunscombe, None; M. Tambasco, None.

2850 Skin Toxicity in IMRT of Head and Neck Cancer Patients

A. Sethi, J. Dombrowski, R. Hong, M. Gao, G. Glasgow, B. Emami

Loyola University Medical Center, Maywood, IL

Purpose/Objective(s): Acute skin reactions have been observed in some head and neck (HN) cancer patients treated with IMRTin our department. The severity of skin reaction is sometimes unacceptable and may lead to treatment interruption and/or plan

S689Proceedings of the 48th Annual ASTRO Meeting