characterization of lung metastasis based on radiomics ... colloquia 2018/sli… · authors: f....
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![Page 1: Characterization of lung metastasis based on radiomics ... Colloquia 2018/Sli… · Authors: F. Ambrogi 2, P. E. Colombo 1-, C. De Mattia , D. Lizio , M. Pecorilla 1-2, R. Ronza ,](https://reader035.vdocuments.us/reader035/viewer/2022081614/5fca75e067ae1d3498187024/html5/thumbnails/1.jpg)
Authors: F. Ambrogi2, P. E. Colombo1-2, C. De Mattia1-2, D. Lizio1-2, M. Pecorilla1-2, R. Ronza1, A. Sartore Bianchi1-2
1 ASST GOM Niguarda2 Università degli Studi di Milano
Supervisor:S. Siena1-2 , A. Torresin 1-2, A. Vanzulli 1-2
Characterization of lung metastasisbased on radiomics features: issues related to acquisitionparameters and to lesion segmentation
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RADIOMICS PROJECT TEAMA multidisciplinar equipe
MedicalPhysics
Oncology
Radiology
ICT
Statistics
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RADIOMICS
‘’ The high-throughput extraction of large amounts of image features from radiographic images ,,
Lambin, European Journal of Cancer 48 (2012)
‘’ The underlying hypothesis of Radiomics is that advanced image analysis on conventionaland novel medical imaging could capture additional information not currently used, andmore specifically, that genomic and proteomics patterns can be expressed in terms ofmacroscopic image-based features. If proven, we can infer phenotypes or gene–proteinsignatures, possibly containing prognostic information, from the quantitative analysis ofmedical image data. ,,
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RADIOMICS
R.N. Sutton, E.L. Hall, Texture meausures for automatic classification of pulmonarydisease, IEEE Trans. On Computers, C-21: 667-676, July 1972
Y.P. Chien, K.S.Fu, Recognition of X-Ray picture patterns, IEEE Trans. on Syst. Man. AndCyber., SMC-4: 145-156, March 1974.
E.L. Hall et al., Computer classification of pneumo-coniosis from radiographs of coalworkers, IEEE Trans. On Biomed. Engg., BME 22: 518-527, Nov. 1975
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RADIOMICS
R.N. Sutton, E.L. Hall, Texture meausures for automatic classification of pulmonarydisease, IEEE Trans. On Computers, C-21: 667-676, July 1972
Y.P. Chien, K.S.Fu, Recognition of X-Ray picture patterns, IEEE Trans. on Syst. Man. AndCyber., SMC-4: 145-156, March 1974
E.L. Hall et al., Computer classification of pneumo-coniosis from radiographs of coalworkers, IEEE Trans. On Biomed. Engg., BME 22: 518-527, Nov. 1975
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The workflow of radiomicsLambin, Clinical Oncology 14 (2017)
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Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
The workflow of radiomicsLambin, Clinical Oncology 14 (2017)
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Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
OUR workflow of radiomics
Multi-ModalityTumor Tracking
DICOM node:CTRT-Struct
IBEX RFs SET + clinical data
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Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
OUR workflow of radiomics
Multi-ModalityTumor Tracking
DICOM node:CTRT-Struct
IBEX RFs SET + clinical data
Process time consuming!
A challenge: we need a large amount of studies, but each case requires a lot of time!
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Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
OUR workflow of radiomics
Multi-ModalityTumor Tracking
DICOM node:CTRT-Struct
IBEX RFs SET + clinical data
The optimization is a continuos process!
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
Lung Lesion
Lepidic Growth
BronchioalveolarCarcinoma
Pancreas Metastasis
Solid nodule
Pancreas Metastasis
Colon Metastasis 97 pz
80 pz
25%
75%
92 pz
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
SCOPE
IDENTIFICATION OF RADIOMICSFEAUTURES THAT CAN CHARACTERIZELUNG METASTASIS OF PANCREATIC ANDCOLON ORIGIN IN ORDER TODESCRIMINATE THE PRIMITIVE TUMOR
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
PANCREAS COLON
N° patients selected by Oncologist 92 97
N° patients excluded by Radiologist 9 4
Gender 39F - 44M 32F - 61M
Age 72 [61-74] 63 [54-68]
NAIVE 47 24
After CT 34 47
ND 2 22
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
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I PHASE: ACQUISITION
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I PHASE: ACQUISITION
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I PHASE: ACQUISITION
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I PHASE: ACQUISITION
• SCANNER• VOLTAGE• CTDI• RECONSTRUCTION FILTER• SLICE THICKNESS• COLLIMATION• TIMING (WASH-IN/WASH-OUT)
CT ACQUISITION
SCAN REGION
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I PHASE: ACQUISITION
• SCANNER• VOLTAGE• CTDI• RECONSTRUCTION FILTER• SLICE THICKNESS• COLLIMATION• TIMING (WASH-IN/WASH-OUT)
CT ACQUISITION
SCAN REGION
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I PHASE: ACQUISITION
MODALITY: CT
ANATOMIC DISTRICT: THORAX
STUDY COMMON NAMES: - CT CHEST WO- CT CHEST W/WO- CT NECH CHST ABD PELVIS MULTIPH WO & W IVCON- CT CHEST ABD PELVIS WO & W IVCON
To comparison the results, we have considered the same CT procedure for all the patients:For each patient, we have chosen the first CT exam after the metastasis diagnosis, that was a totalbodyarterioso protocol.
We have a spread of the time between the diagnosis and the CT exam under analysis!
TOTAL BODY ARTERIOSO PROTOCOL
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
SMDC PHASE ARTERIOSA PHASE VENOSA PHASE TARDIVA PHASE
20-25 s
70-80 s
about 3 min
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione
PitchStrato
mGy mGy·cm mm mm
Totalbodyarterioso
Siemens
Sensation 6436%
SMDC 3.0 B30f
120 6,8 252 28,8
1,2 3
Arteriosa 3.0 B30f
1208,3 417
19.2
99%Venosa 3.0 B30f
1207,5 323
99%Tardiva
3.0 B30f120
7,5 32599%
Siemens
SomatomDefinition
19,5%
Addome SMDC
3.0 B30f
120 88% 9,9 32319,2
0,9 3
140 22% 15,1 621,5
Arteriosa 3.0 B30f
100 74% 8,5 39819,2
120 22% 15,2 744
Venosa 3.0 B30f
100 67% 8,8 343,528,8-19,2
120 26% 11,2 483,5
Tardiva 3.0 B30f
120 88% 9,2 37728,8
140 12% 13,8 660,5
Philips
Brilliance 6410%
Addome
SMDC120 6 221
40 0,58 3Arteriosa 1
80 70% 3,7 201
100 29% 4,8 254
Venosa100
99,8%4,5 203
Tardiva100
99,8%4,4 186
CT CHEST ABD PELVIS WO & W• 4606 exams• 3731 executed with the
totalbody arterioso protocol
2016
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione
PitchStrato
mGy mGy·cm mm mm
Totalbodyarterioso
Siemens
Sensation 6436%
SMDC 3.0 B30f
120 6,8 252 28,8
1,2 3
Arteriosa 3.0 B30f
1208,3 417
19.2
99%Venosa 3.0 B30f
1207,5 323
99%Tardiva
3.0 B30f120
7,5 32599%
Siemens
SomatomDefinition
19,5%
Addome SMDC
3.0 B30f
120 88% 9,9 32319,2
0,9 3
140 22% 15,1 621,5
Arteriosa 3.0 B30f
100 74% 8,5 39819,2
120 22% 15,2 744
Venosa 3.0 B30f
100 67% 8,8 343,528,8-19,2
120 26% 11,2 483,5
Tardiva 3.0 B30f
120 88% 9,2 37728,8
140 12% 13,8 660,5
Philips
Brilliance 6410%
Addome
SMDC120 6 221
40 0,58 3Arteriosa 1
80 70% 3,7 201
100 29% 4,8 254
Venosa100
99,8%4,5 203
Tardiva100
99,8%4,4 186
CT CHEST ABD PELVIS WO & W• 4606 exams• 3731 executed with the
totalbody arterioso protocol
2016
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione
PitchStrato
mGy mGy·cm mm mm
Totalbodyarterioso
Siemens
Sensation 6436%
SMDC 3.0 B30f
120 6,8 252 28,8
1,2 3
Arteriosa 3.0 B30f
1208,3 417
19.2
99%Venosa 3.0 B30f
1207,5 323
99%Tardiva
3.0 B30f120
7,5 32599%
Siemens
SomatomDefinition
19,5%
Addome SMDC
3.0 B30f
120 88% 9,9 32319,2
0,9 3
140 22% 15,1 621,5
Arteriosa 3.0 B30f
100 74% 8,5 39819,2
120 22% 15,2 744
Venosa 3.0 B30f
100 67% 8,8 343,528,8-19,2
120 26% 11,2 483,5
Tardiva 3.0 B30f
120 88% 9,2 37728,8
140 12% 13,8 660,5
Philips
Brilliance 6410%
Addome
SMDC120 6 221
40 0,58 3Arteriosa 1
80 70% 3,7 201
100 29% 4,8 254
Venosa100
99,8%4,5 203
Tardiva100
99,8%4,4 186
CT CHEST ABD PELVIS WO & W• 4606 exams• 3731 executed with the
totalbody arterioso protocol
2016
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione
PitchStrato
mGy mGy·cm mm mm
Totalbodyarterioso
Siemens
Sensation 6436%
SMDC 3.0 B30f
120 6,8 252 28,8
1,2 3
Arteriosa 3.0 B30f
1208,3 417
19.2
99%Venosa 3.0 B30f
1207,5 323
99%Tardiva
3.0 B30f120
7,5 32599%
Siemens
SomatomDefinition
19,5%
Addome SMDC
3.0 B30f
120 88% 9,9 32319,2
0,9 3
140 22% 15,1 621,5
Arteriosa 3.0 B30f
100 74% 8,5 39819,2
120 22% 15,2 744
Venosa 3.0 B30f
100 67% 8,8 343,528,8-19,2
120 26% 11,2 483,5
Tardiva 3.0 B30f
120 88% 9,2 37728,8
140 12% 13,8 660,5
Philips
Brilliance 6410%
Addome
SMDC120 6 221
40 0,58 3Arteriosa 1
80 70% 3,7 201
100 29% 4,8 254
Venosa100
99,8%4,5 203
Tardiva100
99,8%4,4 186
CT CHEST ABD PELVIS WO & W• 4606 exams• 3731 executed with the
totalbody arterioso protocol
2016
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione
PitchStrato
mGy mGy·cm mm mm
Totalbodyarterioso
Siemens
Sensation 6436%
SMDC 3.0 B30f
120 6,8 252 28,8
1,2 3
Arteriosa 3.0 B30f
1208,3 417
19.2
99%Venosa 3.0 B30f
1207,5 323
99%Tardiva
3.0 B30f120
7,5 32599%
Siemens
SomatomDefinition
19,5%
Addome SMDC
3.0 B30f
120 88% 9,9 32319,2
0,9 3
140 22% 15,1 621,5
Arteriosa 3.0 B30f
100 74% 8,5 39819,2
120 22% 15,2 744
Venosa 3.0 B30f
100 67% 8,8 343,528,8-19,2
120 26% 11,2 483,5
Tardiva 3.0 B30f
120 88% 9,2 37728,8
140 12% 13,8 660,5
Philips
Brilliance 6410%
Addome
SMDC120 6 221
40 0,58 3Arteriosa 1
80 70% 3,7 201
100 29% 4,8 254
Venosa100
99,8%4,5 203
Tardiva100
99,8%4,4 186
CT CHEST ABD PELVIS WO & W• 4606 exams• 3731 executed with the
totalbody arterioso protocol
2016
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I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO
Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione
PitchStrato
mGy mGy·cm mm mm
Totalbodyarterioso
Siemens
Sensation 6433%
Addome SMDC
3.0 B30f
1206,7 233 28,8
1,2 3
99%
Arteriosa 3.0 B30f
1207,9 460
19.2
99%
Venosa 3.0 B30f
1207,5 315
99%
Tardiva 3.0 B30f
1207,5 304
99%
Siemens
Somatom Definition
15%
Addome SMDC
3.0 B30f120 90% 9,7 302 19,2
0,9 3
Arteriosa 3.0 B30f
100 76% 8,2 46819,2
120 21% 14,6 828
Venosa 3.0 B30f
100 67% 8,6 35828,8-19,2
120 26% 12 505,5
Tardiva 3.0 B30f
120 89% 9,2 367,528,8
140 11% 14,3 664
CT NECK CHST ABD PELVIS MULTIPH WO & W IVCON• 2596 exams• 1558 executed with the
totalbody arterioso protocol
2016
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I PHASE: ACQUISITION RECONSTRUCTION FILTER
B 30 f
B 70 f
Arteriosa 1
Parenchima
SIEMENS PHILIPS
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I PHASE: ACQUISITION PATIENTS SELECTION BASED ON CT PARAMETER
PROTOCOL TOTALBODY ARTERIOSO
PHASE ARTERIOSA
FILTER HIGH RESOLUTION
SLICE THICHNESS 3 mm
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
PANCREAS COLON
N° patients selected by Oncologist 92 97
N° patients excluded by Radiologist 9 4
N° patients excluded by Physicist 16 9
N° patients analyzed 67 84
Gender 39F - 44M 32F - 61M
Age 72 [61-74] 63 [54-68]
NAIVE 47 24
After CT 34 47
ND 2 22
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I PHASE: ACQUISITION
PANCREAS COLON
PHILIPS BRILLIANCE 64 19 18
SIEMENS SENSATION 64 25 41
SOMATOM DEFINITION 120 kV 11 7
SOMATOM DEFINITION 100 kV 10 15
SOMATOM DEFINITION 140 kV - 1
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I PHASE: ACQUISITION
PARTIAL VOLUME EFFECT
SLICE THICKNESS
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I PHASE: ACQUISITION SLICE THICKNESS
PARTIAL VOLUME EFFECT
Also the slice thickness isimportant!We have considered only thoseexams with reconstructedthickness of 3mm!
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I PHASE: ACQUISITION TWO (TOO) ISSUES!
ARTIFACT CAUSED BY PATIENT MOTION
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I PHASE: ACQUISITION TWO (TOO) ISSUES!
HELICAL RECONSTRUCTION &
POISSON NOISE
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I PHASE: ACQUISITION TWO (TOO) ISSUES!
HELICAL RECONSTRUCTION &
POISSON NOISE
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I PHASE: ACQUISITION TWO (TOO) ISSUES!
HELICAL RECONSTRUCTION &
POISSON NOISE
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
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II PHASE: SEGMENTATION MULTI-MODALITY TUMORTRACKING APPLICATION
Semi-automatic segmentation tool:Region growing starting from a seed point selected by the radiologist on the image
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II PHASE: SEGMENTATION MULTI-MODALITY TUMORTRACKING APPLICATION
Semi-automatic segmentation tool:Region growing starting from a seed point selected by the radiologist on the image
RAPID SEGMENTATION
SMART ROI
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II PHASE: SEGMENTATION
For each point the local metric can beadpated to the image around the point.
Partition of the image in twohomogeneus
regions.
SEGMENTATION
N CONTROL POINTS + NON-EUCLIDEAN KERNEL
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II PHASE: SEGMENTATION
UNIFORMITY 2.5
ADAPTABILITY 200
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II PHASE: SEGMENTATION
UNIFORMITY 2.5
ADAPTABILITY 200
WL -600
WW 1600
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II PHASE: SEGMENTATION
ADAPTABILITY 0 UNIFORMITY 5
ADAPTABILITY 200 UNIFORMITY 5
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II PHASE: SEGMENTATION
ADAPTABILITY 200 UNIFORMITY 3.5
ADAPTABILITY 200 UNIFORMITY 1
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
SOLID NODULE WITH SMOOTH MARGINS
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
SOLID NODULE WITH SPICULATED MARGINS
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
SOLID NODULE EXCAVATED
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
AIR SPACE PATTERN NODULE:nodule with lepidic growth,that seems to correlate moreto a pancreatic origin, insteadof colon one.
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
The distinction is notalways so clear!
IS THIS A AIR SPACEPATTERN NODULE?
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
…or a partial volume effect?
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II PHASE: SEGMENTATION NODULES MORPHOLOGYThe distinction is not always so clear!
CARCINOMATOSIS LYMPHANGITIS CASE:patient excluded!
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II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES:contouring not so easy!
IMPORTANT MANUAL CORRECTION BY THE RADIOLOGIST
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II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES:segmentation parameters
Changing the windowing:
WL -150 WL -600 WW 590 WW 1600
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II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES:segmentation parameters
ADAPTABILITY 50UNIFORMITY 2.5
ADAPTABILITY 200UNIFORMITY 2.5
ADAPTABILITY 200UNIFORMITY 5
ADAPTABILITY 200UNIFORMITY 0.5
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II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES:segmentation parameters
DIFFERENT PARAMETERS DIFFERENT CONTOURING DIFFERENT RFs VALUES
WE HAVE TRIED TO KEEP THE SAME CONTOURING CRITERIA FOR ALL NODULES:NOT ALWAYS POSSIBLE!
357 NODULES: 171 PANCREAS - 186 COLON
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Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
A FIRST APPLICATION:A RETROSPECTIVE STUDY
UNIVARIATE
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III PHASE: RADIOMICS FEAUTURES EXTRACTION
IMAGING BIOMARKER EXPLORER
OPEN INFRASTRUCTURE SOFTWARE PLATFORM
Written: Matlab 2011 ac/c++
Alpha version: Hunter et al. (Med. Phys. 40, 2013)
1.0 beta version: stand-alone without the requirement of MATLAB license
Reference:Zhang et al., IBEX: an open infrastructure software platform to faciliate collaborative work in radiomics, Med. Phys. 42 (3), March 2015
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III PHASE: RADIOMICS FEAUTURES EXTRACTION
IBEX CT NUMBER: Hounsfield Unit +1000
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IBEX: RFs categories 10 CATEGORY
SHAPE
INTENSITY HISTOGRAM
INTENSITY DIRECT
GRADIENT ORIENT HISTOGRAM
INTENSITY HISTOGRAM GAUSS FIT
GRAY LEVEL COOCCURENCE MATRIX 25
GRAY LEVEL COOCCURENCE MATRIX 3
GRAY LEVEL RUN LENGHT MATRIX
NEIGHBOUR INTENSITY DIFFERENCE 25
NEIGHBOUR INTENSITY DIFFERENCE 3
5 RFs FAMILIES
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IBEX: RFs categoriesINTENSITY HISTOGRAM INTENSITY DIRECT
CATEGORY: GRAY LEVEL COOCCURENCE MATRIX
2.5D3D
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IBEX: RFs categoriesINTENSITY HISTOGRAM INTENSITY DIRECT
CATEGORY: GRAY LEVEL COOCCURENCE MATRIX
2.5D3D
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IBEX: RFs categories CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D3D
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IBEX: RFs categories CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D3D
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CATEGORY: GRAY LEVEL RUN LENGHT MATRIX 25IBEX: RFs categories
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CATEGORY: GRAY LEVEL RUN LENGHT MATRIX 25IBEX: RFs categories
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RFs: acquisition CT parameter dependenceIntensity
HistogramIntensity
DirectGLCM 2,5D GLRLM
Skew
ne
ss
Loca
l Std
Min
Au
to
Co
rre
lati
on
Clu
ste
r Sh
ade
Sum
Ave
rage
Sum
V
aria
nce
Hig
h G
L R
un
Emp
has
is
Low
GL
Ru
nEm
ph
asis
Lon
g R
un
H
igh
GL
Emp
has
is
Lon
g R
un
Lo
w G
L Em
ph
asis
Sho
rt R
un
H
igh
GL
Emp
has
is
Sho
rt R
un
Lo
w G
L Em
ph
asis
Co
lon
Br64Mean -0,646 36 1924 -3451 86 6825 1829 0,001 2645 0,001 1674 0,001
RSD 84% 63% 19% 97% 11% 21% 21% 57% 26% 61% 22% 56%
Sens64Mean -0,49 64,3 1726 -3184 80,7 6076 1672 0,0018 2001 0,002 1599 0,0018
RSD 96% 44% 23% 130% 13% 25% 24% 96% 34% 91% 23% 97%
Def120kV
Mean -0,53 69,6 1740 -3239 81,3 6139 1705 0,0028 2027 0,003 1632 0,0028
RSD 82% 46% 23% 82% 14% 24% 22% 213% 26% 207% 22% 215%
Def 100kV
Mean -0,35 74,3 1634 -2425 78,5 5740 1587 0,0019 1878 0,0021 1520 0,0019
RSD 124% 30% 25% 135% 14% 26% 26% 182% 27% 183% 26% 183%
Pan
cre
as
Br64Mean 0,05 82,3 1295 -637 68,6 4505 1233 0,0039 1493 0,0108 1177 0,003
RSD 898% 52% 36% 556% 20% 38% 37% 246% 37% 452% 37% 184%
Sens64Mean -0,2 86,6 1685 -1240 78,8 5984 1601 0,0018 2145 0,0019 1529 0,0017
RSD 265% 57% 48% 293% 23% 52% 43% 148% 109% 138% 41% 151%
Def 120kV
Mean 0,06 101,8 1237 -71,9 67,6 4298 1210 0,0049 1364 0,0053 1173 0,0048
RSD 560% 25% 31% 4364% 17% 33% 31% 136% 32% 136% 31% 136%
Def 100kV
Mean -0,33 65,3 1691 -3759 79,5 5935 1621 0,0014 1967 0,0015 1542 0,0013
RSD 113% 42% 28% 107% 0,2 29% 29% 82% 31% 82% 29% 82%
ANOVA 0,04 1,E-13 0,002 0,65 0,004 0,002 0,02 0,05 7,E-11 0,09 0,27 0,04
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A FIRST APPLICATION:A RETROSPECTIVE STUDY
Acquisition ROIFeauturesExtraction
AnalysisPredictive
Model
MULTIVARIATA
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
Purpose: FEATURE REDUCTION
Linear transformation of the original variables in order to obtain a new cartesiansystem where the component that explain the main variance is projected on thefirst axes, the second component on the second axes, etc.
DATA:• Only a nodule for each patient• V> 50 voxels• No RF of Shape and Gauss Fit Histogram• Missing vriables→ 5 patients excluded: 135 patients (PANCREAS 60 – COLON 75)• 865 variables for each patient
Prof. F. AmbrogiCampus Cascina Rosa
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
11PC: THE HEATMAP
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
11PC: THE HEATMAP
Columns: pricipal componentsRow: patients
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
11PC: THE HEATMAP
Columns: pricipal componentsRow: patients
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
11PC: THE HEATMAP
Columns: pricipal componentsRow: patients
The algorithms shifts the rows inorder to create a local uniformity.The order of PC explains theclustering criteria.
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
11PC: THE HEATMAP
Columns: pricipal componentsRow: patients
The algorithms shifts the rows inorder to create a local uniformity.The order of PC explains theclustering criteria.
We have obtained 9 clustering, butwe can note two main groups, at thetop of the dendogramma.
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
11PC: THE HEATMAP
These two gruops follow the changein colour gradiation of PC1 and PC2.
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PRINCIPAL COMPONENT ANALYSIS:preliminary results
THE CLUSTERING: what about the clusters obtained? Is it a good partition?
CLUSTER 1-4 1-5 5-9 6-9
PANCREAS 27% 50% 73% 50%
AIR SPACE PATTERN 13% 27% 87% 73%
COLON 51% 72% 49% 28%
None of the clusters seems to match with the partitionbased on the CT acquisition parameters!
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RFs that allow to descriminate pancreas and colon metastasis:
26 nodules (135)19 pancreas (60)7 colon (75)10 air space pattern (15): 1 colon - 9 pancreas
25 nodules (135)19 pancreas (60)6 colon (75)11 air space pattern (15): 1 colon - 10 pancreas
AUTOCORRELATION < 1200
SKEWNESS > 0.04
0 nodules: CLUSTER 3-41 nodule: CLUSTER 5
24 nodules: CLUSTER 6-9
2 nodules: CLUSTER 3-41 nodule: CLUSTER 523 nodules: CLUSTER 6-9
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WORK IN PROGRESS
• CLUSTERING OPTIMIZATION
• ANALYSIS OF THE LUNG PRIMITIVE TUMOR TO COMPARISON WITH METASTASIS ORIGINATED BY PANCREAS AND COLON
• OPTIMIZATION OF RADIOMICS WORKFLOW
• OPTIMIZATION OF PATIENTS RESEARCH
• PREDICTIVE MODEL DEFINITION
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THANKS FOR YOUR ATTENTION
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THANKS FOR YOUR ATTENTION
THANKS TO NIGUARDA TEAM
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II PHASE: SEGMENTATION NODULES MORPHOLOGY
Can radiomics answer to this question?NO AIR SPACE PATTERN NODULE