Characterization of lung metastasis based on radiomics features: issues related to acquisition parameters and to lesion segmentation
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1 Characterization of lung metastasis based on radiomics features: issues related to acquisition parameters and to lesion segmentation Authors: F. Ambrogi 2, P. E. Colombo 1-2, C. De Mattia 1-2, D. Lizio 1-2, M. Pecorilla 1-2, R. Ronza 1, A. Sartore Bianchi ASST GOM Niguarda 2 Università degli Studi di Milano Supervisor: S. Siena 1-2, A. Torresin 1-2, A. Vanzulli 1-2
2 RADIOMICS PROJECT TEAM A multidisciplinar equipe Statistics Medical Physics Oncology ICT Radiology
3 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 conventional and novel medical imaging could capture additional information not currently used, and more specifically, that genomic and proteomics patterns can be expressed in terms of macroscopic image-based features. If proven, we can infer phenotypes or gene protein signatures, possibly containing prognostic information, from the quantitative analysis of medical image data.,,
4 RADIOMICS R.N. Sutton, E.L. Hall, Texture meausures for automatic classification of pulmonary disease, IEEE Trans. On Computers, C-21: , July 1972 Y.P. Chien, K.S.Fu, Recognition of X-Ray picture patterns, IEEE Trans. on Syst. Man. And Cyber., SMC-4: , March E.L. Hall et al., Computer classification of pneumo-coniosis from radiographs of coal workers, IEEE Trans. On Biomed. Engg., BME 22: , Nov. 1975
5 RADIOMICS R.N. Sutton, E.L. Hall, Texture meausures for automatic classification of pulmonary disease, IEEE Trans. On Computers, C-21: , July 1972 Y.P. Chien, K.S.Fu, Recognition of X-Ray picture patterns, IEEE Trans. on Syst. Man. And Cyber., SMC-4: , March 1974 E.L. Hall et al., Computer classification of pneumo-coniosis from radiographs of coal workers, IEEE Trans. On Biomed. Engg., BME 22: , Nov. 1975
6 The workflow of radiomics Lambin, Clinical Oncology 14 (2017)
7 The workflow of radiomics Lambin, Clinical Oncology 14 (2017) Acquisition ROI Feautures Extraction Analysis Predictive Model
8 OUR workflow of radiomics Acquisition ROI Feautures Extraction Analysis Predictive Model IBEX RFs SET + clinical data Multi-Modality Tumor Tracking DICOM node: CT RT-Struct
9 OUR workflow of radiomics Acquisition ROI Feautures Extraction Analysis Predictive Model IBEX RFs SET + clinical data Multi-Modality Tumor Tracking DICOM node: CT RT-Struct Process time consuming! A challenge: we need a large amount of studies, but each case requires a lot of time!
10 OUR workflow of radiomics Acquisition ROI Feautures Extraction Analysis Predictive Model IBEX RFs SET + clinical data Multi-Modality Tumor Tracking DICOM node: CT RT-Struct The optimization is a continuos process!
11 A FIRST APPLICATION: A RETROSPECTIVE STUDY Lung Lesion Lepidic Growth Solid nodule Bronchioalveolar Carcinoma Pancreas Metastasis Pancreas Metastasis Colon Metastasis 80 pz 25% 75% 97 pz 92 pz
12 A FIRST APPLICATION: A RETROSPECTIVE STUDY SCOPE IDENTIFICATION OF RADIOMICS FEAUTURES THAT CAN CHARACTERIZE LUNG METASTASIS OF PANCREATIC AND COLON ORIGIN IN ORDER TO DESCRIMINATE THE PRIMITIVE TUMOR
13 A FIRST APPLICATION: A RETROSPECTIVE STUDY PANCREAS COLON N patients selected by Oncologist N patients excluded by Radiologist 9 4 Gender 39F - 44M 32F - 61M Age 72 [61-74] 63 [54-68] NAIVE After CT ND 2 22
14 A FIRST APPLICATION: A RETROSPECTIVE STUDY Acquisition ROI Feautures Extraction Analysis Predictive Model
15 I PHASE: ACQUISITION
16 I PHASE: ACQUISITION
17 I PHASE: ACQUISITION
18 I PHASE: ACQUISITION CT ACQUISITION SCANNER VOLTAGE CTDI RECONSTRUCTION FILTER SLICE THICKNESS COLLIMATION TIMING (WASH-IN/WASH-OUT) SCAN REGION
19 I PHASE: ACQUISITION CT ACQUISITION SCANNER VOLTAGE CTDI RECONSTRUCTION FILTER SLICE THICKNESS COLLIMATION TIMING (WASH-IN/WASH-OUT) SCAN REGION
20 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 TOTAL BODY ARTERIOSO PROTOCOL 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 totalbody arterioso protocol. We have a spread of the time between the diagnosis and the CT exam under analysis!
21 I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO SMDC PHASE ARTERIOSA PHASE VENOSA PHASE TARDIVA PHASE s s about 3 min
22 I PHASE: ACQUISITION Protocollo Tomografo Studi Fasi kv Totalbody arterioso Siemens Sensation 64 Siemens Somatom Definition 36% 19,5% SMDC Arteriosa Venosa Tardiva Addome SMDC Arteriosa Venosa PROTOCOL : TOTAL BODY ARTERIOSO CTDIvol DLP Collimazione Strato Pitch mgy mgy cm mm mm 120 6, , % % % 8, , , % 9, % 15,1 621, % 8, % 15, % 8,8 343, % 11,2 483, ,2 19,2 28,8-19,2 1,2 3 0, CT CHEST ABD PELVIS WO & W 4606 exams 3731 executed with the totalbody arterioso protocol Tardiva % 9, % 13,8 660,5 28,8 Philips Brilliance 64 10% Addome SMDC Arteriosa 1 Venosa Tardiva % 3, % 4, ,8% 4, ,8% 4, ,58 3
23 I PHASE: ACQUISITION Protocollo Tomografo Studi Fasi kv Totalbody arterioso Siemens Sensation 64 Siemens Somatom Definition 36% 19,5% SMDC Arteriosa Venosa Tardiva Addome SMDC Arteriosa Venosa PROTOCOL : TOTAL BODY ARTERIOSO CTDIvol DLP Collimazione Strato Pitch mgy mgy cm mm mm 120 6, , % % % 8, , , % 9, % 15,1 621, % 8, % 15, % 8,8 343, % 11,2 483, ,2 19,2 28,8-19,2 1,2 3 0, CT CHEST ABD PELVIS WO & W 4606 exams 3731 executed with the totalbody arterioso protocol Tardiva % 9, % 13,8 660,5 28,8 Philips Brilliance 64 10% Addome SMDC Arteriosa 1 Venosa Tardiva % 3, % 4, ,8% 4, ,8% 4, ,58 3
24 I PHASE: ACQUISITION Protocollo Tomografo Studi Fasi kv Totalbody arterioso Siemens Sensation 64 Siemens Somatom Definition 36% 19,5% SMDC Arteriosa Venosa Tardiva Addome SMDC Arteriosa Venosa PROTOCOL : TOTAL BODY ARTERIOSO CTDIvol DLP Collimazione Strato Pitch mgy mgy cm mm mm 120 6, , % % % 8, , , % 9, % 15,1 621, % 8, % 15, % 8,8 343, % 11,2 483, ,2 19,2 28,8-19,2 1,2 3 0, CT CHEST ABD PELVIS WO & W 4606 exams 3731 executed with the totalbody arterioso protocol Tardiva % 9, % 13,8 660,5 28,8 Philips Brilliance 64 10% Addome SMDC Arteriosa 1 Venosa Tardiva % 3, % 4, ,8% 4, ,8% 4, ,58 3
25 I PHASE: ACQUISITION Protocollo Tomografo Studi Fasi kv Totalbody arterioso Siemens Sensation 64 Siemens Somatom Definition 36% 19,5% SMDC Arteriosa Venosa Tardiva Addome SMDC Arteriosa Venosa PROTOCOL : TOTAL BODY ARTERIOSO CTDIvol DLP Collimazione Strato Pitch mgy mgy cm mm mm 120 6, , % % % 8, , , % 9, % 15,1 621, % 8, % 15, % 8,8 343, % 11,2 483, ,2 19,2 28,8-19,2 1,2 3 0, CT CHEST ABD PELVIS WO & W 4606 exams 3731 executed with the totalbody arterioso protocol Tardiva % 9, % 13,8 660,5 28,8 Philips Brilliance 64 10% Addome SMDC Arteriosa 1 Venosa Tardiva % 3, % 4, ,8% 4, ,8% 4, ,58 3
26 I PHASE: ACQUISITION Protocollo Tomografo Studi Fasi kv Totalbody arterioso Siemens Sensation 64 Siemens Somatom Definition 36% 19,5% SMDC Arteriosa Venosa Tardiva Addome SMDC Arteriosa Venosa PROTOCOL : TOTAL BODY ARTERIOSO CTDIvol DLP Collimazione Strato Pitch mgy mgy cm mm mm 120 6, , % % % 8, , , % 9, % 15,1 621, % 8, % 15, % 8,8 343, % 11,2 483, ,2 19,2 28,8-19,2 1,2 3 0, CT CHEST ABD PELVIS WO & W 4606 exams 3731 executed with the totalbody arterioso protocol Tardiva % 9, % 13,8 660,5 28,8 Philips Brilliance 64 10% Addome SMDC Arteriosa 1 Venosa Tardiva % 3, % 4, ,8% 4, ,8% 4, ,58 3
27 I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO Protocollo Tomografo Studi Fasi kv Totalbody arterioso Siemens Sensation 64 Siemens Somatom Definition 33% 15% Addome SMDC Arteriosa Venosa Tardiva Addome SMDC Arteriosa Venosa % % % % CTDIvol DLP Collimazione Pitch Strato mgy mgy cm mm mm 6, ,8 7, , , % 9, , % 8, % 14, % 8, % ,5 19,2 28,8-19,2 1,2 3 0, CT NECK CHST ABD PELVIS MULTIPH WO & W IVCON 2596 exams 1558 executed with the totalbody arterioso protocol Tardiva % 9,2 367, % 14, ,8
28 I PHASE: ACQUISITION RECONSTRUCTION FILTER SIEMENS PHILIPS B 30 f Arteriosa 1 B 70 f Parenchima
29 I PHASE: ACQUISITION PATIENTS SELECTION BASED ON CT PARAMETER PROTOCOL TOTALBODY ARTERIOSO PHASE ARTERIOSA FILTER HIGH RESOLUTION SLICE THICHNESS 3 mm
30 A FIRST APPLICATION: A RETROSPECTIVE STUDY PANCREAS COLON N patients selected by Oncologist N patients excluded by Radiologist 9 4 N patients excluded by Physicist 16 9 N patients analyzed Gender 39F - 44M 32F - 61M Age 72 [61-74] 63 [54-68] NAIVE After CT ND 2 22
31 I PHASE: ACQUISITION PANCREAS COLON PHILIPS BRILLIANCE SIEMENS SENSATION SOMATOM DEFINITION 120 kv 11 7 SOMATOM DEFINITION 100 kv SOMATOM DEFINITION 140 kv - 1
32 I PHASE: ACQUISITION SLICE THICKNESS PARTIAL VOLUME EFFECT
33 I PHASE: ACQUISITION SLICE THICKNESS PARTIAL VOLUME EFFECT Also the slice thickness is important! We have considered only those exams with reconstructed thickness of 3mm!
34 I PHASE: ACQUISITION TWO (TOO) ISSUES! ARTIFACT CAUSED BY PATIENT MOTION
35 I PHASE: ACQUISITION TWO (TOO) ISSUES! HELICAL RECONSTRUCTION & POISSON NOISE
36 I PHASE: ACQUISITION TWO (TOO) ISSUES! HELICAL RECONSTRUCTION & POISSON NOISE
37 I PHASE: ACQUISITION TWO (TOO) ISSUES! HELICAL RECONSTRUCTION & POISSON NOISE
38 A FIRST APPLICATION: A RETROSPECTIVE STUDY Acquisition ROI Feautures Extraction Analysis Predictive Model
39 II PHASE: SEGMENTATION MULTI-MODALITY TUMOR TRACKING APPLICATION Semi-automatic segmentation tool: Region growing starting from a seed point selected by the radiologist on the image
40 II PHASE: SEGMENTATION MULTI-MODALITY TUMOR TRACKING APPLICATION Semi-automatic segmentation tool: Region growing starting from a seed point selected by the radiologist on the image RAPID SEGMENTATION SMART ROI
41 II PHASE: SEGMENTATION SEGMENTATION Partition of the image in two homogeneus regions. N CONTROL POINTS + NON-EUCLIDEAN KERNEL For each point the local metric can be adpated to the image around the point.
42 II PHASE: SEGMENTATION UNIFORMITY 2.5 ADAPTABILITY 200
43 II PHASE: SEGMENTATION UNIFORMITY 2.5 ADAPTABILITY 200 WL -600 WW 1600
44 II PHASE: SEGMENTATION ADAPTABILITY 0 UNIFORMITY 5 ADAPTABILITY 200 UNIFORMITY 5
45 II PHASE: SEGMENTATION ADAPTABILITY 200 UNIFORMITY 3.5 ADAPTABILITY 200 UNIFORMITY 1
46 II PHASE: SEGMENTATION NODULES MORPHOLOGY SOLID NODULE WITH SMOOTH MARGINS
47 II PHASE: SEGMENTATION NODULES MORPHOLOGY SOLID NODULE WITH SPICULATED MARGINS
48 II PHASE: SEGMENTATION NODULES MORPHOLOGY SOLID NODULE EXCAVATED
49 II PHASE: SEGMENTATION NODULES MORPHOLOGY AIR SPACE PATTERN NODULE: nodule with lepidic growth, that seems to correlate more to a pancreatic origin, instead of colon one.
50 II PHASE: SEGMENTATION NODULES MORPHOLOGY The distinction is not always so clear! IS THIS A AIR SPACE PATTERN NODULE?
51 II PHASE: SEGMENTATION NODULES MORPHOLOGY or a partial volume effect?
52 II PHASE: SEGMENTATION NODULES MORPHOLOGY The distinction is not always so clear! CARCINOMATOSIS LYMPHANGITIS CASE: patient excluded!
53 II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES: contouring not so easy! IMPORTANT MANUAL CORRECTION BY THE RADIOLOGIST
54 II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES: segmentation parameters Changing the windowing: WL -150 WL -600 WW 590 WW 1600
55 II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES: segmentation parameters ADAPTABILITY 50 UNIFORMITY 2.5 ADAPTABILITY 200 UNIFORMITY 2.5 ADAPTABILITY 200 UNIFORMITY 5 ADAPTABILITY 200 UNIFORMITY 0.5
56 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 COLON
57 A FIRST APPLICATION: A RETROSPECTIVE STUDY Acquisition ROI Feautures Extraction Analysis Predictive Model UNIVARIATE
58 III PHASE: RADIOMICS FEAUTURES EXTRACTION IMAGING BIOMARKER EXPLORER OPEN INFRASTRUCTURE SOFTWARE PLATFORM Written: Matlab 2011 a c/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
59 III PHASE: RADIOMICS FEAUTURES EXTRACTION IBEX CT NUMBER: Hounsfield Unit +1000
60 IBEX: RFs categories 10 CATEGORY SHAPE INTENSITY HISTOGRAM INTENSITY DIRECT GRADIENT ORIENT HISTOGRAM INTENSITY HISTOGRAM GAUSS FIT GRAY LEVEL COOCCURENCE MATRIX 25 5 RFs FAMILIES GRAY LEVEL COOCCURENCE MATRIX 3 GRAY LEVEL RUN LENGHT MATRIX NEIGHBOUR INTENSITY DIFFERENCE 25 NEIGHBOUR INTENSITY DIFFERENCE 3
61 IBEX: RFs categories INTENSITY HISTOGRAM INTENSITY DIRECT CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D 3D
62 IBEX: RFs categories INTENSITY HISTOGRAM INTENSITY DIRECT CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D 3D
63 IBEX: RFs categories CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D 3D
64 IBEX: RFs categories CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D 3D
65 IBEX: RFs categories CATEGORY: GRAY LEVEL RUN LENGHT MATRIX 25
66 IBEX: RFs categories CATEGORY: GRAY LEVEL RUN LENGHT MATRIX 25
67 Pancreas Colon Skewness Local Std Min Auto Correlation Cluster Shade Sum Average Sum Variance High GL Run Emphasis Low GL Run Emphasis Long Run High GL Emphasis Long Run Low GL Emphasis Short Run High GL Emphasis Short Run Low GL Emphasis RFs: acquisition CT parameter dependence Intensity Histogram Intensity Direct GLCM 2,5D GLRLM Br64 Sens64 Def 120kV Def 100kV Br64 Sens64 Def 120kV Def 100kV Mean -0, , , ,001 RSD 84% 63% 19% 97% 11% 21% 21% 57% 26% 61% 22% 56% Mean -0,49 64, , , , ,0018 RSD 96% 44% 23% 130% 13% 25% 24% 96% 34% 91% 23% 97% Mean -0,53 69, , , , ,0028 RSD 82% 46% 23% 82% 14% 24% 22% 213% 26% 207% 22% 215% Mean -0,35 74, , , , ,0019 RSD 124% 30% 25% 135% 14% 26% 26% 182% 27% 183% 26% 183% Mean 0,05 82, , , , ,003 RSD 898% 52% 36% 556% 20% 38% 37% 246% 37% 452% 37% 184% Mean -0,2 86, , , , ,0017 RSD 265% 57% 48% 293% 23% 52% 43% 148% 109% 138% 41% 151% Mean 0,06 101, ,9 67, , , ,0048 RSD 560% 25% 31% 4364% 17% 33% 31% 136% 32% 136% 31% 136% Mean -0,33 65, , , , ,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
68 A FIRST APPLICATION: A RETROSPECTIVE STUDY Acquisition ROI Feautures Extraction Analysis Predictive Model MULTIVARIATA
69 PRINCIPAL COMPONENT ANALYSIS: preliminary results Prof. F. Ambrogi Campus Cascina Rosa Purpose: FEATURE REDUCTION Linear transformation of the original variables in order to obtain a new cartesian system where the component that explain the main variance is projected on the first 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
70 PRINCIPAL COMPONENT ANALYSIS: preliminary results 11PC: THE HEATMAP
71 PRINCIPAL COMPONENT ANALYSIS: preliminary results 11PC: THE HEATMAP Columns: pricipal components Row: patients
72 PRINCIPAL COMPONENT ANALYSIS: preliminary results 11PC: THE HEATMAP Columns: pricipal components Row: patients
73 PRINCIPAL COMPONENT ANALYSIS: preliminary results 11PC: THE HEATMAP Columns: pricipal components Row: patients The algorithms shifts the rows in order to create a local uniformity. The order of PC explains the clustering criteria.
74 PRINCIPAL COMPONENT ANALYSIS: preliminary results 11PC: THE HEATMAP Columns: pricipal components Row: patients The algorithms shifts the rows in order to create a local uniformity. The order of PC explains the clustering criteria. We have obtained 9 clustering, but we can note two main groups, at the top of the dendogramma.
75 PRINCIPAL COMPONENT ANALYSIS: preliminary results 11PC: THE HEATMAP These two gruops follow the change in colour gradiation of PC1 and PC2.
76 PRINCIPAL COMPONENT ANALYSIS: preliminary results THE CLUSTERING: what about the clusters obtained? Is it a good partition? CLUSTER 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 partition based on the CT acquisition parameters!
77 RFs that allow to descriminate pancreas and colon metastasis: SKEWNESS > nodules: CLUSTER nodule: CLUSTER 5 23 nodules: CLUSTER nodules (135) 19 pancreas (60) 7 colon (75) 10 air space pattern (15): 1 colon - 9 pancreas AUTOCORRELATION < nodules: CLUSTER nodule: CLUSTER 5 24 nodules: CLUSTER nodules (135) 19 pancreas (60) 6 colon (75) 11 air space pattern (15): 1 colon - 10 pancreas
78 CLUSTERING OPTIMIZATION WORK IN PROGRESS 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
79 THANKS FOR YOUR ATTENTION
80 THANKS FOR YOUR ATTENTION THANKS TO NIGUARDA TEAM
81 II PHASE: SEGMENTATION NODULES MORPHOLOGY Can radiomics answer to this question? NO AIR SPACE PATTERN NODULE
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