MRI-Based Biomarkers of Therapeutic Response in Triple-Negative Breast Cancer
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1 MRI-Based Biomarkers of Therapeutic Response in Triple-Negative Breast Cancer Daniel Golden Postdoctoral Scholar (Radiology) Stanford University Daniel Rubin Laboratory NCI Cancer Imaging Fellowship Seminar December 12, 2012 SCIT Daniel Golden MRI-Based Biomarkers Dec 12, / 19
2 Space Physics Neutral Atmosphere Daniel Golden MRI-Based Biomarkers Dec 12, / 19
3 Space Physics Advantage: Free Magnet! Neutral Atmosphere Daniel Golden MRI-Based Biomarkers Dec 12, / 19
4 Space Physics Advantage: Free Magnet! Disadvantage: Only 10-4 T Neutral Atmosphere Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
5 Space Physics Advantage: Free Magnet! Disadvantage: Only 10-4 T Neutral Atmosphere Radiation Belts Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
6 Space Physics Advantage: Free Magnet! Disadvantage: Only 10-4 T Neutral Atmosphere Radiation Belts Satellites and astronauts Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
7 Motivation Triple-Negative Breast Cancer 15% of all breast cancers; 30,000 annual diagnoses; 8000 deaths Lacks estrogen, progesterone, HER2 receptors Response to chemo is mixed Daniel Golden MRI-Based Biomarkers Dec 12, / 19
8 Motivation Triple-Negative Breast Cancer 15% of all breast cancers; 30,000 annual diagnoses; 8000 deaths Lacks estrogen, progesterone, HER2 receptors Response to chemo is mixed Critical Need A way to predict in advance whether patients will respond: Precision Medicine Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
9 Motivation Triple-Negative Breast Cancer 15% of all breast cancers; 30,000 annual diagnoses; 8000 deaths Lacks estrogen, progesterone, HER2 receptors Response to chemo is mixed Critical Need A way to predict in advance whether patients will respond: Precision Medicine Known Malignancy Selection of Optimal Treatment??? Treatment A Treatment B Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
10 Motivation Dynamic Contrast-Enhanced MRI Imaging (DCE-MRI) Acquires multiple images before and after contrast injection Whole tumor, minimally-invasive (unlike biopsy) Reveals tumor kinetic phenotype: morphology and texture Hypothesis: Features can predict treatment response Daniel Golden MRI-Based Biomarkers Dec 12, / 19
11 Motivation Dynamic Contrast-Enhanced MRI Imaging (DCE-MRI) Acquires multiple images before and after contrast injection Whole tumor, minimally-invasive (unlike biopsy) Reveals tumor kinetic phenotype: morphology and texture Hypothesis: Features can predict treatment response Known Malignancy Selection of Optimal Treatment??? Treatment A Treatment B Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
12 Motivation Dynamic Contrast-Enhanced MRI Imaging (DCE-MRI) Acquires multiple images before and after contrast injection Whole tumor, minimally-invasive (unlike biopsy) Reveals tumor kinetic phenotype: morphology and texture Hypothesis: Features can predict treatment response Known Malignancy MRI Features Selection of Optimal Treatment??? Treatment A Treatment B Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
13 Data Set The Triple-Negative Breast Cancer (TNBC) Trial Clinical trial run by Melinda Telli and Jim Ford at Stanford 93 patients with triple-negative or BRCA-mutated breast cancer 69 patients available for analysis This imaging study: retrospective and proof-of-concept Daniel Golden MRI-Based Biomarkers Dec 12, / 19
14 Example pre-chemo MRIs Daniel Golden MRI-Based Biomarkers Dec 12, / 19
15 Outline 1 Model Features 2 Modeling and Results 3 Conclusion and Future Work Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
16 Outline 1 Model Features 2 Modeling and Results 3 Conclusion and Future Work Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
17 List of Features Semantic Imaging Breast Imaging Reporting and Data System BI-RADS Quantitative Imaging Lesion kinetic texture via the Gray-Level Co-Occurrence Matrix (GLCM) Daniel Golden MRI-Based Biomarkers Dec 12, / 19
18 Semantic Imaging Features BI-RADS Mass: shape, margins, enhancement Non-Mass: distribution, internal enhancement Daniel Golden MRI-Based Biomarkers Dec 12, / 19
19 Tumor Spatial Heterogeneity Daniel Golden MRI-Based Biomarkers Dec 12, / 19
20 Tumor Spatial Heterogeneity Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Daniel Golden MRI-Based Biomarkers Dec 12, / 19
21 Quantitative Imaging Features The Gray-Level Co-Occurrence Matrix (GLCM) Based on texture of kinetic parameters Daniel Golden MRI-Based Biomarkers Dec 12, / 19
22 Quantitative Imaging Features The Gray-Level Co-Occurrence Matrix (GLCM) Based on texture of kinetic parameters Lesion Kinetic Image Daniel Golden MRI-Based Biomarkers Dec 12, / 19
23 Quantitative Imaging Features The Gray-Level Co-Occurrence Matrix (GLCM) Based on texture of kinetic parameters Lesion Kinetic Image Pixel and Neighbor Values Daniel Golden MRI-Based Biomarkers Dec 12, / 19
24 Quantitative Imaging Features The Gray-Level Co-Occurrence Matrix (GLCM) Based on texture of kinetic parameters Lesion Kinetic Image Pixel and Neighbor Values Count and Sum Pixel Amplitude GLCM Pixel Amplitude 2000 Num Pixels 0 Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
25 Quantitative Imaging Features The Gray-Level Co-Occurrence Matrix (GLCM) Based on texture of kinetic parameters Lesion Kinetic Image Pixel and Neighbor Values Count and Sum Number of pixels with value 4 neighboring pixels with value 1 Pixel Amplitude GLCM Pixel Amplitude 2000 Num Pixels 0 Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
26 Quantitative Imaging Features The Gray-Level Co-Occurrence Matrix (GLCM) Based on texture of kinetic parameters Lesion Kinetic Image Pixel and Neighbor Values Count and Sum Number of pixels with value 4 neighboring pixels with value 1 Pixel Amplitude GLCM Pixel Amplitude 2000 Num Pixels 0 Scalar measures of image texture Contrast Correlation Energy Homogeneity Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
27 Outline 1 Model Features 2 Modeling and Results 3 Conclusion and Future Work Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
28 Example Model Results Modeling Methodology Lasso logistic regression (chooses optimal features and creates regression model) Performance assessed via cross-validated ROC curves Daniel Golden MRI-Based Biomarkers Dec 12, / 19
29 Example Model Results Modeling Methodology Lasso logistic regression (chooses optimal features and creates regression model) Performance assessed via cross-validated ROC curves This Model Response: residual tumor and lymph nodes Features: pre-chemo texture and BI-RADS Daniel Golden MRI-Based Biomarkers Dec 12, / 19
30 Example Model Results Modeling Methodology Lasso logistic regression (chooses optimal features and creates regression model) Performance assessed via cross-validated ROC curves This Model Response: residual tumor and lymph nodes Features: pre-chemo texture and BI-RADS Good Sensitivity Predict Residual Nodes and Tumor Bad N= Specificity Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
31 Example Model Results Modeling Methodology Lasso logistic regression (chooses optimal features and creates regression model) Performance assessed via cross-validated ROC curves This Model Response: residual tumor and lymph nodes Features: pre-chemo texture and BI-RADS Good Sensitivity Predict Residual Nodes and Tumor Texture AUC=0.5 Bad N= Specificity Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
32 Example Model Results Modeling Methodology Lasso logistic regression (chooses optimal features and creates regression model) Performance assessed via cross-validated ROC curves This Model Response: residual tumor and lymph nodes Features: pre-chemo texture and BI-RADS Good Sensitivity Predict Residual Nodes and Tumor Texture AUC=0.5 BI-RADS AUC=0.77 Bad N= Specificity Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
33 Example Model Results Modeling Methodology Lasso logistic regression (chooses optimal features and creates regression model) Performance assessed via cross-validated ROC curves This Model Response: residual tumor and lymph nodes Features: pre-chemo texture and BI-RADS Good Sensitivity Predict Residual Nodes and Tumor Texture AUC=0.5 BI-RADS AUC=0.77 Texture and BI-RADS AUC=0.88 Bad N= Specificity Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
34 Selected Features good response poor response BI-RADS mass enhancement homog. BI-RADS mass shape round GLCM kep energy GLCM kep homogeneity BI-RADS non-mass BI-RADS mass margin spiculated GLCM AUC energy feature weight Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
35 Outline 1 Model Features 2 Modeling and Results 3 Conclusion and Future Work Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
36 Conclusion and Future Work Conclusion Contrast-enhanced MRI can predict treatment response Best model: combination of morphological and texture features Future Work Improve model Extend to 3D New quantitative features (e.g., region clustering via superpixels) Combine imaging with other biomarkers (e.g., genomics) Try to predict survival Daniel Golden MRI-Based Biomarkers Dec 12, / 19
37 Conclusion and Future Work Conclusion Contrast-enhanced MRI can predict treatment response Best model: combination of morphological and texture features Future Work Improve model Extend to 3D New quantitative features (e.g., region clustering via superpixels) Combine imaging with other biomarkers (e.g., genomics) Try to predict survival Daniel Golden MRI-Based Biomarkers Dec 12, / 19
38 Thank You Mentor Daniel Rubin Collaborators Jafi Lipson Melinda Telli Jim Ford Katie Planey Nick Hughes Funding Stanford SCIT Program (NIH T32 CA009695) NIH U01 CA Daniel Golden MRI-Based Biomarkers Dec 12, / 19
39 Non-Imaging Features Clinical Age at diagnosis Tumor stage (IA IIIA) Tumor grade (II or III) T and N stage from TNM (T0 T4, N0 N3) ER/PR percent (for non-triple-negative) Ki67 percent Cycles of treatment received (4 or 6) Genomic BRCA 1/2 mutation status Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
40 Other Models Features Feature Sets Lasso Model Response All Clinical All Clinical but Ki67 Ki67 GLCM Pre GLCM Post GLCM Pre and GLCM Post Patterns of Response BI RADS GLCM Pre and BI RADS Residual Tumor Residual Lymph Nodes Residual Tumor and Nodes Best Models Residual tumor and nodes: imaging (as shown) Residual tumor: post-chemo texture Residual nodes: clinical (imaging good too) Daniel Golden MRI-Based Biomarkers Dec 12, / 19
41 Tumor/Nodes Model Results All Clinical All Clinical but Ki67 Ki67 GLCM Pre GLCM Post GLCM Pre and GLCM Post Patterns of Response BI RADS GLCM Pre and BI RADS n=37 n=51 n=44 n=51 n=44 n=41 n=55 n=58 n=51 Predict residual tumor junk junk junk junk junk junk Predict residual tumor and nodes Predict residual nodes junk junk junk All Clinical All Clinical but Ki67 Ki67 GLCM Pre GLCM Post GLCM Pre and GLCM Post Patterns of Response BI RADS GLCM Pre and BI RADS junk junk junk junk Sensitivity Specificity AUC junk AUC < Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
42 Residual Tumor Model Features GLCM Ktrans correlation post chemo GLCM wash out slope contrast post chemo avg wash out post chemo GLCM Ktrans correlation post chemo GLCM AUC contrast pre chemo GLCM kep contrast post chemo GLCM wash out slope contrast post chemo avg wash out post chemo Relevant features for predicting No Residual Tumor GLCM Post chemotherapy AUC b*std GLCM Pre and GLCM Post chemotherapy AUC b*std ki67 percent Ki AUC b*std Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
43 Residual Nodes Model Features Relevant features for predicting No Residual Lymph Nodes BRCA1 result negative 4 treatment cycles stage IIIA TNM N1 TNM N0 BRCA1 result negative stage IIIA 4 treatment cycles ER status 1+ TNM N1 age at diagnosis BRCA2 result negative TNM N0 GLCM kep homogeneity pre chemo GLCM AUC homogeneity pre chemo GLCM kep contrast pre chemo All Clinical AUC All Clinical but Ki AUC GLCM Pre chemotherapy AUC b*std GLCM Ktrans correlation post chemo lesion area post chemo BI RADS non mass like BI RADS mass margin spiculated BI RADS mass margin smooth BI RADS mass shape round GLCM kep homogeneity pre chemo GLCM AUC energy pre chemo BI RADS non mass like GLCM kep energy pre chemo GLCM kep contrast pre chemo BI RADS mass shape round GLCM Post chemotherapy AUC b*std BI RADS AUC b*std GLCM Pre chemotherapy and BI RADS AUC b*std Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
44 Residual Tumor and Nodes Model Features TNM N0 TNM N2 TNM T1 TNM N1 stage IIIA BRCA1 result negative tumor grade II ER status 1+ 4 treatment cycles lesion area post chemo GLCM Ktrans correlation post chemo lesion area post chemo GLCM Ktrans correlation post chemo All Clinical but Ki AUC b*std GLCM Post chemotherapy AUC b*std GLCM Pre and GLCM Post chemotherapy AUC b*std Relevant features for predicting Residual Tumor and Lymph Nodes BI RADS mass shape round BI RADS mass enhancement homogeneous BI RADS mass margin spiculated BI RADS non mass like BI RADS mass enhancement homogeneous BI RADS mass shape round GLCM kep energy pre chemo GLCM kep homogeneity pre chemo BI RADS non mass like BI RADS mass margin spiculated GLCM AUC energy pre chemo BI RADS AUC b*std GLCM Pre chemotherapy and BI RADS AUC b*std Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
45 Dynamic Contrast-enhanced MRI t = 0 sec t = 51 sec t = 61 sec t = 195 sec 1 cm Avg voxel intensity Wash Out k ep 600 Wash In K trans Time (sec) Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
46 Kinetic Modeling t=1.5 min 1 cm Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
47 Kinetic Modeling t=1.5 min Fractional enhancement Data Model 1 cm Minutes Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
48 Kinetic Modeling t=1.5 min Fractional enhancement Data Model 1 cm Minutes K trans (min 1 ) K ep (min 1 ) v e (unitless) Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
49 Breast DCE-MRI Heterogeneity Review Texture Histogram Malignancy Survival Type Sinha et al., 1997; Chen et al., 2007; Woods et al., 2007; Kale et al., 2008; Nie et al., 2008; Agner et al., 2011; Karahaliou et al., 2012 Hauth et al., 2008; Preim et al., 2011 Johansen et al., 2009 Holli et al., 2010 Treatment Response Chang et al., 2004; Padhani et al., 2009 Caveats Generally considered all BC subtypes together Usually reported simple t-tests for each feature; lacked multivariate regression and cross-validation Daniel Golden MRI-Based Biomarkers Dec 12, / 19
50 Breast DCE-MRI Heterogeneity Review Texture Histogram Malignancy Survival Type Sinha et al., 1997; Chen et al., 2007; Woods et al., 2007; Kale et al., 2008; Nie et al., 2008; Agner et al., 2011; Karahaliou et al., 2012 Hauth et al., 2008; Preim et al., 2011 Johansen et al., 2009 Holli et al., 2010 Treatment Response You Are Here Chang et al., 2004; Padhani et al., 2009 Caveats Generally considered all BC subtypes together Usually reported simple t-tests for each feature; lacked multivariate regression and cross-validation Daniel Golden MRI-Based Biomarkers Dec 12, / 19
51 Residual Cancer Burden 1 Cumulative Distribution Function Natural separation points 0<RCB<2.5 (26, 46%) RCB>2.5 (12, 21%) RCB=0 (pcr) (19, 33%) Term 2 (Positive Nodes) RCB Value RCB Residual Nodes (3, 5%) Residual Tumor (22, 39%) pcr (19, 33%) Tumor + Nodes (13, 23%) Term 1 (Primary Tumor) Only 1 case with residual tumor and nodes and RCB<2.5 Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
52 RCB Model Results All Clinical All Clinical but Ki67 Ki67 GLCM Pre GLCM Post GLCM Pre and GLCM Post Patterns of Response BI RADS GLCM Pre and BI RADS n=39 n=53 n=47 n=54 n=44 n=41 n=60 n=64 n=54 a) Predict pcr (RCB=0) junk junk junk junk junk b) Predict RCB > 2.5 junk junk junk junk Sensitivity Specificity AUC Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, / 19
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