Qualitative and Quantitative Image-Based Biomarkers of Therapeutic Response for Triple Negative...

7/28/2019 Qualitative and Quantitative Image-Based Biomarkers of Therapeutic Response for Triple Negative Cancer

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Qualitative and Quantitative Image-Based

Biomarkers of Therapeutic Response for

Triple-Negative Breast Cancer

Daniel I. Golden, Jafi A. Lipson, Melinda L. Telli, James M. Ford,

Daniel L. Rubin

Department of Radiology, Stanford UniversityDaniel Rubin Laboratory

AMIA Joint Summits on Translational Bioinformatics

March 1822, 2013

Daniel Golden ([email protected]) Breast Cancer Imaging Biomarkers Mar 1822, 2013 1 / 14


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Motivation


15% of all breast cancers; 30,000 annual diagnoses; 8000 deathsLacks estrogen, progesterone, HER2 receptors

Response to chemo is mixed



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Motivation




Critical NeedA way to predict in advance whether patients will respond:

Precision Medicine



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Motivation




Critical NeedA way to predict in advance whether patients will respond:

Precision Medicine



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Motivation

Dynamic Contrast-Enhanced MRI (DCE-MRI)Acquires multiple images before and after contrast injection

Whole tumor, minimally-invasive (unlike biopsy)

Reveals tumor kineticphenotype: morphology and texture

Hypothesis: Features can predict treatment response



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Motivation







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Motivation







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Outline

1 Imaging Biomarkers

2 Modeling and Results

3 Conclusion and Future Work



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Outline






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List of Features

Semantic Imaging

Breast Imaging Reporting and Data System (BI-RADS)

Quantitative ImagingLesion kinetic texture via the Gray-Level Co-Occurrence Matrix

(GLCM)

Both assessed prior to chemotherapy



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List of Features

Semantic Imaging

Breast Imaging Reporting and Data System (BI-RADS)

Quantitative ImagingLesion kinetic texture via the Gray-Level Co-Occurrence Matrix

(GLCM)

Both assessed prior to chemotherapy



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Semantic Imaging Features

BI-RADS

Checklist of descriptors of lesion shape, margins, enhancement



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Semantic Imaging Features

BI-RADS

Checklist of descriptors of lesion shape, margins, enhancement


S


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Tumor Spatial Heterogeneity


T S i l H i


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Tumor Spatial Heterogeneity


D i C t t h d MRI


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Dynamic Contrast-enhanced MRI


D i C t t h d MRI


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Dynamic Contrast-enhanced MRI


Quantitative Texture via the GLCM


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The Gray-Level Co-Occurrence Matrix (GLCM)

Based on kinetic texture (rate of contrast uptake)

Gray Level = pixel value




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Outline


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Outline





Example Model Results


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Modeling Methodology

Cohort: 60 neoadjuvant triple-negative BC patients

Lasso logistic regression (includes feature selection)

Performance assessed via cross-validated ROC curves




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Outline


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Outline





Conclusion and Future Work


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Conclusion

Contrast-enhanced MRI can predict treatment response

Both morphological and texture features effective for different

response definitions

Future WorkImprove model

Extend features to 3DNew quantitative features (e.g., wavelets, region clustering viasuperpixels)

Combine imaging with other biomarkers (e.g., genomics)Sensitivity analysis

Validate in independent cohort


Conclusion and Future Work


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Conclusion

Contrast-enhanced MRI can predict treatment response

Both morphological and texture features effective for different

response definitions

Future WorkImprove model

Extend features to 3DNew quantitative features (e.g., wavelets, region clustering viasuperpixels)

Combine imaging with other biomarkers (e.g., genomics)Sensitivity analysis

Validate in independent cohort


Thank You


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Mentor

Daniel Rubin

Collaborators

Jafi Lipson

Melinda Telli

Jim Ford

Katie Planey

Nick Hughes

Funding

Stanford SCIT Program (NIH T32 CA009695)

NIH U01 CA142555


Breast DCE-MRI Heterogeneity Review


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g y

Malignancy Survival TypeTreatment

Response

Texture

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

Holli et

al.,

2010

HistogramHauth et al., 2008; Preim et

al., 2011

Johansen

et al.,

2009

Chang et

al., 2004;

Padhani et

al., 2009


Breast DCE-MRI Heterogeneity Review


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Malignancy Survival TypeTreatment

Response

Texture

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

Holli et

al.,

2010

You

Are

Here

HistogramHauth et al., 2008; Preim et

al., 2011

Johansen

et al.,

2009

Chang et

al., 2004;

Padhani et

al., 2009


Example Selected Features


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BI-RADS to predict residual tumor + nodes


Data Set


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The Triple-Negative Breast Cancer (TNBC) Trial

Clinical trial run by Melinda Telli and Jim Ford at Stanford

93 patients with triple-negative orBRCA-mutated breast cancer69 patients available for analysis

This imaging study: retrospectiveand proof-of-concept


Non-Imaging Features


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Clinical

Age at diagnosis

Tumor stage (IAIIIA)

Tumor grade (II or III)

T and N stage from TNM (T0T4, N0N3)

ER/PR percent (for non-triple-negative)

Ki67 percent

Cycles of treatment received (4 or 6)

Genomic

BRCA 1/2 mutation status


Kinetic Modeling


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Kinetic Modeling


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Kinetic Modeling


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Lasso


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All Model Results


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Model Features


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Model Features


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Model Features


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Model Features


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Residual Cancer Burden


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Qualitative and Quantitative Image-Based Biomarkers of Therapeutic Response for Triple Negative...

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