MRI-Based Biomarkers of Therapeutic Response in Triple...

MRI-Based Biomarkers of Therapeutic Responsein Triple-Negative Breast Cancer

Daniel Golden

Postdoctoral Scholar (Radiology)Stanford University

Daniel Rubin Laboratory

NCI Cancer Imaging Fellowship SeminarDecember 12, 2012

Daniel Golden (dgolden1@stanford.edu) MRI-Based Biomarkers Dec 12, 2012 1 / 19

Space Physics

Neutral Atmosphere

Advantage: Free Magnet! Disadvantage: Only 10-4 T

Radiation Belts

Satellites and

astronauts

Space Physics

Neutral Atmosphere

Advantage: Free Magnet!

Disadvantage: Only 10-4 T

Radiation Belts

Satellites and

astronauts

Space Physics

Neutral Atmosphere

Radiation Belts

Satellites and

astronauts

Space Physics

Neutral Atmosphere

Radiation Belts

Satellites and

astronauts

Space Physics

Neutral Atmosphere

Radiation Belts

Satellites and

astronauts

Motivation

Triple-Negative Breast Cancer15% of all breast cancers; 30,000 annual diagnoses; 8000 deathsLacks estrogen, progesterone, HER2 receptorsResponse to chemo is mixed

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

Treatment A

Treatment B

Known Malignancy Selection of Optimal Treatment

Motivation

Treatment A

Treatment B

Motivation

Treatment A

Treatment B

Motivation

Dynamic Contrast-Enhanced MRI Imaging (DCE-MRI)Acquires multiple images before and after contrast injectionWhole tumor, minimally-invasive (unlike biopsy)Reveals tumor kinetic phenotype: morphology and textureHypothesis: Features can predict treatment response

Treatment A

Treatment B

???Treatment A

Treatment B

MRI Features

Motivation

Treatment A

Treatment B

Treatment A

Treatment B

MRI Features

Motivation

Treatment A

Treatment B

???Treatment A

Treatment B

MRI Features

Data Set

The Triple-Negative Breast Cancer (TNBC) TrialClinical trial run by Melinda Telli and Jim Ford at Stanford93 patients with triple-negative or BRCA-mutated breast cancer69 patients available for analysisThis imaging study: retrospective and proof-of-concept

Example pre-chemo MRIs

Outline

1 Model Features

2 Modeling and Results

3 Conclusion and Future Work

Outline

1 Model Features

List of Features

Semantic ImagingBreast Imaging Reporting and Data System BI-RADS

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

Semantic Imaging Features

BI-RADSMass: shape, margins, enhancementNon-Mass: distribution, internal enhancement

Tumor Spatial Heterogeneity

Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor.

Tumor Spatial Heterogeneity

Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor.

Quantitative Imaging Features

The Gray-Level Co-Occurrence Matrix (GLCM)Based on texture of kinetic parameters

Lesion KineticImage

Pixel and Neighbor Values

2 4 6 8

Pixel Amplitude

Countand Sum

Number of pixels with value 4 neighboring pixels with value 1

Contrast Correlation Energy HomogeneityScalar measures of image texture

Lesion KineticImage

2 4 6 8

Pixel Amplitude

Countand Sum

Lesion KineticImage

2 4 6 8

Pixel Amplitude

Countand Sum

Lesion KineticImage

2 4 6 8

Pixel Amplitude

Countand Sum

Lesion KineticImage

2 4 6 8

Pixel Amplitude

Countand Sum

Lesion KineticImage

2 4 6 8

Pixel Amplitude

Countand Sum

Outline

1 Model Features

Example Model Results

Modeling MethodologyLasso logistic regression(chooses optimalfeatures and createsregression model)Performance assessedvia cross-validated ROCcurves

This ModelResponse: residualtumor and lymph nodesFeatures: pre-chemotexture and BI-RADS

0 0.5 10

1 − Specificity

Predict Residual Nodes and Tumor

Bad N=58

TextureAUC=0.5

BI-RADSAUC=0.77

Texture and BI-RADSAUC=0.88

0 0.5 10

1 − Specificity

Bad N=58

TextureAUC=0.5

BI-RADSAUC=0.77

0 0.5 10

1 − Specificity

Bad N=58

TextureAUC=0.5

BI-RADSAUC=0.77

0 0.5 10

1 − Specificity

Bad N=58

TextureAUC=0.5

BI-RADSAUC=0.77

0 0.5 10

1 − Specificity

Bad N=58

TextureAUC=0.5

BI-RADSAUC=0.77

0 0.5 10

1 − Specificity

Bad N=58

TextureAUC=0.5

BI-RADSAUC=0.77

Selected Features

−0.5 0 0.5 1

GLCM AUC energy

BI-RADS mass margin spiculated

BI-RADS non-mass

GLCM kep homogeneity

GLCM kep energy

BI-RADS mass shape round

BI-RADS mass enhancement homog.

feature weight

good responsepoor response

Outline

1 Model Features

Conclusion and Future Work

ConclusionContrast-enhanced MRI can predict treatment responseBest model: combination of morphological and texture features

Future WorkImprove model

Extend to 3DNew quantitative features (e.g., region clustering via superpixels)Combine imaging with other biomarkers (e.g., genomics)

Try to predict survival

Conclusion and Future Work

ConclusionContrast-enhanced MRI can predict treatment responseBest model: combination of morphological and texture features

Future WorkImprove model

Extend to 3DNew quantitative features (e.g., region clustering via superpixels)Combine imaging with other biomarkers (e.g., genomics)

Try to predict survival

Thank You

MentorDaniel Rubin

CollaboratorsJafi LipsonMelinda TelliJim FordKatie PlaneyNick Hughes

FundingStanford SCIT Program (NIH T32 CA009695)NIH U01 CA142555

Non-Imaging Features

ClinicalAge at diagnosisTumor 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 percentCycles of treatment received (4 or 6)

GenomicBRCA 1/2 mutation status

Other Models Features

BI−RADSGLCM Pre and BI−RADS

Patterns of ResponseGLCM Pre and GLCM Post

GLCM PreKi67

All Clinical but Ki67All Clinical

Feature Sets Lasso Model Response

Residual Tumor

Residual Lymph Nodes

Residual Tumorand Nodes

GLCM Post

Best Models

Residual tumor and nodes:imaging (as shown)Residual tumor: post-chemo

textureResidual nodes: clinical(imaging good too)

Tumor/Nodes Model Results

SensitivitySpecificityAUC

junkjunk

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Predict residual nodes

junkjunk

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Predict residual tumor and nodes

junkjunkjunk

junkjunk

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

GLCM Pre and BI−RADSBI−RADS

GLCM PostGLCM Pre

Ki67All Clinical but Ki67

All Clinical

Predict residual tumor

n=37 n=51 n=44 n=51 n=44 n=41 n=55 n=58 n=51

GLCM PostGLCM Pre

All Clinical

junk ≡ AUC < 0.6

Residual Tumor Model Features

Relevant features forpredicting No Residual Tumor

0.5 0.75 1AUC

−0.5 0 0.5

ki67 percent

0.5 0.75 1AUC

GLCM Post−chemotherapy

−1 0 1

avg wash out post−chemoGLCM wash out slope contrast post−chemo

GLCM Ktrans correlation post−chemo

0.5 0.75 1AUC

GLCM Pre− and GLCM Post−chemotherapy

−1 0 1

avg wash out post−chemoGLCM wash out slope contrast post−chemo

GLCM kep contrast post−chemoGLCM AUC contrast pre−chemo

GLCM Ktrans correlation post−chemo

Residual Nodes Model FeaturesRelevant features for

predicting No Residual Lymph Nodes

0.5 0.75 1AUC

All Clinical

−1 0 1

TNM N0TNM N1

stage IIIA4 treatment cycles

BRCA1 result negative

0.5 0.75 1AUC

All Clinical but Ki67

−1 0 1

TNM N0BRCA2 result negative

age at diagnosisTNM N1

ER status 1+4 treatment cycles

stage IIIABRCA1 result negative

0.5 0.75 1AUC

GLCM Pre−chemotherapy

−0.5 0 0.5

GLCM kep contrast pre−chemoGLCM AUC homogeneity pre−chemo

GLCM kep homogeneity pre−chemo

0.5 0.75 1AUC

−0.5 0 0.5

lesion area post−chemoGLCM Ktrans correlation post−chemo

0.5 0.75 1AUC

BI−RADS

−1 0 1

BI−RADS mass shape roundBI−RADS mass margin smooth

BI−RADS mass margin spiculatedBI−RADS non−mass−like

0.5 0.75 1AUC

GLCM Pre−chemotherapy and BI−RADS

−0.5 0 0.5

BI−RADS mass shape roundGLCM kep contrast pre−chemoGLCM kep energy pre−chemo

BI−RADS non−mass−likeGLCM AUC energy pre−chemo

GLCM kep homogeneity pre−chemo

Residual Tumor and Nodes Model Features

Relevant features forpredicting Residual Tumor and Lymph Nodes

0.5 0.75 1AUC

All Clinical but Ki67

−1 0 1

4 treatment cyclesER status 1+

tumor grade IIBRCA1 result negative

stage IIIATNM N1TNM T1TNM N2TNM N0

0.5 0.75 1AUC

−1 0 1

GLCM Ktrans correlation post−chemolesion area post−chemo

0.5 0.75 1AUC

GLCM Pre− and GLCM Post−chemotherapy

−0.5 0 0.5

GLCM Ktrans correlation post−chemolesion area post−chemo

0.5 0.75 1AUC

BI−RADS

−1 0 1

BI−RADS non−mass−likeBI−RADS mass margin spiculated

BI−RADS mass enhancement homogeneousBI−RADS mass shape round

0.5 0.75 1AUC

GLCM Pre−chemotherapy and BI−RADS

−1 0 1

GLCM AUC energy pre−chemoBI−RADS mass margin spiculated

BI−RADS non−mass−likeGLCM kep homogeneity pre−chemo

GLCM kep energy pre−chemoBI−RADS mass shape round

BI−RADS mass enhancement homogeneous

Dynamic Contrast-enhanced MRI

0 50 100 150 200

Time (sec)

t = 0 sec

t = 51 sec t = 61 sec t = 195 sec

Wash In ≈ Ktrans

Wash Out ≈ kep

Kinetic Modeling

t=1.5 min

0 5 10 15−1

Minutes

Fractionalenhancement

DataModel

3ve(unitless)

Ktrans (min−1)

Kep(min−1)

Kinetic Modeling

t=1.5 min

1 cm 0 5 10 15−1

Minutes

DataModel

3ve(unitless)

Ktrans (min−1)

Kep(min−1)

Kinetic Modeling

t=1.5 min

1 cm 0 5 10 15−1

Minutes

DataModel

3ve(unitless)

Ktrans (min−1)

Kep(min−1)

Breast DCE-MRI Heterogeneity Review

Malignancy Survival Type TreatmentResponse

Texture

Sinha et al., 1997; Chen etal., 2007; Woods et al.,

2007; Kale et al., 2008; Nieet al., 2008; Agner et al.,2011; Karahaliou et al.,

Holli etal.,

Histogram Hauth et al., 2008; Preim etal., 2011

Johansenet al.,2009

Chang etal., 2004;

Padhani etal., 2009

Caveats

Generally considered all BC subtypes together

Usually reported simple t-tests for each feature; lacked multivariate regressionand cross-validation

Breast DCE-MRI Heterogeneity Review

Malignancy Survival Type TreatmentResponse

Texture

Sinha et al., 1997; Chen etal., 2007; Woods et al.,

2007; Kale et al., 2008; Nieet al., 2008; Agner et al.,2011; Karahaliou et al.,

Holli etal.,

YouAre

Histogram Hauth et al., 2008; Preim etal., 2011

Johansenet al.,2009

Chang etal., 2004;

Padhani etal., 2009

Caveats

Generally considered all BC subtypes together

Usually reported simple t-tests for each feature; lacked multivariate regressionand cross-validation

Residual Cancer Burden

Term 1 (Primary Tumor)

0 1 2 30

0<RCB<2.5

(26, 46%)RCB=0

(19, 33%)

RCB>2.5

(12, 21%)

Residual

(3, 5%)

(19, 33%)Residual

(22, 39%)Tumor

+ Nodes

(13, 23%)

0 1 2 3 4 50

RCB Value

Natural separation points

Only 1 case with residual tumor and nodes and RCB<2.5

RCB Model Results

SensitivitySpecificityAUC

junkjunkjunk

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

b) Predict RCB > 2.5

junkjunk

junkjunkjunk

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

GLCM PostGLCM Pre

All Clinical

a) Predict pCR (RCB=0)

n=39 n=53 n=47 n=54 n=44 n=41 n=60 n=64 n=54

MRI-Based Biomarkers of Therapeutic Response in Triple...

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