Breast MRI for Early Prediction of Residual Disease Following Neoadjuvant Chemotherapy: Optimization of Response Cut-Point by Tumor Subtype

05/09/2016

Wen Li1, Vignesh Arasu1, Ella Jones1, David C. Newitt1, Lisa Wilmes1, John Kornak2, Laura Esserman3, Nola M. Hylton1

1 Radiology and Biomedical Imaging2 Epidemiology & Biostatistics3 Surgery

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Dynamic contrast-enhanced MRI was used to monitor tumor response to neoadjuvant chemotherapy (NACT) in I-SPY 1 / ACRIN 6657 TRIAL for locally advanced breast cancer*

Early identification of poor responding patients at high risk of relapse during NACT will guide treatment interventions

MRI is superior for clinical assessment and pCR prediction, but its predictive performance differs among breast cancer subtypes defined by hormone receptor (HR) and HER2 status

Background

*Hylton et al. Radiology 2012;263(3):663-672 and Radiology 2016; 279 (1): 44-55

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ObjectiveTo optimize the sensitivity and specificity of MRI-based biomarkers to identify non-responders at an early time-point during neoadjuvant chemotherapy

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I-SPY 1 TRIAL Multi-center breast cancer clinical trial for patients undergoing neoadjuvant

chemotherapy (duration: 2002 − 2006)

Women with locally advanced breast cancer (≥3 cm tumors)

Total enrollment: n=237

Surgery Clinical

Study

MRI1 MRI2 MRI3 MRI4

Anthracycline Taxane

Early change

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Imaging Protocol Single breast sagittal scan T1-weighted images with contrast and fat suppression T2-weighted fast spin echo sequence with fat suppression

• Single dose of gadolinium injection (0.1 mmol/kg body weight)

• Scan time duration between 4.5 and 5 minutes

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Functional Tumor Volume (FTV)• The sum of voxels assigned as tumor by having enhancement

above pre-defined thresholds of percentage enhancement (PEt) and signal enhancement ratio (SERt)

MRI1 MRI2 MRI3 MRI4

FTV1=246 ccFTV2=87 cc FTV3=44 cc FTV4=29 cc

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PEt/SERt InfluenceM

RI

Sig

nal

t1 t2t0

S2

S1

∆S1 ∆S2S0

injection

Plateau0.9≤SER≤1.1

SER map

WashoutSER>1.1

GradualSER<0.9

PE = ΔS1

S0× 100% SER =

ΔS1

ΔS2

PEt=30% / SERt=0

FTV = 53 cc

PEt=70% / SERt=0

FTV = 41 cc

PEt=110% / SERt=0

FTV = 22 cc

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Cut-point

Negative condition: pCR or RCB 0/I (responders) Positive condition: non-pCR or RCB II/III (non-responders)

o Negative test: ΔFTVi < cut-pointo Positive test: ΔFTVi ≥ cut-point

Condition

Non-responders Responders

Test

Positive aTrue positive

bFalse positive

Negative cFalse negative

dTrue negative

2×2 contingency table

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Prediction optimization

PEt/SERt FTV ΔFTVi

Cut-point

Predict

responders

Non-responders

PEt/SERt maximize AUC of ΔFTVi predicting response.Cut-point: maximize sensitivity when keeping specificity≥90%

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Results (patient population)

pCR; 34non-

pCR; 82

RCB 0; 34

RCB I; 10

RCB II; 49

RCB III; 23

HR+/HER2-; 45

HER2+; 39

Triple negative;

30pCR RCB

responders

pCR RCB

pCR RCB

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Optimization example – thresholds

MRI1 MRI2

1.75 1.3 1.00.9

SERDefault: PEt=70% / SERt=0FTV1=5.31 cc FTV2=5.14 cc ΔFTV2=-3%

Optimized by maximize AUC: PEt=140% / SERt=1.4FTV1=1.33 cc FTV2=2.58 cc ΔFTV2=94%

Predictor: ΔFTV2 Cancer subtype: HR+/HER2-

MRI1

MRI1

MRI2

MRI2

AUC=0.71

AUC=0.83

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Optimization example – cut pointPredictor: ΔFTV2 Cancer subtype: HR+/HER2-

cp (%)

Sensitivity

Specificity

Default 17 15% 100%Optimized

-50 72% 100%

MRI1 MRI2

Default: PEt=70% / SERt=0

Optimized by maximize AUC: PEt=140% / SERt=1.4

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Optimization Results – HR+/HER2-

7282

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Sensitivities with PEt/SERt for ΔFTVs

Sens

itivi

ty (%

)

ΔFTV2 ΔFTV3 ΔFTV4

Optimized PEt/SERt

Cut point(%)

Sensitivity (%)

Specificity (%)

ΔFTV2 140%/1.4 -50 72 100

ΔFTV3 130%/0 -99.5 82 100

ΔFTV4 90%/0 -94 49 100

Responders: pCRNon-responders: non-pCR

Comparison of optimization for different ΔFTV

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Optimization Results – HER2+

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57

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Sensitivities with PEt/SERt for ΔFTVs

Sens

itivi

ty (%

)

ΔFTV2 ΔFTV3 ΔFTV4

Optimized PEt/SERt

Cut point(%)

Sensitivity (%)

Specificity (%)

ΔFTV2 60%/0.6 11 4 94

ΔFTV3 130%/2.0 -83.5 57 94

ΔFTV4 140%/0 -99 39 94

Responders: pCRNon-responders: non-pCR

Comparison of optimization for different ΔFTV

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Optimization Results – Triple Negative

63 63 59

Sensitivities with PEt/SERt for ΔFTVs

Sens

itivi

ty (%

)

ΔFTV2 ΔFTV3 ΔFTV4

Optimized PEt/SERt

Cut point(%)

Sensitivity (%)

Specificity (%)

ΔFTV2 70%/0.4 -35.5 63 91

ΔFTV3 140%/0 -98 63 91

ΔFTV4 130%/0.2 -99.5 58 91

Responders: pCRNon-responders: non-pCR

Comparison of optimization for different ΔFTV

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Conclusions The performance of using functional tumor volume (FTV) to predict

response to neoadjuvant chemotherapy was impacted by the choice of enhancement thresholds

Cut-point of FTV demonstrated tradeoffs between sensitivity and specificity

Maximum sensitivities were found as early as after one cycle of NACT (triple negative) and between regimens (HR+/HER2- and HER2+) when specificity is ensured to be above 90%

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Future work A large cohort will be studied for optimization model validation HER2+ subtype will be further categorized into HR+/HER2+ and

HR-/HER2+ if the sample size allows

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Acknowledgement Thanks to patients and investigators of ACRIN 6657 and I-SPY 1

TRIAL This work is funded in part by NIH/NCI U01 CA151235