breast mri for early prediction of residual disease following neoadjuvant chemotherapy: optimization...
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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
49
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+
4
57
39
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
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