Abstract P2-09-23: Diffusion-weighted MRI improves imaging prediction of response in the I-SPY 2 trial
Authors: Li W , Wilmes LJ , Newitt DC , Jones EF , Gibbs J , Poon M , Li E , Partridge SC , Kornak J , Esserman LJ , Hylton NM 
Published: February 2018

Abstract
Background: The I-SPY 1 TRIAL demonstrated that functional tumor volume (FTV) measured by dynamic contrast-enhanced (DCE) MRI during neoadjuvant chemotherapy (NAC) predicts both pathologic complete response (pCR) and recurrence free survival1. In addition to DCE, the I-SPY 2 TRIAL is testing whether diffusion weighted MRI (DWI), a non-contrast method that characterizes water mobility and cellularity by measuring the apparent diffusion coefficient (ADC), acquired during the same MRI exam as DCE, can provide valuable distinct information about tumor response. We hypothesize that combining FTV and ADC can improve the predictive performance of breast MRI.
Methods: I-SPY 2 includes women with stage II or III breast cancer with tumor size ≥ 2.5 cm. A sub-cohort of I-SPY 2 patients from 2 graduated experimental drug arms2,3 (N=115 of 263): veliparib-carboplatin (VC, N=38), neratinib (N=37) and their controls (treated with paclitaxel or paclitaxel + trastuzumab, N=40), were included in this study: 148 patients were excluded due to missing imaging data or poor DWI quality. Each patient had four MRI exams: pre-treatment (T1), early treatment (after 3 weekly cycles of experimental drugs, T2), between regimen (T3), and pre-surgery (T4). FTV and ADC were measured for the whole tumor at T1, T2, and T3. Percent change of FTV (ΔFTV) and ADC (ΔADC) at T2 and T3 compared to T1 were analyzed as predictors of pCR. The predictive performance of ΔFTV, ΔADC and their combination was evaluated using a logistic regression model treating pCR as the binary outcome. Odds ratios were estimated for each 10% decrease of ΔFTV and 10% increase of ΔADC to reach pCR. The likelihood ratio test was used to evaluate the effect of variables in the logistic model. The statistical significance level for all testing was set at 0.05.
Results: Out of 115 patients included in this analysis, 36 (31%) reached pCR. The combined model using ΔFTV+ΔADC showed statistically significant improvement over the single predictor ΔFTV alone (p=0.038 for the period T1 to T2 and p&lt;0.001 for the period T1 to T3). The odds ratio estimates represent a 27% increase in odds for each 10% increase in ΔADC after accounting for ΔFTV at T2 and 38% increase at T3 (see Table 1).
Table 1 The odds ratio of ΔFTV and ΔADC with 95% confidence intervalTreatment time pointΔFTV aloneΔADC aloneΔADC adjusted for ΔFTVEarly treatment (T2)1.19 (1.05, 1.36)1.33 (1.06, 1.70)1.27 (1.01, 1.63)Bwteeen regimen (T3)1.41 (1.12, 1.98)1.44 (1.24, 1.71)1.38 (1.18, 1.64)
Conclusion: The addition of ADC to standard FTV MRI may help refine the prediction of treatment response. Evaluation of the method by cancer subtype in a larger cohort is ongoing.
References
1. Hylton NM, Gatsonis CA, Rosen MA, et al. Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology. 2016;279(1):44-55.
2. Rugo HS, Olopade OI, DeMichele A, et al. Adaptive Randomization of Veliparib-Carboplatin Treatment in Breast Cancer. N Engl J Med. 2016;375(1):23-34.
3. Park JW, Liu MC, Yee D, et al. Adaptive Randomization of Neratinib in Early Breast Cancer. N Engl J Med. 2016;375(1):11-22.
Citation Format: Li W, Wilmes LJ, Newitt DC, Jones EF, Gibbs J, Poon M, Li E, Partridge SC, Kornak J, Esserman LJ, Hylton NM. Diffusion-weighted MRI improves imaging prediction of response in the I-SPY 2 trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-23.