Effect of MR Imaging Contrast Thresholds on Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes: A Subgroup Analysis of the ACRIN 6657/I-SPY 1 TRIAL , and ACRIN 6657 Trial Team and I-SPY Investigators Network

Abbreviations: Functional tumor volume (FTV), percent enhancement threshold (PEt), signal enhancement ratio threshold (SERt), hormone receptor (HR), human epidermal growth factor receptor 2 (HER2), neoadjuvant chemotherapy (NACT), pathological complete response (pCR), anthracycline-cyclophosphamide (AC), immunohistochemistry (IHC), triple-negative breast cancer (TNBC), dynamic contrast-enhanced (DCE), region of interest (ROI), receiver operating characteristic (ROC), area under the ROC curve (AUC) Functional tumor volume (FTV) measurements by dynamic contrast-enhanced magnetic resonance imaging can predict treatment outcomes for women receiving neoadjuvant chemotherapy for breast cancer. Here, we explore whether the contrast thresholds used to define FTV could be adjusted by breast cancer subtype to improve predictive performance. Absolute FTV and percent change in FTV (⌬FTV) at sequential time-points during treatment were calculated and investigated as predictors of pathologic complete response at surgery. Early percent enhancement threshold (PEt) and signal enhancement ratio threshold (SER t ) were varied. The predictive performance of resulting FTV predictors was evaluated using the area under the receiver operating characteristic curve. A total number of 116 patients were studied both as a full cohort and in the following groups defined by hormone receptor (HR) and HER2 receptor subtype: 45 HRϩ/HER2Ϫ, 39 HER2ϩ, and 30 triple negatives. High AUCs were found at different ranges of PE t and SER t levels in different subtypes. Findings from this study suggest that the predictive performance to treatment response by MRI varies by contrast thresholds, and that pathologic complete response prediction may be improved through subtype-specific contrast enhancement thresholds. A validation study is underway with a larger patient population. INTRODUCTION Breast cancer, the most common type of cancer among women, is a heterogeneous disease comprising subtypes with different biology, prognosis, and treatment outcome. Breast cancer can be classified into subtypes based on the hormone receptor (HR) status, including both estrogen and progesterone receptors, and human epidermal growth factor receptor 2 (HER2) expression to inform treatment decisions (1, 2). These breast cancer subtype classifications also have implications for disease-free survival and relapse (3). Further understanding of subtype-specific response and effective monitoring by imaging may provide means for early therapeutic intervention, leading to better outcomes (4)