EARLY PREDICTION OF NEOADJUVANT SYSTEMIC THERAPY RESPONSE IN TRIPLE NEGATIVE BREAST CANCER USING FUNCTIONAL MAGNETIC RESONANCE IMAGING

Neoadjuvant systemic therapy (NAST) is administered prior to surgery to reduce tumor burden in patients with triple-negative breast cancer (TNBC); however, about half of TNBC patients do not respond to NAST and develop a distant spread within five years. Early assessment of NAST response for triple-negative breast cancer (TNBC) is critical for patient care, both to avoid toxicities from ineffective treatments and to provide novel targeted therapies to the non-responders. The purpose of this study was to explore and then validate functional magnetic resonance imaging (MRI) techniques as imaging biomarkers capable of early assessment of NAST response in TNBC. First, we investigated the potential of tumoral and peritumoral radiomic features extracted from dynamic contrast-enhanced (DCE)-MRI and diffusion-weighted imaging (DWI) to predict response to neoadjuvant systemic therapy (NAST) in TNBC. We explored and validated these features on 163 patients (training set = 109, testing set = 54) at three different time points: before the initiation of NAST (BL), after two cycles of NAST (C2), and after four cycles of NAST (C4). We identified 152 radiomic features with an area under the curve (AUC) ≥ 0.70 for both the testing and training cohorts. The relative changes in the 1st and 5th percentile between BL and C4 had AUCs ≥ 0.80 for both training and testing sets. Secondly, we developed multivariate radiomic models based on multiparametric MRI images and investigated them to predict NAST response. Forty-nine multiparametric MRI-based models had AUCs > 0.75. The top-performing radiomic model used 35 radiomic features and had AUCs of 0.91 and 0.80 for training and testing sets, respectively. Finally, we assessed functional tumor volumes (FTVs) from DCE-MRI as predictors of response to TNBC. FTVs were measured for 100 patients at BL, C2, and C4 using optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The maximum AUC for predicting treatment response using FTV was 0.84 at C4, followed by FTV at C2 (AUC = 0.82). Our study demonstrated the potential of radiomic analysis of multiparametric MRI and FTV as non-invasive biomarkers for early prediction of treatment response in TNBC patients.