Abstract PD16-08: Title: Characterizing Changes in Tumor Heterogeneity via Radiomic Phenotyping for Predicting Response to Neoadjuvant Chemotherapy for Locally Advanced Breast Cancer: Preliminary Results from the ACRIN 6698/I-SPY 2 trial

Abstract Purpose: To predict pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC), from baseline and early-treatment DCE-MRI scans, in the context of the ACRIN 6698/I-SPY 2 BMMR2 challenge. Materials and Methods: The BMMR2 dataset consists of 191 patients undergoing NAC for locally advanced breast cancer as part of the ACRIN 6698/I-SPY 2 trial. DCE-MRI was obtained at time points T0 (pre-NAC), T1 (3 weeks), and T2 (12 weeks). The BMMR2 challenge provided the MRI scans, tumor annotations, and limited clinical and demographic information. The data were split 60/40; using the 60% training data, the task was to develop models to predict pCR; the competition was for best area under the curve (AUC) when applied to the 40% unseen test data. Using the publicly available CaPTk software we calculated 3 types of radiomic features within the segmented tumor volume: 1) texture of the signal enhancement ratio (SER) kinetic map of T0 images; 2) texture of the difference between the T1 kinetic maps (PE, WIS, WOS, and SER) and corresponding T0 maps; 3) texture of the difference between the T1 kinetic maps and the corresponding T0 maps, with T1 scans deformably registered to T0 scans. ComBat harmonization was applied to the extracted features to account for MRI acquisition differences. We computed the tumor longest diameter, functional tumor volume (FTV), and clinical tumor size each at T0 and T1. We modeled pCR via logistic regression. Using the training data alone, with the criteria of performance in univariable modeling and low collinearity, we selected radiomic features and clinical, demographic, and size covariates. We then performed PCA on the combined set of selected radiomic features and size covariates. We evaluated multivariable models including the selected clinical covariates in combination with the first few PCs via cross-validated AUC (5-fold, 200 repetitions) on the training data. The best models were submitted for independent evaluation on the unseen test data of the BMMR2 challenge. Results: Of the available clinical covariates, only hormone receptor (HR)± and human epidermal growth factor receptor 2 (HER2)± had any association with pCR. We retained these in all models, and performed PCA on the set combining the best-performing features and the size variables FTV at T0, FTV at T1, and longest diameter at T1. Models based on the first few PCs, HR, and HER2, had training AUCs in 0.78–0.81. Our best-performing model had an AUC on test data of 0.84, using the covariates PCs 1–5, HR, and HER2 (Table 1). Conclusions: Our preliminary results suggest that radiomic phenotyping of changes in tumor heterogeneity can accurately predict pCR early in the course of NAC. Future analysis with larger samples from ISPY-2 could also examine the effect of different therapies, including targeted therapy and immunotherapy. Table 1: Performance of candidate logistic regression models on training and test data. AUC: Area under receiver operating characteristic curve. * Mean 5-fold cross-validated AUC across 200 replicates. † Competition best-performing predictions. Citation Format: Eric A. Cohen, Rhea D. Chitalia, Snekha Thakran, Walter C. Mankowski, Alex Anh-Tu Nguyen, Hannah Horng, Elizabeth S. McDonald, Michael Feldman, Angela DeMichele, Despina Kontos. Title: Characterizing Changes in Tumor Heterogeneity via Radiomic Phenotyping for Predicting Response to Neoadjuvant Chemotherapy for Locally Advanced Breast Cancer: Preliminary Results from the ACRIN 6698/I-SPY 2 trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD16-08.