Development of an MRI Radiomic Machine-Learning Model to Predict Triple-Negative Breast Cancer Based on Fibroglandular Tissue of the Contralateral Unaffected Breast in Breast Cancer Patients

Simple Summary Triple-negative breast cancer is the most aggressive breast cancer subtype. However, women at risk for developing triple-negative breast cancer may not be identified by existing risk models. Thus, we present a study to determine if triple-negative breast cancer can be predicted based on a radiomic analysis and the machine-learning features of the fibroglandular tissue of the contralateral unaffected breast. Our initial results indicate that this approach can be used to predict triple-negative breast cancer. In the future, triple-negative breast-cancer-specific models may be implemented in the screening workflow to identify those women who are at elevated risk for triple-negative breast cancer specifically, for whom early detection and treatment are most essential.Abstract Aim: The purpose of this study was to develop a radiomic-based machine-learning model to predict triple-negative breast cancer (TNBC) based on the contralateral unaffected breast's fibroglandular tissue (FGT) in breast cancer patients. Materials and methods: This study retrospectively included 541 patients (mean age, 51 years; range, 26-82) who underwent a screening breast MRI between November 2016 and September 2018 and who were subsequently diagnosed with biopsy-confirmed, treatment-na & iuml;ve breast cancer. Patients were divided into training (n = 250) and validation (n = 291) sets. In the training set, 132 radiomic features were extracted using the open-source CERR platform. Following feature selection, the final prediction model was created, based on a support vector machine with a polynomial kernel of order 2. Results: In the validation set, the final prediction model, which included four radiomic features, achieved an F1 score of 0.66, an area under the curve of 0.71, a sensitivity of 54% [47-60%], a specificity of 74% [65-84%], a positive predictive value of 84% [78-90%], and a negative predictive value of 39% [31-47%]. Conclusions: TNBC can be predicted based on radiomic features extracted from the FGT of the contralateral unaffected breast of patients, suggesting the potential for risk prediction specific to TNBC.