Deep Learning Models Based on Pretreatment MRI and Clinicopathological Data to Predict Responses to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.

Abstract

Purpose: To develop deep learning models for predicting the pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in patients with triple-negative breast cancer (TNBC) based on pretreatment multiparametric breast MRI and clinicopathological data.

Methods: The prospective institutional review board-approved study [NCT02276443] included 282 patients with stage I-III TNBC who had multiparametric breast MRI at baseline and underwent NAST and surgery during 2016-2021. Dynamic contrast-enhanced MRI (DCE), diffusion-weighted imaging (DWI), and clinicopathological data were used for the model development and internal testing. Data from the I-SPY 2 trial (2010-2016) were used for external testing. Four variables with a potential impact on model performance were systematically investigated: 3D model frameworks, tumor volume preprocessing, tumor ROI selection, and data inputs.

Results: Forty-eight models with different variable combinations were investigated. The best-performing model in the internal testing dataset used DCE, DWI, and clinicopathological data with the originally contoured tumor volume, the tight bounding box of the tumor mask, and ResNeXt50, and achieved an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.60-0.88). The best-performing models in the external testing dataset achieved an AUC of 0.72 (95% CI: 0.57-0.84) using only DCE images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNeXt50) and an AUC of 0.72 (95% CI: 0.56-0.86) using only DWI images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNet18).

Conclusions: We developed 3D deep learning models based on pretreatment data that could predict pCR to NAST in TNBC patients.