For breast cancer, the standard of treatment for most patients is neoadjuvant chemotherapy (NAC), but response rates may vary among patients, causing delays in appropriate treatment. The authors of this prospective study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage of breast cancer treatment. The authors found that the proposed deep learning radiomics pipeline (DLRP) strategy has the potential to effectively predict NAC response at an early stage for breast cancer patients.
Key points
- We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points.
- Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC.
- The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
Authors: Jionghui Gu, Tong Tong, Chang He, Min Xu, Xin Yang, Jie Tian, Tianan Jiang & Kun Wang
