Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.

Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectures and are often validated in small patient cohorts. We developed a Mamba-based deep learning model for predicting chemotherapy efficacy using needle biopsy (MCEN) from 1646 patients with breast cancer across five tertiary hospitals, aiming to predict pathological complete response following NAC. We randomly divided 1023 biopsy samples from one hospital into training and validation sets at an 8:2 ratio and used the remaining four hospitals as external test sets to evaluate the model's performance and robustness. In the training and validation sets, the MCEN achieved areas under the receiver operating characteristic curve (AUROCs) of 0.923 and 0.78, respectively. For the four external test sets, the MCEN achieved AUROCs ranging from 0.761- to 0.809. Incorporating clinicopathological information improved the MCEN model's predictive performance, achieving AUROCs of 0.937 and 0.811 in the training and validation sets, respectively, and ranging from 0.773- to 0.84 in the external test sets. Our study demonstrates the potential of the MCEN as a valuable tool in clinical decision-making.