A self-interpretable deep learning network for early prediction of pathologic complete response to neoadjuvant chemotherapy based on breast pre-treatment dynamic contrast-enhanced magnetic resonance imaging

Accurate prediction of pathologic complete response to neoadjuvant chemotherapy non-invasively before treatment via dynamic contrast-enhanced magnetic resonance imaging is vital for developing a personalized therapy strategy. However, the application of deep learning in this domain is characterized by its black-box nature, largely relying on post-hoc analysis to interpret final decision-making. This reliance results in a lack of self-interpretability in the operational mechanisms of feature extraction, feature fusion, and decision-making. Moreover, these models have demonstrated unsatisfactory prediction performance due to insufficient feature modeling. To address these issues, we propose a self-interpretable deep learning network that can provide the intrinsic interpretability of feature extraction, multi-scale feature fusion, and final prediction. First, the interpretable perception module is designed to extract features both effectively and interpretably. Furthermore, the interpretable adaptive multi-scale feature fusion module is proposed to fuse multi-scale features. Finally, an end-to-end self-interpretable deep learning network is presented to predict pathologic complete response with self-interpretability. Validated on a multi-center pre-treatment dynamic contrast-enhanced magnetic resonance imaging dataset, our self-interpretable deep learning network outperforms state-of-the-art methods in both prediction performance and self-interpretability, improving the area under the receiver operating characteristic curve by at least 4.81% while providing both qualitative and quantitative self-interpretability. Our study demonstrates that our proposed self-interpretable deep learning network can extract key information from pre-treatment breast dynamic contrast-enhanced magnetic resonance imaging while enhancing both the prediction performance and the transparency of the model, thereby improving its trustworthiness in clinical settings. • IPM improves the self-interpretability of feature extraction process. • IFFM adaptively interprets the process of multi-scale feature fusion. • Our SIDLN provides precise and self-interpretable pCR prediction to NAC. • The interpretability of SIDLN surpasses that of the existing post-hoc methods. • Our methods enhance the trustworthiness of decision-making in medicine.