Predicting pathological complete response based on weakly and semi-supervised joint learning in breast cancer multi-parametric MRI

Neoadjuvant chemotherapy (NAC) is the primary treatment used to reduce the tumor size in early breast cancer. Patients who achieve a pathological complete response (pCR) after NAC treatment have a significantly higher five-year survival rate. However, accurately predicting whether patients could achieve pCR remains challenging due to the limited availability of manually annotated MRI data. This study develops a weakly and semi-supervised joint learning model that integrates multi-parametric MR images to predict pCR to NAC in breast cancer patients. First, the attention-based multi-instance learning model is designed to characterize the representation of multi-parametric MR images in a weakly supervised learning setting. The Mean-Teacher learning framework is then developed to locate tumor regions for extracting radiochemical parameters in a semi-supervised learning setting. Finally, all extracted MR imaging features are fused to predict pCR to NAC. Our experiments were conducted on a cohort of 442 patients with multi-parametric MR images and NAC outcomes. The results demonstrate that our proposed model, which leverages multi-parametric MRI data, provides the AUC value of over 0.85 in predicting pCR to NAC, outperforming other comparative methods.