Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting 252Cf neutron therapy response in cervical cancer

Background

Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide. Although californium-252 (252Cf) neutron brachytherapy has demonstrated favorable therapeutic efficacy for locally advanced cervical cancer, substantial inter-patient heterogeneity in treatment response still exists. Therefore, developing a reliable non-invasive predictive model before treatment is of considerable clinical importance.