Multi-Scale MRI, Radiology Reports, and Blood Biomarker–Guided Multimodal Deep Learning for Predicting Postoperative Recurrence in Cervical Cancer: A Multicenter Study

Objectives

Cervical cancer (CC) remains a leading cause of cancer-related morbidity and mortality among women worldwide, with postoperative recurrence posing a major challenge to long-term survival. Multimodal data integration, including imaging, textual, and clinical information, holds significant potential for enhancing prognostic prediction. This study aims to develop and validate a multimodal, multi-scale deep learning (DL) model for predicting recurrence in operable CC patients.