Multi-Scale MRI, Radiology Reports, and Blood Biomarker-Guided Multimodal Deep Learning for Predicting Postoperative...

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. Methods: This multicenter retrospective study included 445 operable CC patients with preoperative MRI and corresponding radiology reports from three institutions. A Multi-Scale Model (MSM) combining ConvNeXt and dual-path Vision Transformer (ViT) was developed. Textual features extracted from radiology reports using BERT were fused with imaging and clinical data to construct a multimodal network (MSM-TC). Model interpretability was enhanced using SHapley Additive exPlanations (SHAP) and attention visualization. Results: The MSM integrating ConvNeXt and ViT achieved receiver operating characteristic curve (AUC) values of 0.944, 0.837, and 0.681 in the training, internal validation, and external validation cohorts, respectively. Incorporating textual data (MSM-T) further improved performance (AUCs: 0.902, 0.860, and 0.742). The final multimodal model (MSM-TC) integrating imaging, textual, and clinical data demonstrated the highest predictive accuracy with AUCs of 0.930, 0.860, and 0.798 across training, internal validation, and external validation cohorts, respectively. Kaplan-Meier analysis confirmed that the model-derived risk score effectively stratified patients into high- and low-risk groups. Conclusions: Our multimodal, multi-scale DL framework robustly predicts recurrence and survival in operable CC patients, supporting its potential for individualized prognostic assessment and future clinical translation.