End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study.

This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models with varied input sources were compared. Performance (AUC, accuracy/ACC) and reading times of three radiologists were compared. The proposed Model SEG-CL-NC achieved AUC/ACC of 0.823/0.776 (Internal Test Set 1) and 0.836/0.781 (Internal Test Set 2). In External Dataset Centers 2, 3, and 4, its ACC was 0.714, 0.740, and 0.756, comparable to contrast-enhanced models and radiologists (P > 0.05), while its diagnosis time was significantly shorter than radiologists (P < 0.01). Our results suggested that the proposed Model SEG-CL-NC could achieve comparable performance to contrast-enhanced models and radiologists in diagnosing benign and malignant PSTs, offering an accurate, efficient, and cost-effective tool for clinical practice.