鼻咽癌诊断的深度学习非增强MRI模型：一项端到端的无钆解决方案

Nasopharyngeal carcinoma (NPC) diagnosis and routine follow-up for recurrence typically rely on contrast-enhanced MRI. This study introduces a deep learning model for diagnosing NPC using only non-contrast MRI, reducing the need for gadolinium-based contrast agents (GBCA). This approach helps avoid safety concerns related to GBCA deposition, while also shortening scan times and reducing costs. In this study, we propose an innovative deep learning model for NPC diagnosis using only non-contrast MRI, reducing the need for gadolinium-based contrast agents (GBCA). This approach not only mitigates potential safety concerns associated with residual GBCA deposition but also reduces scan time and examination costs. The study consisted of three phases. Firstly, a novel knowledge distilled modality fusion model is developed using a cohort of 854 cases and tested its performance on an internal set (257 cases, AUC = 0.95) and an independent external set (277 cases, AUC = 0.86). Secondly, the proposed method was compared with: (1) Non-contrast MRI without model improvement (Baseline 1) and (2) current virtual-contrast enhancement-based NPC diagnosis using three state-of-the-art methods (Baselines 2-4). The proposed model consistently outperformed Baselines 1 on both internal dataset (AUC: 0.95 vs. 0.93) and external test set (AUC: 0.86 vs. 0.82). Additionally, it surpassed Baselines 2-4, achieving performance gains of 6.7%, 69.8%, and 28.6% in AUC, over three state-of-the-art methods. Thirdly, the effectiveness of this model was evaluated through a fully crossed multi-reader, multi-case study involving 13 readers from 6 hospitals. The results showed that with AI assistance, readers could diagnose NPC using only non-contrast MRI, achieving results that were not inferior to contrast-enhanced imaging (AUC: 0.90 vs. 0.93, p<0.01). In conclusion, this study demonstrated the model's potential as a safe, cost-effective, and GBCA-free option for NPC diagnosis in clinical practice.