Deep learning-based non-contrast MRI model for nasopharyngeal carcinoma diagnosis: an end-to-end gadolinium-free solution
Year: 2025
Type: article
Abstract: 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.
Source: npj Digital Medicine
Authors: Zhen Li, Yiqian Shi, Yiqian Shi, Li Wang, Jiayu Lu, Xueli Liu, Xueli Liu, Jonghye Woo, Jingsong Ouyang, Jiyi Hu, Di Zhou, Wentao Gong, Xiaojing Yang, Haoyan Yu, Haoyan Yu, Yongkang Wang, Feidi Liu, Yafei Dong, Meng Ye, Shang Shi, Xiao Chen, Mingyan Qiu, Georges El Fakhri, Lin Kong, Xicai Sun, Yefeng Zheng, Yuxuan Shi, Yuxuan Shi, Hongmeng Yu, Hongmeng Yu, Xiaofeng Liu, Xiaofeng Liu
Institutions: Fudan University, Yale University, Eye & ENT Hospital of Fudan University, Boston University, Broad Institute, Massachusetts Institute of Technology, Harvard University, Gordon Center for Medical Imaging, Massachusetts General Hospital, Fudan University Shanghai Cancer Center, Brigham and Women's Hospital, Shanghai Sixth People's Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing Drum Tower Hospital, Westlake University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,090
OpenAlex ID: W7116748330