机构：[1]Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.[2]Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.[3]Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.[4]Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.[5]Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.[6]Department of Radiology, Zhongshan Hospital Fudan University, Shanghai, China.[7]Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.河北医科大学第四医院CT磁共振科医技科室[8]Shanghai Key Laboratory of Sleep Disordered Breathing, Department of Otolaryngology-Head and Neck Surgery, Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.[9]Department of Ophthalmology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China.[10]Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. In a Chinese seven-center cohort, data from 347 subjects were analyzed. Our one-step model incorporated normal tissue/organ labels to provide contextual information, offering a solution for tumors with complex backgrounds. To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. This success suggests potential for AI models in screening other whole-body primary/metastatic tumors.
基金：
National Natural Science Foundation of China (National Science Foundation of China) [82472579, 82102344, 82172228]; National Natural Science Foundation of China [2023ZZ02023]; Shanghai Plastic Surgery Research Center of Shanghai Priority Research Center; Shanghai Clinical Research Center of Plastic and Reconstructive Surgery [22MC1940300, YBKA202204]; Science and Technology Commission of Shanghai Municipality; Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine
语种：
外文
被引次数：
WOS：
PubmedID：
中科院分区：
出版当年[2025]版：
大类|1 区医学
小类|1 区卫生保健与服务1 区医学：信息
最新[2025]版：
大类|1 区医学
小类|1 区卫生保健与服务1 区医学：信息
JCR分区：
出版当年[2024]版：
Q1HEALTH CARE SCIENCES & SERVICESQ1MEDICAL INFORMATICS
最新[2024]版：
Q1HEALTH CARE SCIENCES & SERVICESQ1MEDICAL INFORMATICS
第一作者机构：[1]Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.[2]Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
共同第一作者：
通讯作者：
通讯机构：[1]Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.[2]Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
推荐引用方式(GB/T 7714)：
Wei Cheng-Jiang,Tang Yan,Sun Yang-Bai,et al.A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification[J].NPJ DIGITAL MEDICINE.2025,8(1):doi:10.1038/s41746-025-01454-z.
APA：
Wei, Cheng-Jiang,Tang, Yan,Sun, Yang-Bai,Yang, Tie-Long,Yan, Cheng...&Li, Qing-Feng.(2025).A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.NPJ DIGITAL MEDICINE,8,(1)
MLA：
Wei, Cheng-Jiang,et al."A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification".NPJ DIGITAL MEDICINE 8..1(2025)
医学