Evidence map›Paper›PMID 41430426›Full record

ArticleNPJ digital medicine2025

Deep learning-based non-contrast MRI model for nasopharyngeal carcinoma diagnosis: an end-to-end gadolinium-free solution.

Zhen Li, Yiqian Shi, Li Wang, Jiayu Lu, Xueli Liu, Jonghye Woo, Jingsong Ouyang, Jiyi Hu, Di Zhou, Wentao Gong and 16 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Zhen Li *ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Yiqian Shi *Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Li Wang *ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Jiayu LuDepartment of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
Xueli LiuENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Jonghye WooGordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Jingsong OuyangYale Biomedical Imaging Institute and Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Jiyi HuDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China.
Di ZhouENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Wentao GongENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Xiaojing YangDepartment of Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Haoyan YuDepartment of Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Yongkang WangDepartment of Radiology, Nanjing Drum Tower Hospital Group Suqian Hospital, Suqian, China.
Feidi LiuDepartment of Radiology, Jiangsu Province Suqian Hospital, Suqian, China.
Yafei DongYale Biomedical Imaging Institute and Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Meng YeDepartment of Radiology, Nanjing Drum Tower Hospital Group Suqian Hospital, Suqian, China.
Shang ShiENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Xiao ChenENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Mingyan QiuENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Georges El FakhriYale Biomedical Imaging Institute and Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Lin KongDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China.
Xicai SunENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Yefeng ZhengMedical Artificial Intelligence Laboratory, Westlake University, Hangzhou, China.
Yuxuan ShiENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China. yuxuan_shi@fudan.edu.cn.
Hongmeng YuENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China. hongmengyush@fudan.edu.cn.
Xiaofeng LiuYale Biomedical Imaging Institute and Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA. xiaofeng.liu@yale.edu.

Funding

National Natural Science Foundation of China 82301276National Natural Science Foundation of China 82371123National Natural Science Foundation of China 82501372Shanghai Sailing Program 23YF140480Shanghai Sailing Program 24YF2745500Shanghai Science and Technology Committee Foundation 23Y31900500the New Technologies of Endoscopic Surgery in Skull Base Tumor: CAMS Innovation Fund for Medical Sciences CIFMS, 2019-I2M-5-003
6 · The paper itself

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.

Identifiers

PMID41430426
PMCPMC12748797

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.