Evidence map›Paper›PMID 41831029›Full record

ArticleEuropean radiology2026

A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.

Ran Cao, Yangyang Zhu, Haina Zhao, Zhibin Zhu, Lin Chen, Yue Hu, Fei Ouyang, Nannan Zhao, Tao Jiang, Yifang Li and 5 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Ran CaoCollege of Biomedical Engineering, Fudan University, Shanghai, China.
Yangyang ZhuUltrasound Medical Center, The Second Hospital of Lanzhou University, Lanzhou, China.
Haina ZhaoDepartment of Ultrasound, West China Hospital, Sichuan University, Chengdu, China.
Zhibin ZhuCollege of Biomedical Engineering, Fudan University, Shanghai, China.
Lin ChenDepartment of Ultrasound, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Yue HuDepartments of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China.
Fei OuyangDepartment of Ultrasound Medicine, The First People's Hospital of Chenzhou, Hunan, China.
Nannan ZhaoDepartment of Radiology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang, China.
Tao JiangCollege of Biomedical Engineering, Fudan University, Shanghai, China.
Yifang LiCollege of Biomedical Engineering, Fudan University, Shanghai, China.
Wenyu XingSchool of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, China.
Juan SongDepartment of Ultrasound, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China.
Fang NieUltrasound Medical Center, The Second Hospital of Lanzhou University, Lanzhou, China. ery_nief@lzu.edu.cn.
Li QiuDepartment of Ultrasound, West China Hospital, Sichuan University, Chengdu, China. qiulihx@scu.edu.cn.
Dean TaCollege of Biomedical Engineering, Fudan University, Shanghai, China. tda@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study developed and validated a deep learning model for diagnosing lymphadenopathy (LA) using B-mode ultrasound (BUS) and color Doppler flow imaging (CDFI) videos. MATERIALS AND

methodsA retrospective and prospective study was conducted from January 2016 to August 2025, including 7371 patients (3824 male [51.9%], 3547 females [48.1%], median age, 52 years [9-94 years]) who underwent multimodal ultrasound examinations across six centers from five regions of China. A total of 147,420 key frames were extracted from BUS and CDFI videos of all patients for model training and validation. Besides, patient clinical information was integrated to enhance the diagnostic performance of the model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE). The clinical practical value of the model was verified by comparing with the performance of independent diagnosis and model-assisted diagnosis of radiologists with different levels of experience.

resultsThis model achieved AUCs of 0.956 (95% CI: 0.925-0.981), 0.928 (95% CI: 0.884-0.965), and 0.912 (95% CI: 0.863-0.952) in the internal, retrospective external, and prospective external validation cohorts, respectively. In the retrospective external cohort, the average AUC of junior radiologists improved from 0.739 (95% CI: 0.676-0.801) to 0.891 (95% CI: 0.846-0.940) with the assistance of the model. In the prospective external cohort, their average AUC improved from 0.767 (95% CI: 0.705-0.829) to 0.899 (95% CI: 0.853-0.944).

conclusionThis multimodal video-based deep learning model enhances LA diagnostic accuracy and shows strong potential as a noninvasive, efficient tool for clinical decision-making. KEY POINTS: Question Why is multimodal ultrasound video needed in clinical practice to enhance the automated assessment of LA? Findings The proposed multimodal ultrasound video AI model achieved high diagnostic accuracy and robustness, outperforming senior radiologists in distinguishing benign from malignant LA across multicenter datasets. Clinical relevance This model offers a reliable, noninvasive clinical decision-support tool that enhances diagnostic performance, reduces operator dependence, and facilitates early detection and precise management of LA across healthcare institutions with varying resources.

Indexed as

Deep LearningLymphadenopathyMultimodal ImagingUltrasonography, Doppler, ColorAdolescentAdultAgedAged, 80 and overChildChinaFemaleHumansMaleMiddle AgedProspective StudiesRetrospective StudiesDeep learningLymphadenopathyMultimodalUltrasound video

Identifiers

PMID41831029

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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.