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Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study

文献类型：期刊论文

作者Long, Bin6,7; Zhang, Haoyan4,5; Zhang, Han3; Chen, Wen6; Sun, Yang6; Tang, Rui6,7; Lin, Yuxuan2; Fu, Qiang1; Yang, Xin4,5; Cui, Ligang6,7
刊名BMC MEDICINE
出版日期2023-10-26
卷号21期号:1页码:11
ISSN号1741-7015
关键词Superficial soft-tissue masses Deep learning model Ultrasound Diagnosis Computer-assisted diagnosis
DOI10.1186/s12916-023-03099-9
通讯作者Cui, Ligang(cuijuegang@126.com) ; Wang, Kun(kun.wang@ia.ac.cn)
英文摘要BackgroundMost of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses.MethodsBetween January 2015 and December 2022, data for 1615 patients with superficial soft-tissue masses were retrospectively collected. Two experienced radiologists (radiologists 1 and 2 with 8 and 30 years' experience, respectively) analyzed the ultrasound images of each superficial soft-tissue mass and made a diagnosis of malignant mass or one of the five most common benign masses. After referring to the DLM results, they re-evaluated the diagnoses. The diagnostic performance and concerns of the radiologists were analyzed before and after referring to the results of the DLM results.ResultsIn the validation cohort, DLM-1 was trained to distinguish between benign and malignant masses, with an AUC of 0.992 (95% CI: 0.980, 1.0) and an ACC of 0.987 (95% CI: 0.968, 1.0). DLM-2 was trained to classify the five most common benign masses (lipomyoma, hemangioma, neurinoma, epidermal cyst, and calcifying epithelioma) with AUCs of 0.986, 0.993, 0.944, 0.973, and 0.903, respectively. In addition, under the condition of the DLM-assisted diagnosis, the radiologists greatly improved their accuracy of differential diagnosis between benign and malignant tumors.ConclusionsThe proposed DLM system has high clinical application value in the differential diagnosis of superficial soft-tissue masses.
WOS关键词ULTRASOUND ; TUMORS ; CT
资助项目Not applicable.
WOS研究方向General & Internal Medicine
语种英语
出版者BMC
WOS记录号WOS:001086980200002
资助机构Not applicable.
源URL[http://ir.ia.ac.cn/handle/173211/54352]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Cui, Ligang; Wang, Kun
作者单位1.Beijing Civil Aviat Gen Hosp, Dept Ultrasound, Beijing, Peoples R China
2.Capital Med Univ, Beijing Friendship Hosp, Dept Ultrasound, Beijing, Peoples R China
3.Hebei Med Univ, Hosp 2, Dept Ultrasound, Shijiazhuang, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
6.Peking Univ Third Hosp, Dept Diagnost Ultrasound, Beijing, Peoples R China
7.Peking Univ Hlth Sci Ctr, Inst Med Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Long, Bin,Zhang, Haoyan,Zhang, Han,et al. Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study[J]. BMC MEDICINE,2023,21(1):11.
APA Long, Bin.,Zhang, Haoyan.,Zhang, Han.,Chen, Wen.,Sun, Yang.,...&Wang, Kun.(2023).Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study.BMC MEDICINE,21(1),11.
MLA Long, Bin,et al."Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study".BMC MEDICINE 21.1(2023):11.

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来源：自动化研究所

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