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

BMC Medicine

Research Article

Haoyan Zhang¹  Kun Wang¹  Xin Yang¹  Wen Chen²  Yang Sun²  Qiang Fu³  Yuxuan Lin⁴  Han Zhang⁵  Bin Long⁶  Ligang Cui⁶  Rui Tang⁶ 

[1] CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China;School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China;Department of Ultrasound, Beijing Civil Aviation General Hospital, Beijing, China;Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China;Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, China;Institute of Medical Technology, Peking University Health Science Center, 100191, Beijing, China;Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China;

关键词: Superficial soft-tissue masses;    Deep learning model;    Ultrasound;    Diagnosis;    Computer-assisted diagnosis;   

DOI  :  10.1186/s12916-023-03099-9

 received in 2023-04-19, accepted in 2023-09-29,  发布年份 2023

来源: Springer

【 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

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