Investigation of multimodal deep learning models for predicting ovarian tumor malignancy based on ultrasound images and clinical information - a comprehensive comparative study against readers and O-RADS.

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Title: Investigation of multimodal deep learning models for predicting ovarian tumor malignancy based on ultrasound images and clinical information - a comprehensive comparative study against readers and O-RADS.
Authors: Lai L; Department of Health Management, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310022, China., Chen C; Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.; Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310018, China.; Zhejiang Provincial Research Center for Innovative Technology and Equipment in Interventional Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China., Zhou Y; Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.; Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310018, China.; Zhejiang Provincial Research Center for Innovative Technology and Equipment in Interventional Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China., Wang VY; Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China., Zhu M; Department of Ultrasound Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310022, China., Jin Z; Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China., Wu Y; Department of Ultrasound Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310022, China., Ma C; Department of Health Management, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310022, China., Zhang Q; Department of Ultrasound Medicine, Shaoxing People's Hospital, Shaoxing, 312000, China., Chen Q; Department of Ultrasound Medicine, Xianju People's Hospital, Zhejiang Southeast Campus of Zhejiang Provincial People's Hospital, Affiliated Xianju's Hospital, Hangzhou Medical College, Xianju, 317300, China., Xu D; Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China. xudong@zjcc.org.cn.; Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310018, China. xudong@zjcc.org.cn.; Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China. xudong@zjcc.org.cn.; Zhejiang Provincial Research Center for Innovative Technology and Equipment in Interventional Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China. xudong@zjcc.org.cn.
Source: BMC medical imaging [BMC Med Imaging] 2026 Apr 06; Vol. 26 (1). Date of Electronic Publication: 2026 Apr 06.
Publication Type: Journal Article; Comparative Study; Multicenter Study
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 100968553 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2342 (Electronic) Linking ISSN: 14712342 NLM ISO Abbreviation: BMC Med Imaging Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1471-2342
DOI:10.1186/s12880-026-02312-4