Evidence map›Paper›PMID 41942868›Full record

ArticleBMC medical imaging2026

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.

Lei Lai, Chen Chen, Yahan Zhou, Vicky Yang Wang, Mengjiao Zhu, Zhiyan Jin, Yan Wu, Chenxia Ma, Qi Zhang, Qi Chen and 1 more

Abstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 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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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

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Lei Lai *Department of Health Management, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310022, China.
Chen Chen *Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
Yahan Zhou *Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
Vicky Yang WangWenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China.
Mengjiao ZhuDepartment of Ultrasound Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310022, China.
Zhiyan JinDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
Yan WuDepartment of Ultrasound Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310022, China.
Chenxia MaDepartment of Health Management, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310022, China.
Qi ZhangDepartment of Ultrasound Medicine, Shaoxing People's Hospital, Shaoxing, 312000, China.
Qi ChenDepartment 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.
Dong XuDepartment 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the second deadly cancer affecting women globally, precise and timely classification of ovarian tumors plays an instrumental role in improving the rate of curing and reducing the rate of mortality. This study was set out to comprehensively investigate the effectiveness of deep learning model for classifying benign and malignant ovarian tumors, utilizing multimodal ultrasound images and clinical data, in comparison to traditional methods such as manual assessment by radiologists and those based on O-RADS.

methodsThis retrospective multicenter study recruited women diagnosed with ovarian tumors between January 2022 and June 2023, with histopathological examination results as the reference diagnoses. The dataset was divided into three subsets: training (70%), validation (10%), and test (20%). Employing the Dense Convolutional Network algorithm, we constructed and investigated two fusion models: DL

resultsA total of 508 patients with ovarian tumors (mean age: 44.3 ± 15.9 years) were enrolled, including 327 benign and 181 malignant tumors. In the test set, the DL

conclusionThe deep learning model integrating multimodal ultrasound images and clinical information is capable of discriminating between benign and malignant ovarian tumors, exceeding the diagnostic capabilities of both radiologists and O-RADS assessments.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedOvarian NeoplasmsAdultAgedFemaleHumansMiddle AgedMultimodal ImagingRetrospective StudiesUltrasonographyDeep learningO-RADSOvarian tumorsUltrasonography

Identifiers

PMID41942868
PMCPMC13188482

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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.