Integrating Ultrasound-CT-MR for Preoperative Multi-Task Prediction in Ovarian Cancer: Achieving Diagnostic Parity with Multidisciplinary Team Consensus

Integrating Ultrasound-CT-MR for Preoperative Multi-Task Prediction in Ovarian Cancer: Achieving Diagnostic Parity with Multidisciplinary Team Consensus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Ultrasound-CT-MR for Preoperative Multi-Task Prediction in Ovarian Cancer: Achieving Diagnostic Parity with Multidisciplinary Team Consensus Jingjing Yu, Peijun Hu, Ruixia Dai, Xiaomin Liu, Shanshan Zhang, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6334307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Ovarian cancer, with its high mortality rate, requires precise preoperative integration of ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) for guide therapy, yet current clinical workflows rely on specialized multidisciplinary team (MDT) evaluations, a barrier in resource-limited settings. Current artificial intelligence (AI)-driven studies, primarily focused on single-modality analysis and distinguishing malignant from benign ovarian lesions, fail to address the complexity of multimodal decision-making. Here, we developed OVUCM, a multi-modality AI system integrating Ultrasound/CT/MRI via intermediate fusion, using radiomics and machine learning on a retrospective cohort of 1,742 patients from National Regional Medical Center for Obstetrics and Gynecology. The OVUCM system simultaneously predicts five clinical endpoints: benign vs. non-benign (AUC 0.929), borderline vs. malignant (0.889), non-epithelial vs. epithelial (0.897), International Federation of Gynecology and Obstetrics (FIGO) staging I-II vs. III-IV (0.853), and non-high-grade serous ovarian cancer (HGSOC) vs. HGSOC (0.847). This system achieved diagnostic parity with MDT consensus (ΔAUC 0-0.25; Delong test, P > 0.05 in five tasks), while outperforming independent gynecologists in some tasks. External validation in 102 patients from a general hospital confirmed its generalizability (AUCs: 0.899–0.974 in five tasks). This multi-task and multi-modality system standardizes preoperative workflows by bridging the gap between single-modality tools and the comprehensive, multidisciplinary decision-making required for personalized therapy. Its ability to replicate MDT expertise in resource-limited settings positions it as a transformative tool for global health equity. Biological sciences/Cancer/Cancer imaging Biological sciences/Cancer/Gynaecological cancer/Ovarian cancer Ovarian Cancer Predictive Model Multi-modality fusion Multi-task Ultrasound computed tomography Magnetic resonance imaging Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 31 Mar, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 29 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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