[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-342655-105":59,"doc-detail-342655-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","development-and-validation-of-an-interpretable-model-integrating-multimodal-information-for-improving-ovarian-cancer-diagnosis-paper-summary","Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis - Paper Summary","","Development of OvcaFinder targets the need for accurate, early, and consistent ovarian cancer diagnosis. The interpretable model integrates ultrasound image–based deep learning predictions, radiologists’ Ovarian–Adnexal Reporting and Data System (O-RADS) scores, and routine clinical variables. In internal and external test datasets, OvcaFinder improves diagnostic performance versus both a clinical model and a deep learning model, with higher AUC values and reduced false positive rates. Radiologists’ performance and inter-reader agreement improve with assistance, supporting better diagnostic accuracy and consistency.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-and-validation-of-an-interpretable-model-integrating-multimodal-information-for-improving-ovarian-cancer-diagnosis-paper-summary/342655/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-and-validation-of-an-interpretable-model-integrating-multimodal-information-for-improving-ovarian-cancer-diagnosis-paper-summary/342655.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is OvcaFinder designed to improve in ovarian cancer diagnosis?","Question",{"text":112,"@type":113},"OvcaFinder is designed to improve diagnostic accuracy and consistency for identifying ovarian cancer by combining multiple information sources in an interpretable framework.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What inputs does OvcaFinder integrate?",{"text":117,"@type":113},"It integrates ultrasound image–based deep learning predictions, O-RADS scores provided by radiologists, and routine clinical variables.",{"name":119,"@type":110,"acceptedAnswer":120},"How does OvcaFinder perform compared with other models?",{"text":121,"@type":113},"OvcaFinder outperforms both the clinical model and the deep learning model, showing higher AUCs on internal and external test datasets and lower false positive rates.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},342655,1790198014,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},16904993612988,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Article [https://doi.org/10.1038/s41467-024-46700-2](https://doi.org/10.1038/s41467-024-46700-2)  \nDevelopment and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis  \nReceived: 16 August 2023  \n\n| Accepted: 5 March 2024 |\n| --- |\n| |\n| Check for updates |\n\nHuiling Xiang 1,2,11, Yongjie Xiao3,11, Fang Li4,5,11, Chunyan Li2, Lixian Liu6, Tingting Deng2, Cuiju Yan2, Fengtao Zhou7, Xi Wang8,9, Jinjing Ou2, Qingguang Lin2, Ruixia Hong4,5, Lishu Huang4,5, Luyang Luo7, Huangjing Lin3,9, Xi Lin2  & Hao Chen 7,10   \nOvarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is ofgreat significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian–Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.  \nOvarian cancer remains the most lethal gynecological cancer and accounted for approximately 14,070 cancer-related deaths and 22,240 new cases of cancer in the United States in 20181. About 58% of ovarian cancers are initially diagnosed as metastatic ovarian cancers, which have a 5-year survival rate of only30%, compared with a survival rate of  \n93% for localised cancers2. An accurate diagnostic method for the early diagnosis of ovarian cancer improves therapeutic outcomes by enabling early intervention. Patients with ovarian cancer who refer to gynaecology oncology centre for debulking surgery and systemic therapies have longer survival compared to those managed in  \n1Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China. 2Department of Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China. 3AI Research Lab, Imsight Technology Co., Ltd., Nanshan, Shenzhen 518000, China. 4Department of Ultrasound, Chongqing University Cancer Hospital, Chongqing, China. 5Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer(iCQBC), Chongqing University Cancer Hospital, Chongqing400030, China. 6Department of Ultrasound, Guangdong Second Provincial General Hospital, No. 466, Xingang Middle Road, Haizhu District, Guangzhou, Guangdong, China. 7Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. 8Zhejiang Lab, Hangzhou, China. 9Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China. 10Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. 11These authors contributed equally: Huiling Xiang, Yongjie Xiao, Fang Li.  \n e-mail: [linxi@sysucc.org.cn](linxi@sysucc.org.cn); [jhc@cse.ust.hk](jhc@cse.ust.hk)  \ncommunity or general hospitals3. For patients with lesions of benign ultrasound morphology, the 2-year cumulative incidence of major complications, including invasive malignancy, torsion, and cyst rupture, was less than 0.5%, which can be followed up to prevent unnecessary surgeries as well as associat","cbCaiqWpR6cIIWB5","https://ap.wps.com/l/cbCaiqWpR6cIIWB5","pdf",5381709,12,"English","# Introduction\n## Clinical need for early ovarian cancer diagnosis\n## Imaging background: TVUS and O-RADS\n## Prior deep-learning approaches and gaps\n# Method Overview\n## Model components and interpretability\n# Results\n## Performance on internal and external test datasets\n## Impact on radiologists and inter-reader agreement\n# Discussion\n## Potential to improve diagnostic accuracy and consistency","[{\"question\":\"What is OvcaFinder designed to improve in ovarian cancer diagnosis?\",\"answer\":\"OvcaFinder is designed to improve diagnostic accuracy and consistency for identifying ovarian cancer by combining multiple information sources in an interpretable framework.\"},{\"question\":\"What inputs does OvcaFinder integrate?\",\"answer\":\"It integrates ultrasound image–based deep learning predictions, O-RADS scores provided by radiologists, and routine clinical variables.\"},{\"question\":\"How does OvcaFinder perform compared with other models?\",\"answer\":\"OvcaFinder outperforms both the clinical model and the deep learning model, showing higher AUCs on internal and external test datasets and lower false positive rates.\"}]","Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis - Paper Summary | PDF",1790047570]