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RareNet achieves an overall accuracy (F1) of about 96%, outperforming Random Forest, K Nearest Neighbors, Decision Tree, and Support Vector models, while addressing common diagnostic delays and errors.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/rarenet-a-deep-learning-model-for-rare-cancer-diagnosis/366007/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/rarenet-a-deep-learning-model-for-rare-cancer-diagnosis/366007.png","ImageObject",300,407,{"name":42,"@type":43},"Bintang","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-27","2026-09-23",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does RareNet address in rare cancer diagnosis?","Question",{"text":63,"@type":64},"Rare cancers are difficult to diagnose early because they are uncommon and associated with limited data and expertise, leading to late or incorrect diagnoses.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does RareNet use transfer learning?",{"text":68,"@type":64},"RareNet transfers learned features from the established CancerNet model and fine-tunes a robust classifier using DNA methylation data from rare cancer biopsies.",{"name":70,"@type":61,"acceptedAnswer":71},"How well does RareNet perform compared with other models?",{"text":72,"@type":64},"RareNet reaches an overall accuracy around 96% (F1 score) and outperforms Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},366007,1791091675,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":110,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":11,"faqs":140,"seo_title":141,"seo_description":12,"update_tm":142,"read_time":117},962085564381,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nRareNet: a deep learning model for rare cancer diagnosis  \nDanyang Shao1, Sohan Addagudi2, Joseph Cowles3, Arnav Jain4, Lauren D’Souza4, Steven Gore1 & Rajeev K. Azad1􀀍  \nAlthough significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leukemia, perhaps due to their relative obscurity and scarce data compared to common cancers. Application of artificial intelligence or deep learning has shown promising results in disease diagnosis including in their ability to diagnose cancers and detect their tissue of origin. However, their ability to detect rare cancers is yet to be comprehensively assessed. This motivated us to develop, RareNet, which leverages transfer learning of an established deep learning model, namely, CancerNet, to classify rare cancers. The transfer learning framework of RareNet utilized DNA methylation data of various biopsied rare cancers to learn epigenetic signatures of rare cancers. RareNet achieved an overall accuracy (F1 score) of ~ 96%, outperforming other machine learning models including Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier.  \nRare cancers are generally defined as cancers that occur in less than 6 people per 100,000 individuals1. Despite each rare cancer affecting only a small portion of the population, cancers that fall under the umbrella of rare cancers actually make up approximately 22% of cancer diagnoses2. Notably, patients with these cancers often face worse outcomes. The five-year relative survival of rare cancers is a dismal 47%, compared to 65% for common  \n3  \ncancers .  \nOne potential reason for the low survival rate of rare cancer patients is often an incorrect or late diagnosis. Current diagnostic measures typically are based on histopathology. Some of these techniques include staining with haematoxylin and eosin, immunohistochemistry, conventional karyotyping, and fluorescence in-situ hybridization (FISH) . However, the diagnosis through these techniques is subject to interpretational error ata rate of approximately 4%4. This discrepancy is even more pronounced in rare cancers. Soft cell sarcomas are an umbrella classification that contains over fifty different subtypes of tumors– all of which are considered rare tumors5. When initial histological diagnoses of sarcoma patients were compared to those from a panel of experts, the diagnoses differed approximately 42% of all instances6. Furthermore, as histological diagnosis requires tissue samples of a tumor, cancer patients have to be subjected to an invasive procedure to obtain these samples. These samples cannot even be collected if the tumor is not ofa noticeable size yet. Both of these factors can delay diagnosis and treatment of the cancer, resulting in worse patient outcomes7.  \nWhile histopathology is still by far the most common diagnostic method, Whole Genome Sequencing (WGS) has begun to gain more attraction in recent years. WGS is a process in which entire DNA complement within a cell can be sequenced, including the tumor DNA. It remains a promising way to identify and classify cancers; however, large scale sequencing could be cost prohibitive. DNA molecules harbored by cancerous tissues display methylation patterns that distinct from those of normal tissues and furthermore, methylation patterns differ among different cancers. Quantification of DNA methylation through techniques such as, Whole Genome Bisulfite Sequencing (WGBS), is thus a promising alternative that could be leveraged to diagnose and classify cancers8.  \nIndeed, the DNA methylation patterns in cancers were exploited in several studies recently to classify cancers. One such diagnostic tool, CancerNet9, utilizes a deep learning framework with a variational a","cbCaikWjewREh99d","https://ap.wps.com/l/cbCaikWjewREh99d","pdf",2182712,"English","[{\"question\":\"What problem does RareNet address in rare cancer diagnosis?\",\"answer\":\"Rare cancers are difficult to diagnose early because they are uncommon and associated with limited data and expertise, leading to late or incorrect diagnoses.\"},{\"question\":\"How does RareNet use transfer learning?\",\"answer\":\"RareNet transfers learned features from the established CancerNet model and fine-tunes a robust classifier using DNA methylation data from rare cancer biopsies.\"},{\"question\":\"How well does RareNet perform compared with other models?\",\"answer\":\"RareNet reaches an overall accuracy around 96% (F1 score) and outperforms Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier.\"}]","RareNet - a deep learning model for rare cancer diagnosis | PDF",1790163531]