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By utilizing high-resolution primary tumor whole slide images (WSIs), the authors extract interpretable cellular and tissue-level features, including cell morphology, texture, and collagen organization. The study demonstrates that these features effectively stratify patient risk, yielding performance metrics competitive with state-of-the-art models like CLAM. Furthermore, the analysis identifies a significant dose-dependent relationship between ulceration scores and survival outcomes. Validated against a cohort from the Melanoma Institute of Australia, the model offers a promising tool for clinical decision-making, enabling more precise risk stratification, tailored follow-up care, and optimized enrollment in oncology research studies. The research highlights the critical importance of sub-staging patients categorized as IUSM IIA and IIB, particularly for those presenting with bulky tumors in superficial spreading or nodular melanoma subtypes.",{"@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/constructing-machine-learning-models-with-interpretable-cellular-and-tissue-level-features-for-prognosing-early-stage-cutaneous-melanoma-from-primary-tumor-whole-slide-images/291088/",{"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/constructing-machine-learning-models-with-interpretable-cellular-and-tissue-level-features-for-prognosing-early-stage-cutaneous-melanoma-from-primary-tumor-whole-slide-images/291088.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the primary objective of this research?","Question",{"text":112,"@type":113},"The research aims to develop and validate an interpretable machine-learning tool that utilizes histopathological features from primary melanoma tumors to predict patient progression-free survival.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed model perform compared to existing methods?",{"text":117,"@type":113},"The research findings indicate that the proposed method performs similarly to the state-of-the-art deep-learning model known as CLAM.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the clinical significance of the ulceration score developed in this study?",{"text":121,"@type":113},"The study demonstrates that the novel ulceration score has a significant dose-dependent relationship with progression-free survival, aiding in the stratification of patient risk.","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},291088,1789643421,{"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":66,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":138,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":129,"read_time":81,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","cbCaimNuLoW9984f","https://ap.wps.com/l/cbCaimNuLoW9984f","pdf",480564,"English","# Abstract\n# Introduction\n# Materials & Methods\n## Study Cohorts\n## Pipeline\n# Results\n# Conclusions","[{\"question\":\"What is the primary objective of this research?\",\"answer\":\"The research aims to develop and validate an interpretable machine-learning tool that utilizes histopathological features from primary melanoma tumors to predict patient progression-free survival.\"},{\"question\":\"How does the proposed model perform compared to existing methods?\",\"answer\":\"The research findings indicate that the proposed method performs similarly to the state-of-the-art deep-learning model known as CLAM.\"},{\"question\":\"What is the clinical significance of the ulceration score developed in this study?\",\"answer\":\"The study demonstrates that the novel ulceration score has a significant dose-dependent relationship with progression-free survival, aiding in the stratification of patient risk.\"}]","Constructing machine learning models with interpretable cellular and tissue level features for prognosing early-stage cutaneous melanoma from primary tumor whole slide images | PDF"]