[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126055-en":3,"doc-seo-126055-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":20,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126055,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning endometrial cancer risk prediction model - integrating guidelines of European Society for Medical Oncology with the tumor immune framework","Current prognostic factors for endometrial cancer do not reliably predict recurrence in early stages, even when patients are classified as low risk under the ESMO-ESGO-ESTRO consensus framework and its biomolecular POLE, TP53, and microsatellite instability components. Immune context integration has remained insufficiently assessed. This study develops a machine-learning model using public biomolecular data, estimating immune-cell abundances and selecting features to predict disease-free survival and relapse risk.","Original research  \nMachine learning endometrial cancer risk prediction model: integrating guidelines of European Society for Medical Oncology with the tumor immune framework  \nValentina Bruno,1 Martina Betti  ,2 Lorenzo D’Ambrosio  ,3 Alice Massacci,3 Benito Chiofalo,1 Adalgisa Pietropolli,4 Giulia Piaggio,3 Gennaro Ciliberto,3 Paola Nisticò,3 Matteo Pallocca,3 Alessandro Buda  ,5 Enrico Vizza1  \n► Additional supplemental material is published online only. To view, please visit the journal online ([http://dx.doi.org/](http://dx.doi.org/)[ ](http://dx.doi.org/)[10.1136/ijgc-2023-004671](10.1136/ijgc-2023-004671)) .  \nFor numbered affiliations see end of article.  \nCorrespondence to  \nDr Valentina Bruno, Department of Experimental Clinical Oncology, IRCCS Regina Elena National Cancer Institute, Rome 00144, Italy; valentina. bruno@ [ifo.it](ifo.it)  \nVB, MB and LD’A contributed equally.  \nMP, AB and EV contributed equally.  \nReceived 25 May 2023 Accepted 31 August 2023 Published Online First  \n24 October 2023  \n© IGCS and ESGO 2023. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ.  \nTo cite: Bruno V, Betti M, D’Ambrosio L, et al.  \nInt J Gynecol Cancer 2023;33:1708–1714 .  \nABSTRACT  \nObjective Current prognostic factors for endometrial cancer are not sufficient to predict recurrence in early stages. Treatment choices are based on the prognostic factors included in the risk classes defined by the ESMO-ESGOESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European Society for Radiotherapy and Oncology) consensus conference with the new biomolecular classification based on POLE, TP53, and microsatellite instability status. However, a minority of early stage cases relapse regardless of their low risk profiles. Integration of the immune context status to existing molecular based models has not been fully evaluated. This study aims to investigate whether the integration of the immune landscape in the tumor microenvironment could improve clinical risk prediction models and allow better profiling of early stages. Methods Leveraging the potential of in silico deconvolution tools, we estimated the relative abundances of immune populations in public data and then applied feature selection methods to generate a machine learning based model for disease free survival probability prediction.  \nResults We included information on International Federation of Gynecology and Obstetrics (FIGO) stage, tumor mutational burden, microsatellite instability, POLEmut status, interferon γ signature, and relative abundances of monocytes, natural killer cells, and CD4+T cells to build a relapse prediction model and obtained a balanced accuracy of 69%. We further identified two novel early stage profiles that undergo different pathways of recurrence.  \nConclusion This study presents an extension of current prognostic factors for endometrial cancer by exploiting machine learning models and deconvolution techniques on available public biomolecular data. Prospective clinical trials are advisable to validate the early stage stratification.  \nINTRODUCTION  \nEndometrial cancer is the sixth most common cancer in women and, although the mortality rate decreased in the past few decades, its incidence and prevalence rates are increasing worldwide.1 Treatments for endometrial cancer are related to well known prognostic factors included in the risk classes defined by the ESMO-ESGOESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European Society  \n\n| WHAT IS ALREADY KNOWN ON THIS TOPIC |\n| --- |\n| ⇒ Prognostic factors defined by the ESMOESGO-ESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European Society for Radiotherapy and Oncology) consensus conference together with biomolecular classification are currently applied tostratify patients into risk classes and in turn to assign the correct adjuvant treatment.\u003Cbr>⇒ However, known p","cbCail8S0yoLOeYe","https://ap.wps.com/l/cbCail8S0yoLOeYe","pdf",3434826,7,1,"English","en",105,"# Abstract\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Background and clinical need\n## Immune signatures and integration gap\n# What is already known on this topic\n# What this study adds\n# How this study might affect research, practice or policy","[{\"question\":\"Why are current prognostic factors insufficient for early-stage endometrial cancer?\",\"answer\":\"They cannot fully identify patients who will experience recurrence, and some early-stage cases relapse despite low-risk profiles defined by existing molecular risk classes.\"},{\"question\":\"How does the study build its machine learning risk prediction model?\",\"answer\":\"It estimates immune population abundances from public datasets using in silico deconvolution, then applies feature selection to train a model that predicts disease-free survival probability.\"},{\"question\":\"What inputs are used to generate the relapse prediction model?\",\"answer\":\"The model incorporates FIGO stage, tumor mutational burden, microsatellite instability, POLEmut status, interferon-gamma signature, and relative abundances of monocytes, natural killer cells, and CD4+ T cells.\"}]","Machine learning endometrial cancer risk prediction model - 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