[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126290-en":3,"doc-seo-126290-105":31,"detail-sidebar-cat-0-en-105":93},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126290,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Machine learning to predict overall short-term mortality in cutaneous melanoma","Cutaneous malignant melanoma is among the most frequent cancers, and clinicopathological staging is central for reliable prognosis. Artificial intelligence has been increasingly used to build prognostic staging systems, and this study develops a machine learning–based approach to estimate overall short-term survival in CMM patients. Data from the Veneto Cancer Registry and regional health service were modeled with Cox regression and multiple ML architectures, validated via cross-validation and tested on an independent set, then deployed as an online tool.","Research  \nMachine learning to predict overall short‑term mortality in cutaneous melanoma  \nC. Cozzolino1 · A. Buja2 · M. Rugge3,4 · A. Miatton2 · M. Zorzi3 · A. Vecchiato1 · P. Del Fiore1 · S. Tropea1 · A. Brazzale5 ·  \nG. Damiani6 · L. dall’Olmo1,7 · C. R. Rossi7 · S. Mocellin1,7  \nReceived: 3 November 2022 / Accepted: 19 January 2023  \n© The Author(s) 2023 OPEN  \nAbstract  \nBackground Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival.  \nMethods CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool.  \nResults The univariate analysis confirmed the significant prognostic value ofTNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction.  \nConclusions Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented ( [unipd.link/melanomaprediction](unipd.link/melanomaprediction)). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model’s accuracy beyond the original research context.  \nKeywords Artificial intelligence · Machine learning · Melanoma · Oncology · Mortality · Predictors  \n* C. Cozzolino, [claudia.cozzolino@iov.veneto.it |](claudia.cozzolino@iov.veneto.it |1Soft-Tissue)[1](claudia.cozzolino@iov.veneto.it |1Soft-Tissue)[Soft-Tissue](claudia.cozzolino@iov.veneto.it |1Soft-Tissue), Peritoneum and Melanoma Surgical Oncology Unit, Veneto Institute of Oncology IOV-IRCCS, Via Gattamelata, 64, 35128 Padua, PD, Italy. 2Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Padua, Italy. 3Veneto Tumor Registry (RTV), Azienda Zero, Padua, Italy. 4Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy. 5Department of Statistical Sciences, University of Padua, Padua, Italy. 6Clinical Dermatology, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy. 7Department of Surgery, Oncology and GastroenterologyDISCOG, University of Padua, Padua, Italy.  \nDiscover Oncology  \n(2023) 14:13  \n| [https://doi.org/10.1007/s12672-023-00622-5](https://doi.org/10.1007/s12672-023-00622-5)  \n1 Introduction  \nCutaneous malignant melanoma (CMM) is one of the deadliest skin cancers due to its intrinsic biological aggressiveness and relatively high probability of misdiagnosis [1, 2] . Melanoma a","cbCaij31ckuosrKO","https://ap.wps.com/l/cbCaij31ckuosrKO","pdf",1520456,5,1,14,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# 1 Introduction","[{\"question\":\"What is the main goal of the study on cutaneous melanoma?\",\"answer\":\"To develop a machine learning tool that predicts overall short-term survival (3-years mortality probability) for patients with cutaneous malignant melanoma.\"},{\"question\":\"Which data sources and modeling approaches were used?\",\"answer\":\"Patient records were taken from the Veneto Cancer Registry and the Veneto regional health service. The study used univariate Cox regression and trained multiple machine learning models, including a deep neural network.\"},{\"question\":\"Which variables and models showed the strongest predictive performance?\",\"answer\":\"TNM staging remained a significant prognostic factor. Additional variables such as age, T and M stages, mitotic count, and ulceration contributed strongly, while neural network and random forest achieved the best balanced accuracy.\"}]","Machine learning to predict overall short-term mortality in cutaneous melanoma | PDF",1785904286,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-to-predict-overall-short-term-mortality-in-cutaneous-melanoma","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-to-predict-overall-short-term-mortality-in-cutaneous-melanoma/126290/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of the study on cutaneous melanoma?","Question",{"text":77,"@type":78},"To develop a machine learning tool that predicts overall short-term survival (3-years mortality probability) for patients with cutaneous malignant melanoma.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which data sources and modeling approaches were used?",{"text":82,"@type":78},"Patient records were taken from the Veneto Cancer Registry and the Veneto regional health service. The study used univariate Cox regression and trained multiple machine learning models, including a deep neural network.",{"name":84,"@type":75,"acceptedAnswer":85},"Which variables and models showed the strongest predictive performance?",{"text":86,"@type":78},"TNM staging remained a significant prognostic factor. Additional variables such as age, T and M stages, mitotic count, and ulceration contributed strongly, while neural network and random forest achieved the best balanced accuracy.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]