{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:12:58Z","timestamp":1779203578662,"version":"3.51.4"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment decisions and predicts patients\u2019 outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the prognostic ability of the eighth edition TNM (TNM-8) staging system to predict overall (OS) and melanoma-specific survival (MSS) in cutaneous malignant melanoma (CMM), and to explore the potential of machine learning (ML) methods to enhance melanoma prognostication.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The records of adult patients diagnosed with CMM from 2018 to 2022 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program database. TNM-8 was evaluated using Kaplan\u2013Meier plots and OS and MSS probabilities. Fine\u2013Gray competing risk and accelerated failure time (AFT) models evaluated factors affecting MSS and OS, respectively. ML models were used to evaluate the predictive ability of TNM stages for OS and MSS, compared with that of multiple clinically important prognostic variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Altogether, 111 871 patients with CMM were included in the analysis, with OS and MSS as survival endpoints. Most T (tumour), N\u00a0(node), M (metastasis) and TNM-8 stage categories had distinct MSS and OS probabilities, except in the N1 and N2 stages at 12 months and in OS for TNM-8 stages 2 and 3 at 12, 36, 48 and 59 months. Fine\u2013Gray and AFT models showed that age, sex, race\/origin, tumour site, histological type, ulceration, Breslow thickness, mitotic rate, number of positive lymph nodes and M stage were important prognostic variables for MSS and OS. ML models showed that TNM had a higher predictive prognostic ability for MSS than OS and that including clinically important prognostic variables in addition to TNM stage has higher discriminative prognostic ability for OS and MSS (testing C-indices range 0.84\u20130.85 and 0.89\u20130.92, respectively) than using TNM staging alone (testing C-indices range 0.67\u20130.72 and 0.82\u20130.87, respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This proof-of-concept study demonstrated that although TNM staging retains prognostic value, adding important prognostic variables and using ML could improve prognostication for patients with melanoma. ML could be used to develop interactive clinical decision-support tools to improve the prognostication of individuals with melanoma.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjd\/ljag018","type":"journal-article","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T12:30:58Z","timestamp":1768134658000},"page":"1087-1096","source":"Crossref","is-referenced-by-count":1,"title":["Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients"],"prefix":"10.1093","volume":"194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-2763","authenticated-orcid":false,"given":"Mohamed","family":"Mortagy","sequence":"first","affiliation":[{"name":"Hampshire Hospitals NHS Foundation Trust , Winchester ,","place":["UK"]},{"name":"St. George\u2019s University School of Medicine , St. George\u2019s ,","place":["Grenada"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6857-6493","authenticated-orcid":false,"given":"Nikita","family":"Cliff-Patel","sequence":"additional","affiliation":[{"name":"Hampshire Hospitals NHS Foundation Trust , Winchester ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3508-2480","authenticated-orcid":false,"given":"Regina","family":"Askary","sequence":"additional","affiliation":[{"name":"Hampshire Hospitals NHS Foundation Trust , Winchester ,","place":["UK"]},{"name":"Concord Repatriation General Hospital , Sydney ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7168-5834","authenticated-orcid":false,"given":"Ana-Maria","family":"Bologan","sequence":"additional","affiliation":[{"name":"Hampshire Hospitals NHS Foundation Trust , Winchester ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9711-4164","authenticated-orcid":false,"given":"Aya","family":"Abdelhameed","sequence":"additional","affiliation":[{"name":"Hampshire Hospitals NHS Foundation Trust , Winchester ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6976-1068","authenticated-orcid":false,"given":"Dan","family":"Burns","sequence":"additional","affiliation":[{"name":"Hampshire Hospitals NHS Foundation Trust , Winchester ,","place":["UK"]},{"name":"School of Electronics and Computer Science, University of Southampton , Southampton ,","place":["UK"]},{"name":"NIHR Applied Research Collaboration Wessex, University of Southampton , Southampton 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