{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:11:49Z","timestamp":1783120309265,"version":"3.54.6"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities of various machine learning (ML) algorithms in predicting progression, treatment response, and relapse and to assess their predictive power against that of the Cox proportional hazards (CPH) model in patients with early-stage MF. The data of patients aged 18 years and over who were diagnosed with early-stage MF at Ankara University Faculty of Medicine Hospital from 2006 to 2024 were retrospectively reviewed. ML algorithms were utilized to predict complete response, relapse, and disease progression using patient data. Of the 185 patients, 94 (50.8%) were female, and 91 (49.2%) were male. Complete response was observed in 114 patients (61.6%), while relapse and progression occurred in 69 (37.3%) and 54 (29.2%) patients, respectively. For predicting progression, the Support Vector Machine (SVM) algorithm demonstrated the highest success rate, with an accuracy of 75%, outperforming the CPH model (C-index: 0.652 for SVM vs. 0.501 for CPH). The most successful model for predicting complete response was the Ensemble model, with an accuracy of 68.89%, surpassing the CPH model (C-index: 0.662 for the Ensemble model vs. 0.543 for CPH). For predicting relapse, the decision tree classifier showed the highest performance, with an accuracy of 78.17%, outperforming the CPH model (C-index: 0.782 for the decision tree classifier vs. 0.505 for CPH). The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients.<\/jats:p>","DOI":"10.3390\/life14111371","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T03:46:04Z","timestamp":1729827964000},"page":"1371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Banu","family":"\u0130smail Mendi","sequence":"first","affiliation":[{"name":"Department of Dermatology, Ni\u011fde \u00d6mer Halisdemir University Training and Research Hospital, Ni\u011fde 51000, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hatice","family":"\u015eanl\u0131","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Faculty of Medicine, Ankara University, Ankara 06620, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4347-1190","authenticated-orcid":false,"given":"Mert Ak\u0131n","family":"Insel","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Y\u0131ld\u0131z Technical University, \u0130stanbul 34220, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beliz","family":"Bay\u0131nd\u0131r Aydemir","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Faculty of Medicine, Ankara University, Ankara 06620, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Fatih","family":"Atak","sequence":"additional","affiliation":[{"name":"Department of Dermatology, New York Medical College, Valhalla, NY 10595, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.1182\/blood-2004-09-3502","article-title":"WHO-EORTC classification for cutaneous lymphomas","volume":"105","author":"Willemze","year":"2005","journal-title":"Blood"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1182\/blood-2007-03-055749","article-title":"Revisions to the staging and classification of mycosis fungoides and Sezary syndrome: A proposal of the International Society for Cutaneous Lymphomas (ISCL) and the cutaneous lymphoma task force of the European Organization of Research and Treatment of Cancer (EORTC)","volume":"110","author":"Olsen","year":"2007","journal-title":"Blood"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4730","DOI":"10.1200\/JCO.2009.27.7665","article-title":"Survival outcomes and prognostic factors in mycosis fungoides\/S\u00e9zary syndrome: Validation of the revised International Society for Cutaneous Lymphomas\/European Organisation for Research and Treatment of Cancer staging proposal","volume":"28","author":"Agar","year":"2010","journal-title":"J. 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