{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:00:57Z","timestamp":1777726857621,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Science and Technology Program of Guangzhou","award":["2023A04J0568"],"award-info":[{"award-number":["2023A04J0568"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2024A03J0873"],"award-info":[{"award-number":["2024A03J0873"]}]},{"name":"Health Science and Technology Project of Guangzhou","award":["20231A011047"],"award-info":[{"award-number":["20231A011047"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Int Med Res"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate prognostication is crucial for managing human immunodeficiency virus (HIV)-associated cutaneous T-cell lymphoma. In this study, we aimed to develop an improved machine learning-based prognostic model for predicting the 5-year survival rates in HIV-associated cutaneous T-cell lymphoma patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We derived and tested machine learning models using algorithms including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest. Our study involved data from a US population-based cohort of patients diagnosed with HIV-associated cutaneous T-cell lymphoma between 1 January 2000 and 31 December 2018, which were extracted from the Surveillance, Epidemiology, and End Results database. The primary outcome was the prediction of 5-year overall survival. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using Brier scores.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A cohort of 381 HIV-associated cutaneous T-cell lymphoma patients was analyzed. Multivariate logistic regression identified age \u226560\u2009years (odds ratio\u2009=\u20094.88), regional stage (odds ratio\u2009=\u200910.31), distant stage (odds ratio\u2009=\u200928.37), and chemotherapy (odds ratio\u2009=\u20094.71) as significant independent risk factors for 5-year mortality. Among seven machine learning models developed, the XGBoost model demonstrated the highest discrimination for 5-year overall survival (AUC\u2009=\u20090.867), followed by LightGBM (AUC\u2009=\u20090.835). Both models exhibited good calibration with low Brier scores (XGBoost\u2009=\u20090.130, LightGBM\u2009=\u20090.109). Support Vector Machine performed optimally in ten-fold cross-validation, logistic regression showed the lowest Brier score (0.106), and XGBoost provided the best balance of discrimination and robust performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our novel machine learning approach produced prognostic models with superior discrimination for 5-year overall survival in HIV-associated cutaneous T-cell lymphoma patients using standard clinicopathological variables. These models offer potential for more accurate and personalized prognostics, potentially improving patient management and clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605251359433","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T01:56:30Z","timestamp":1757382990000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-based prognostic model for human immunodeficiency virus-associated cutaneous T-cell lymphoma: A Surveillance, Epidemiology, and End Results database analysis"],"prefix":"10.1177","volume":"53","author":[{"given":"Weimin","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manwen","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanlan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Ai","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinying","family":"Gan","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junteng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingzhen","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0949-0354","authenticated-orcid":false,"given":"Yongmin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Hematology, Guangzhou Eighth People\u2019s Hospital, Guangzhou Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms241210095"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.847710"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/00000478-199910000-00005"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13000-015-0419-5"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/jim-2017-000636"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(19900915)66:6<1130::AID-CNCR2820660609>3.0.CO;2-0"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/jncimonographs\/lgae033"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/ALN.0000000000004380"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/bco2.253"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2025.1542707"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1097\/QAD.0000000000002955"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci11081037"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1097\/QAI.0000000000002783"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2024.1383227"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.2147\/CMAR.S505390"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-024-02470-1"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e38435"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-023-05123-0"},{"key":"e_1_3_6_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2024.12.062"},{"key":"e_1_3_6_21_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1572720"},{"key":"e_1_3_6_22_2","doi-asserted-by":"publisher","DOI":"10.1111\/cts.13619"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-023-02886-2"},{"key":"e_1_3_6_24_2","first-page":"763","article-title":"Multi-task learning via adaptation to similar tasks for mortality prediction of diverse rare diseases","volume":"2020","author":"Liu L","year":"2020","unstructured":"Liu L, Liu Z, Wu H, et\u00a0al. 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