{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T03:52:53Z","timestamp":1785642773303,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but a lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalances and the inherent complexity of time-dependent events. While there are current prognostic indices for survival, they do not specifically address HT risk. This study aimed to develop and validate survival-based and traditional classification machine-learning models to predict HT in cohorts. Methods: Using a multicenter retrospective dataset (n = 1068), survival models (Cox proportional hazards, Lasso-Cox, Random Survival Forest, Gradient-boosted Cox [GBM-Cox], eXtreme Gradient Boosting [XGBoost]-Cox), and classification models (Logistic regression, Lasso logistic, Random Forest, Gradient Boosting, XGBoost) were compared. The best-performing survival models\u2014XGBoost-Cox, Lasso-Cox, and GBM-Cox\u2014were assessed on an independent test set (n = 92). Model sensitivity was maximized using optimal binary risk cutoff points based on Youden\u2019s index. Results: Survival models showed superior predictive performance than classical classifiers, with XGBoost-Cox exhibiting the highest mean accuracy (85.3%), time-dependent area under the curve (0.795), sensitivity (98%), specificity (83.9%), and concordance index (0.836). Incorporating next-generation sequencing (NGS) data improved model accuracy and specificity, indicating that genetic factors improve HT prediction. Principal component analysis revealed distinct gene mutation patterns associated with HT risk, highlighting DNA-repair genes such as TP53, BLM, and RAD50. Conclusions: This study highlights the clinical value of survival-based machine-learning methods integrated with NGS data to personalize HT risk stratification for patients with FL and MZL.<\/jats:p>","DOI":"10.3390\/cancers17182952","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T19:16:17Z","timestamp":1757445377000},"page":"2952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-992X","authenticated-orcid":false,"given":"Tong-Yoon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Hematology, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"},{"name":"Lymphoma and Cell Therapy Research Center, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3140-3681","authenticated-orcid":false,"given":"Tae-Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9041-9977","authenticated-orcid":false,"given":"Eun Ji","family":"Han","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine, Department of Radiology, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7636-4693","authenticated-orcid":false,"given":"Gi-June","family":"Min","sequence":"additional","affiliation":[{"name":"Lymphoma and Cell Therapy Research Center, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"},{"name":"Department of Hematology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5429-4839","authenticated-orcid":false,"given":"Seok-Goo","family":"Cho","sequence":"additional","affiliation":[{"name":"Lymphoma and Cell Therapy Research Center, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"},{"name":"Department of Hematology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seoree","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Oncology, Bucheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 14647, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8465-2791","authenticated-orcid":false,"given":"Jong Hyuk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Hematology, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 21431, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byung-Su","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Hematology, Eunpyeong St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon Won","family":"Jeoung","sequence":"additional","affiliation":[{"name":"Department of Oncology, Daejeon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 34943, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5134-4538","authenticated-orcid":false,"given":"Hye Sung","family":"Won","sequence":"additional","affiliation":[{"name":"Department of Oncology, Uijeongbu St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 11765, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3362-8200","authenticated-orcid":false,"given":"Youngwoo","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Hematology, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"},{"name":"Lymphoma and Cell Therapy Research Center, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.3324\/haematol.2016.148809","article-title":"Non-Hodgkin lymphoma in the developing world: Review of 4539 cases from the International Non-Hodgkin Lymphoma Classification Project","volume":"101","author":"Perry","year":"2016","journal-title":"Haematologica"},{"key":"ref_2","first-page":"443","article-title":"2016 US lymphoid malignancy statistics by World Health Organization subtypes","volume":"66","author":"Teras","year":"2016","journal-title":"CA Cancer J. 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