Machine learning-based prognostic model for human immunodeficiency virus-associated cutaneous T-cell lymphoma: A...

ObjectiveAccurate 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.MethodsWe 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.ResultsA cohort of 381 HIV-associated cutaneous T-cell lymphoma patients was analyzed. Multivariate logistic regression identified age ≥60 years (odds ratio = 4.88), regional stage (odds ratio = 10.31), distant stage (odds ratio = 28.37), and chemotherapy (odds ratio = 4.71) 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 = 0.867), followed by LightGBM (AUC = 0.835). Both models exhibited good calibration with low Brier scores (XGBoost = 0.130, LightGBM = 0.109). 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.ConclusionOur 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.