Predicting the efficiency of chidamide in patients with angioimmunoblastic T-cell lymphoma using machine learning algorithm - Zhang, Chunlan; Xu, Juan; Gu, Mingyu; Tang, Yun; Tang, Wenjiao; Wang, Jie; Liu, Qinyu; Yang, Yunfan; Zhong, Xushu; Xu, Caigang (Frontiers in Pharmacology, 2024)
Background: Chidamide is subtype-selective histone deacetylase (HDAC) inhibitor that showed promising result in clinical trials to improve prognosis of angioimmunoblastic T-cell lymphoma (AITL) patients. However, in real world settings, contradictory reports existed as to whether chidamide improve overall survival (OS). Therefore, we aimed to develop an interpretable machine learning (ML)-based model to predict the 2-year OS of AITL patients based on chidamide usage and baseline features. Methods: A total of 183 patients with AITL were randomly divided into training set and testing set. We used five ML algorithms to build predictive models. Recursive feature elimination (RFE) method was used to filter for the most important features. The ML models were interpreted and the relevance of the selected features was determined using the Shapley additive explanations (SHAP) method and the local interpretable modelagnostic explanation (LIME) algorithm. Results: A total of 183 patients with newly diagnosed AITL from 2012-2022 from 3 centers in China were enrolled in our study. Seventy-one patients were dead within 2 years after diagnosis. Five ML algorithms were built based on chidamide usage and 16 baseline features to predict 2-year OS. Catboost model presented to be the best predictive model. After RFE screening, 12 variables demonstrated the best performance (AUC=0.8651). Using chidamide ranked third among all the variables that correlated with 2-year OS. Conclusion: This study demonstrated that the Catboost model with 12 variables could effectively predict the 2-year OS of AITL patients. Combining chidamide in the treatment therapy was positively correlated with longer OS of AITL patients.