Zhang Chunlan, Xu Juan, Gu Mingyu, Tang Yun, Tang Wenjiao, Wang Jie, Liu Qinyu, Yang Yunfan, Zhong Xushu, Xu Caigang
Department of Hematology, Institute of Hematology, West China Hospital, Sichuan University, Chengdu, China.
West China School of Medicine and West China Hospital, Sichuan University, Chengdu, China.
Front Pharmacol. 2024 Aug 28;15:1435284. doi: 10.3389/fphar.2024.1435284. eCollection 2024.
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 (Machine learning)-based model to predict the 2-year overall survival of AITL patients based on chidamide usage and baseline features.
A total of 183 patients with AITL were randomly divided into training set and testing set. We used 5 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 model-agnostic explanationalgorithm.
A total of 183 patients with newly diagnosed AITL from 2012 to 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.
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.
西达本胺是一种亚型选择性组蛋白去乙酰化酶（HDAC）抑制剂，在改善血管免疫母细胞性T细胞淋巴瘤（AITL）患者预后的临床试验中显示出有前景的结果。然而，在现实世界中，关于西达本胺是否能改善总生存期（OS）存在相互矛盾的报道。因此，我们旨在开发一种基于可解释机器学习的模型，根据西达本胺的使用情况和基线特征预测AITL患者的2年总生存期。
总共183例AITL患者被随机分为训练集和测试集。我们使用5种机器学习算法构建预测模型。采用递归特征消除（RFE）方法筛选最重要的特征。使用Shapley加法解释（SHAP）方法和局部可解释模型无关解释算法对机器学习模型进行解释，并确定所选特征的相关性。
我们的研究纳入了2012年至2022年来自中国3个中心的183例新诊断的AITL患者。71例患者在诊断后2年内死亡。基于西达本胺的使用情况和16个基线特征构建了5种机器学习算法来预测2年总生存期。Catboost模型表现为最佳预测模型。经过RFE筛选，12个变量表现出最佳性能（AUC = 0.8651）。使用西达本胺在与2年总生存期相关的所有变量中排名第三。
本研究表明，具有12个变量的Catboost模型可以有效预测AITL患者的2年总生存期。在治疗中联合使用西达本胺与AITL患者更长的总生存期呈正相关。
