Interpretable machine learning model predicting immune checkpoint inhibitor-induced hypothyroidism: A retrospective cohort study

IF 4.5 2区 医学 Q1 ONCOLOGY
Cancer Science Pub Date : 2024-09-23 DOI:10.1111/cas.16352
Su-Yan Zhu, Tong-Tong Yang, Yi-Zhuo Zhao, Yu Sun, Xiao-Meng Zheng, Hong-Bin Xu
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引用次数: 0

Abstract

Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in patients treated with ICIs. The retrospective cohort of patients treated with ICIs was from the First Affiliated Hospital of Ningbo University. ML methods applied include logistic regression (LR), random forest classifier (RFC), support vector machine (SVM), and extreme gradient boosting (XGBoost). The area under the receiver-operating characteristic curve (AUC) was the main evaluation metric used. Furthermore, the Shapley additive explanation (SHAP) was utilized to interpret the outcomes of the prediction model. A total of 458 patients were included in the study, with 59 patients (12.88%) observed to have developed hypothyroidism. Among the models utilized, XGBoost exhibited the highest predictive capability (AUC = 0.833). The Delong test and calibration curve indicated that XGBoost significantly outperformed the other models in prediction. The SHAP method revealed that thyroid-stimulating hormone (TSH) was the most influential predictor variable. The developed interpretable ML model holds potential for predicting the likelihood of hypothyroidism following ICI treatment in patients. ML technology offers new possibilities for predicting ICI-induced hypothyroidism, potentially providing more precise support for personalized treatment and risk management.

Abstract Image

预测免疫检查点抑制剂诱发甲状腺功能减退症的可解释机器学习模型:一项回顾性队列研究
甲状腺功能减退症是已知的与癌症治疗中使用免疫检查点抑制剂(ICIs)相关的不良事件。本研究旨在开发一种可解释的机器学习(ML)模型,用于对接受 ICIs 治疗的患者的甲状腺功能减退症进行个体化预测。接受 ICIs 治疗的患者的回顾性队列来自宁波大学附属第一医院。应用的ML方法包括逻辑回归(LR)、随机森林分类器(RFC)、支持向量机(SVM)和极梯度提升(XGBoost)。所使用的主要评价指标是接收者工作特征曲线下的面积(AUC)。此外,还利用夏普利加法解释(SHAP)来解释预测模型的结果。研究共纳入 458 名患者,观察到 59 名患者(12.88%)出现甲状腺功能减退症。在所使用的模型中,XGBoost 的预测能力最高(AUC = 0.833)。德隆测试和校准曲线表明,XGBoost 的预测能力明显优于其他模型。SHAP 方法显示,促甲状腺激素(TSH)是最有影响力的预测变量。所开发的可解释 ML 模型具有预测 ICI 治疗后患者甲状腺功能减退可能性的潜力。ML 技术为预测 ICI 引起的甲状腺功能减退提供了新的可能性,有可能为个性化治疗和风险管理提供更精确的支持。
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来源期刊
Cancer Science
Cancer Science 医学-肿瘤学
自引率
3.50%
发文量
406
审稿时长
2 months
期刊介绍: Cancer Science (formerly Japanese Journal of Cancer Research) is a monthly publication of the Japanese Cancer Association. First published in 1907, the Journal continues to publish original articles, editorials, and letters to the editor, describing original research in the fields of basic, translational and clinical cancer research. The Journal also accepts reports and case reports. Cancer Science aims to present highly significant and timely findings that have a significant clinical impact on oncologists or that may alter the disease concept of a tumor. The Journal will not publish case reports that describe a rare tumor or condition without new findings to be added to previous reports; combination of different tumors without new suggestive findings for oncological research; remarkable effect of already known treatments without suggestive data to explain the exceptional result. Review articles may also be published.
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