Original Research
Tuesday, October 15, 2024
08:30 AM–10:00 AM
Abstract
Introduction: Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment.
Research Question or Hypothesis: This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in patients treated with ICIs.
Study Design: retrospective cohort study
Methods: 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, Shapley additive explanation (SHAP) was utilized to interpret the outcomes of the prediction model.
Results: 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.
Conclusion:
The developed interpretable ML model holds potential for predicting the likelihood of hypothyroidism following ICI treatment in patients. Machine learning technology offers new possibilities for predicting ICI-induced hypothyroidism, potentially providing more precise support for personalized treatment and risk management.
Presenting Author
Suyan Zhu Master's degree in Applied Chemistrythe First Affiliated Hospital of Ningbo University
Authors
Hong-Bin Xu PhD in Pharmacology
the First Affiliated Hospital of Ningbo University
Tongtong Yang Master
The First Affiliated Hospital of Ningbo University