Abstract
New research on Drugs and Therapies-Personalized Medicine is the subject of a report. According to news reporting or iginating in Zhejiang, People's Republic of China, by NewsRx journalists, resear ch stated, "Hypothyroidism is a known adverse event associated with the use of i mmune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to deve lop an interpretable machine learning (ML) model for individualized prediction o f hypothyroidism in patients treated with ICIs." Financial support for this research came from Medical Science and Technology Pro ject of Zhejiang Province. The news reporters obtained a quote from the research from First Affiliated Hosp ital, "The retrospective cohort of patients treated with ICIs was from the First Affiliated Hospital of Ningbo University. ML methods applied include logistic r egression (LR), random forest classifier (RFC), support vector machine (SVM), an d extreme gradient boosting (XGBoost). The area under the receiver-operating cha racteristic curve (AUC) was the main evaluation metric used. Furthermore, the Sh apley 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 pa tients (12.88%) observed to have developed hypothyroidism. Among th e 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 thyr oid-stimulating hormone (TSH) was the most influential predictor variable. The d eveloped interpretable ML model holds potential for predicting the likelihood of hypothyroidism following ICI treatment in patients."
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