Cancers (Basel): Using machine learning models to study the related factors of thyroid-related adverse events in patients receiving PD-1 or PD-L1 inhibitors

Background and purpose: People are actively studying the molecular mechanisms of tumorigenesis and metastasis, especially T lymphocytes, especially the antigenic cytotoxicity, which has attracted more and more people's interest to develop immunotherapies for cancer treatment
.

Various negative regulators of T cell activation can be used as checkpoint molecules, such as cytotoxic T lymphocyte-associated protein 4 (CTLA-4) inhibitors, anti-programmed cell death 1 (PD-1) drugs, and anti-programmed cells Death Ligand 1 (PDL1) drug

Various negative regulators of T cell activation can be used as checkpoint molecules, such as cytotoxic T lymphocyte-associated protein 4 (CTLA-4) inhibitors, anti-programmed cell death 1 (PD-1) drugs and anti-programmed cells The therapeutic target of

Results: After adjusting for covariates, we found that smoking history and hypertension increased the risk of thyroid dysfunction by approximately 3.
7 times and 4.
1 times, respectively (95% confidence interval (ci) 1.
338-10.
496 and 1.
478-11.
332, p = 0.
012 and 0.
007)
.

In contrast, patients who took opioids had a risk of thyroid-related complications that were about 4.

Table 1 Multivariate analysis to identify predictors of thyroid-related adverse events in patients treated with immune checkpoint inhibitors
.

Table 1 Multivariate analysis to identify predictors of thyroid-related adverse events in patients treated with immune checkpoint inhibitors
.

Table 2 AUC comparison of Logistic regression model, elastic network model, random forest model and support vector machine model
.

Table 2 AUC comparison of Logistic regression model, elastic network model, random forest model and support vector machine model
.

Figure 1 Using random forest to predict the top 10 variables of thyroid-related adverse events in cancer patients treated with ICIS (estimated by importance)

Figure 1 Using random forest to predict the top 10 variables of thyroid-related adverse events in cancer patients treated with ICIS (estimated by importance)

Figure 2 Receiver operating characteristic curves for the prediction performance of Elastic Network (ENET), Logistic Regression (LR), Random Forest (RF) and Support Vector Machine Radial (SVM_R)

Figure 2 Receiver operating characteristic curves for the prediction performance of Elastic Network (ENET), Logistic Regression (LR), Random Forest (RF) and Support Vector Machine Radial (SVM_R)

Table 3 Details of the machine learning model
.

Table 3 Details of the machine learning model
.

Conclusion: This study used a variety of machine learning models to predict and found that factors such as smoking history, hypertension, and opioids were associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors
.

This study used a variety of machine learning models to predict and found that factors such as smoking history, hypertension, and opioids were associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors
.

Original source:

Kim W, Cho YA, Kim DC,et al.

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models in this message