Key result
Decision tree model predicts second primary thyroid cancer risk in breast cancer patients with ~0.91 AUC.
Why the study?
Second primary thyroid cancer is the most common multiple primary cancer in female breast cancer patients during survivorship, but tools to predict its occurrence after surgery are needed.
Can machine learning algorithms effectively predict the risk of second primary thyroid cancer in postoperative female breast cancer patients?
Case-Control (n=794)
No
Can machine learning algorithms effectively predict the risk of second primary thyroid cancer in postoperative female breast cancer patients?
Effect estimate: AUC 0.909 (95% CI 0.879-0.939)
A Decision Tree machine learning model can effectively predict the risk of second primary thyroid cancer in postoperative female breast cancer patients, providing a tool for personalized survivorship monitoring.
Should not yet inform SPTC risk prediction in practice; leaves open prospective validation of retrospective decision tree performance.
Female breast cancer patients remain at risk of developing additional primary cancers even following surgical treatment. Second primary thyroid cancer (SPTC) is the most common type of multiple primary cancer (MPC) in this demographic during their survivorship period, significantly burdening patients' quality of life. This study aimed to develop a risk prediction model for the occurrence of SPTC in postoperative female breast cancer patients using various machine learning algorithms. Key risk factors were progressively identified utilizing a literature review, the Delphi method, and the LASSO algorithm. A retrospective cohort of 684 breast cancer patients (228 in the SPTC group and 456 in the non-SPTC group) from 2014 to 2019 was used to construct and internally validate the optimal prediction model for postoperative SPTC risk. An independent cohort of 110 breast cancer patients treated from 2021 to 2024 was collected for temporal-split validation. Five machine learning algorithms-Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes-were compared. The Decision Tree model demonstrated superior performance on both the training and test sets, with AUC values of 0.915 (95% CI: 0.891-0.939) and 0.909 (95% CI: 0.879-0.939), alongside recall rates of 0.80 for both. On the temporal-split validation set, it achieved an AUC of 0.861 (95% CI: 0.802-0.920) and a recall of 0.77. Calibration curve analysis revealed that the Decision Tree model achieved the lowest Log loss value, indicating high consistency between the predicted and observed probabilities of SPTC. Interpretability analysis using the SHAP framework revealed that BMI ≥ 30 kg/m², anxious or depressive psychological state, history of thyroid disease, ER positivity, and PR positivity positively influenced the model output. Decision tree models can effectively assist in evaluating the risk of SPTC in postoperative female breast cancer patients, providing a valuable reference for the formulation of personalized survivorship monitoring strategies.
No takes yet. Share an insight, caveat, or question.
Yang et al. (2026) conducted a case-control in Second primary thyroid cancer in female breast cancer (n=794). Decision Tree model vs. Logistic Regression, KNN, SVM, and Naive Bayes models was evaluated on Area under the receiver operating characteristic curve (AUC) on the test set (AUC 0.909, 95% CI 0.879-0.939). The Decision Tree model effectively predicted the risk of second primary thyroid cancer in postoperative female breast cancer patients, achieving an AUC of 0.909 on the test set.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: