Comparing different models in the test set
PRS, abortion, ER+ breast cancer, breast cancer
OVARIAN CANCER, BREAST CANCER, SENSITIVITY, REGRESSION, ER+ BREAST CANCER
We examined the performance of various models we had created in the test set, as well as risk scores calculated from established breast cancer risk prediction tools (Table The comparison of all the models in the test set.Model 1: logistics regression model that includes only traditional risk factors; model 2: logistics regression model that includes both traditional risk factors and PRS; model 3: lasso regression model; model 4: SVM model; model 5: CKB model; model 6: Gail model; model 7: Tyrer–Cuzick model.P (1) represents comparison with model 1, P (2) represents comparison with model 2, P (3) represents comparison with model3, P (4) represents comparison with model 4, P (5) represents comparison with model 5, P (6) represents comparison with model.Receiver‐operating characteristic (ROC) curves for different risk assessment models of breast cancer. (A): ROC curves of logistic model that include only traditional risk factors compared with the models based on CKB score, Gail score and Tyrer–Cuzick score. (B): ROC curves of logistic model that include only traditional risk factors compared with logistics regression model that include both traditional risk factors and PRS. (C): ROC curves of logistic model that include only traditional risk factors compared with LASSO regression model and SVM m.The 8 variables included in the model for ER+ breast cancer were age, age at menarche, age at first live birth, number of live birth, number of abortion, use of exogenous hormones, family history of breast cancer and ovarian cancer, and prior breast surgery, with an AUC of 0.73 (95% CI: 0.69–0.77, Sensitivity 41.7% and Specificity 92.8%). Only four factors, BMI, number of abortion, use of exogenous hormones and family history of breast cancer and ovarian cancer, were included in ER‐ breast cancer model, which had an AUC of 0.64 (95% CI: 0.57–0.72, Sensitivity 34.5% and Specificity 94.0%). However, there was no statistically statistical difference between the overall model and the risk prediction model stratified by ER status (Table 