2.7. Statistical Analysis

The clinical variable distribution of the patients in the training, internal validation, and external validation cohorts were compared using Student’s t-test or chi-squared test. For the OR prediction, performance was evaluated using the receiver operating characteristic curve (ROC) analysis and the area under the curve (AUC). Quantitative indices including accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV) were also computed with the confusion matrix. Youden’s J statistic [43] was applied to determine the optimal operating points in ROC analysis.

The DeLong test was used to compare AUCs between different models. Survival curves were generated using the Kaplan–Meier method, and OS was compared between low- and high-risk patients with the log-rank test. For the tumor segmentation, the Dice coefficient and tumor segmentation time were compared using paired Student’s t-test. All statistical analyses were performed using R (version 4.0.4, Foundation for Statistical Computing, Vienna, Austria). A two-tailed p-value of less than 0.05 was considered as statistical significance.