Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma

Figure 3 Performance comparison between machine-learning models and computed tomography in evaluating lymph node metastasis status in esophageal cancer patients. A: The receiver operating characteristic curves in the training cohort, where lines of different colors represent different models or computed tomography (CT); B-F: The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the training and validation sets. Model-1 with the highest area under the curve in the training set, developed by the random forest algorithm, included five factors: (1) Tumor location; (2) Depth of tumor invasion; (3) Tumor length; (4) CT reported results; and (5) Number of abnormal protein biomarkers. Model-2 with the best F1 score in the training cohort, established by naïve Bayes algorithm, contained four variables: (1) Tumor location; (2) Depth of tumor invasion; (3) Tumor length; and (4) CT results.