Applying Machine Learning to Predict Esophageal Cancer Recurrence after Esophagectomy

Abstract

INTRODUCTION: Artificial intelligence and machine learning (ML) models have recently been adapted in healthcare applications with promising results. The objective of this proof-of-concept study was to develop a ML model designed to predict esophageal cancer recurrence after esophagectomy. METHODS: We conducted a retrospective study of 260 consecutive patients who underwent esophagectomy for esophageal cancer from 2009 through 2018. Over 20,000 patient-specific characteristics were collected. Risk prediction models for different prediction windows were constructed via a sequential forward selection process. Five traditional machine learning algorithms including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naive Bayes (NB) were included in our analysis. Model performance was assessed by calculating sensitivity, specificity, positive predictive value (PPV), F1 score, area under the receiver operating characteristic curve (AUC), and overall accuracy using five-fold cross-validation. RESULTS: Of the 260 patients, 121 (46.5%) experienced cancer recurrence at a median of 267 days (49-2,057 days). The highest AUC for prediction of recurrence at any time after esophagectomy was 0.82 (SD ± 0.02) by DT. Similarly, the accuracy of the model to predict whether or not a new subject inserted into the model will have recurrence at any time was 82% (0.82 ± 0.03) by RF. The LR algorithm had the highest accuracy (92%, 0.92 ± 0.01) for predicting recurrence within 180 days after esophagectomy (Table). CONCLUSION: This proof-of-concept study demonstrates the feasibility of using machine learning models to predict esophageal cancer recurrence. The accuracy and AUC of the machine learning models exceeded 80% in all recurrence prediction timeframes. Table. - Overall Performance of the Machine Learning Models Prediction window No. (%) of subjects with recurrence/no. (%) of subjects without recurrence Model Accuracy (SD) AUC (SD) Recurrence at any time after esophagectomy 121 (46.5) / 139 (53.5) LR 0.80 (0.05) 0.81 (0.04) SVM 0.77 (0.05) 0.79 (0.06) RF 0.82 (0.03) 0.82 (0.05) DT 0.79 (0.03) 0.82 (0.02) NB 0.80 (0.04) 0.78 (0.06) Recurrence within 180 days after esophagectomy 37 (14.2) / 223 (85.8) LR 0.92 (0.01) 0.78 (0.05) SVM 0.92 (0.02) 0.81 (0.07) RF 0.90 (0.03) 0.73 (0.10) DT 0.90 (0.02) 0.70 (0.04)