Machine learning based prediction of recurrence after curative resection for rectal cancer

Abstract

ABSTRACTPurposePatients with rectal cancer without distant metastases are typically treated with radical surgery. Post curative resection, several factors can affect tumor recurrence. This study aimed to analyze factors related to rectal cancer recurrence after curative resection using different machine learning techniques.MethodsConsecutive patients who underwent curative surgery for rectal cancer between 2004 and 2018 at Gil Medical Center were included. Patients with stage IV disease, colon cancer, anal cancer, other recurrent cancer, emergency surgery, or hereditary malignancies were excluded from the study. The SMOTETomek technique was used to compensate for data imbalance between recurrent and no-recurrent groups. Four machine learning methods, logistic regression (LR), support vector machine (SVM), random forest (RF), and XGBoost (XGB), were used to identify significant factors. To overfit and improve the model performance, feature importance was calculated using the permutation importance technique.ResultsA total of 3320 patients were included in the study. However, after exclusion, the total sample size of the study was 961 patients. The median follow-up period was 60.8 months (range:1.2-192.4). The recurrence rate during follow-up was 13.2% (n=127). After applying the SMOTETomek method, the number of patients in both groups, recurrent and non-recurrent group were equalized to 667 patients. After analyzing for 16 variables, the top eight ranked variables (pT, sex, concurrent chemoradiotherapy, pN, age, postoperative chemotherapy, pTNM, and perineural invasion) were selected based on the order of permutational importance. The highest area under the curve (AUC) was for the SVM method (0.831). The sensitivity, specificity, and accuracy were found to be 0.692, 0.814, and 0.798, respectively. The lowest AUC was obtained for the XGBloost method (0.804), with a sensitivity, specificity, and accuracy of 0.308, 0.928, and 0.845, respectively. The variable with highest importance was pT as assessed through SVM, RF, and XGBoost (0.06, 0.12, and 0.13, respectively), whereas pTNM had the highest importance when assessed by LR (0.05).ConclusionsIn the current study, SVM showed the best AUC, and the most influential factor across all machine learning methods except LR was found to be pT. Clinicians should be more alert if patients have a high pT stage during postoperative follow-up in rectal cancer patients.