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

Abstract

Purpose Patients 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. Methods Consecutive 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 Synthetic Minority Oversampling Technique with Tomek link (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 Extreme gradient boosting (XGBoost), were used to identify significant factors. To overfit and improve the model performance, feature importance was calculated using the permutation importance technique. Results A total of 3320 patients were included in the study. 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 {pathologic Tumor stage (pT), sex, concurrent chemoradiotherapy, pathologic Node stage (pN), age, postoperative chemotherapy, pathologic Tumor-Node-Metastasis stage (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 XGBoost 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). Conclusions In 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. The rectal cancer patients who have a high pT stage during postoperative follow-up are need to be more close surveillance.

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

“…Their results indicated that SVM achieved the best predictive performance (AUC: 0.831, Se: 69.2%, Sp: 81.4%, and accuracy: 79.8%) for the prediction of the evaluated outcome. RF, which achieved a Se of 73.1%, a Sp of 80.2%, an accuracy of 79.3%, and an AUC value of 0.826, closely followed this algorithm [ 45 ]. In contrast, the lowest AUC value was obtained for the XGBoost method (0.804), with a sensitivity, specificity, and accuracy of 30.8%, 92.8%, and 84.5%, respectively.…”

“…Jeon et al investigated the predictive performance of 4 ML-based algorithms (logistic regression, LR, SVM, RF, and extreme gradient boosting, XGBoost) based on clinical and paraclinical data for the prediction of rectal cancer recurrence after curative resection [ 45 ]. Their results indicated that SVM achieved the best predictive performance (AUC: 0.831, Se: 69.2%, Sp: 81.4%, and accuracy: 79.8%) for the prediction of the evaluated outcome.…”

Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians

“…In the postoperative and surveillance subdomains, Jeon et al employed ML techniques, including LR, support vector machine (SVM), RF, and XGBoost, to predict rectal cancer recurrence following curative resection, with SVM yielding the highest area under the curve (AUC) of 0.831 [65]. Significant predictors of recurrence, such as pathologic Tumor stage (pT), concurrent chemoradiotherapy, and pathologic Node stage (pN), underscored the potential of ML models in stratifying patients for enhanced postoperative surveillance [65]. This study emphasizes the utility of advanced analytics in tailoring follow-up care and improving outcomes for rectal cancer patients, particularly those with elevated pT stages requiring intensified monitoring.…”

“…This study emphasizes the utility of advanced analytics in tailoring follow-up care and improving outcomes for rectal cancer patients, particularly those with elevated pT stages requiring intensified monitoring. In patients undergoing surgical resection for colorectal, liver, and pancreatic cancers, postoperative complications pose significant challenges despite low mortality rates [65]. Merath et al utilized NSQIP data from 2014 to 2016, and decision tree models were employed to forecast overall and specific complications [66].…”