The Effectiveness of Machine Learning in Predicting Lateral Lymph Node Metastasis From Lower Rectal Cancer: A Single Center Development and Validation Study

Aim: Accurate preoperative diagnosis of lateral lymph node metastasis (LLNM) from lower rectal cancer is important to identify patients who require lateral lymph node dissection (LLND). We aimed to create an effective prediction model for LLNM using machine learning by combining preoperative information.

Methods: We retrospectively examined patients who underwent primary rectal cancer surgery with unilateral or bilateral LLND between April 2010 and March 2020 at a single institution. Using the machine learning software "Prediction One" (Sony Network Communications), we developed a prediction model in the training cohort that included 267 consecutive patients (500 sides) from April 2010. Clinicopathological data obtained from the preoperative examinations were used as the learning items. In the validation cohort that included subsequent patients until March 2020, we compared the discriminating powers of the prediction model and the conventional method using the short-axis diameter of the largest lateral lymph node, as detected on magnetic resonance imaging.

Results: The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.903 in the validation cohort comprising 56 patients (107 sides). This indicated significantly higher predictive power than that of the conventional method (AUC = 0.754;  = .022). Using the cutoff values defined in the training cohort, the accuracy, sensitivity, and specificity of the prediction model were 80.4%, 90.0%, and 79.4%, respectively. The model was able to correctly predict four of five sides comprising LLNM with the short-axis diameters ≤4 mm.

Conclusion: Machine learning contributed to the creation of an effective prediction model for LLNM.