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.

SUBMITTER: Jeon Y 

PROVIDER: S-EPMC10723658 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature