Machine Learning-Based Risk Scoring Using Clinical and Surgical Variables for Severe Complications after Rectal Cancer Surgery
Seung Ho Song, Hyeok Kyu Kwon, Jun Seok Park, Soo Yeun Park, Hye Jin Kim, Jeongjik Lee, Minwoo Chae, Gyu-Seog ChoiBACKGROUND:
Severe postoperative complications after rectal cancer surgery increase morbidity, delay recovery, and may compromise oncologic outcomes. Existing risk models show limited accuracy and generalizability. Machine learning can integrate complex clinical and surgical variables to improve individualized risk prediction.
OBJECTIVE:
To develop and prospectively validate machine learning-based models for predicting severe postoperative complications after rectal cancer surgery.
DESIGN:
Retrospective model development and prospective temporal internal validation study.
SETTING:
Single tertiary referral center in South Korea.
PATIENTS:
A total of 3,503 patients who underwent curative rectal cancer resection with anastomosis between 2011 and 2025 were included, comprising 3,402 retrospective and 101 prospective cases.
MAIN OUTCOME MEASURES:
Severe postoperative complications defined as Clavien–Dindo grade ≥ 3 within 30days after surgery.
RESULTS:
Severe complications occurred in 9.8% of the retrospective and 13.9% of the prospective cohort. Four machine-learning algorithms – logistic regression, random forest, artificial neural network, and gradient boosting – were trained using 5-fold crossvalidation. In internal validation, the random forest model achieved the best performance (area under the receiver operating characteristic Curve 0.898; Sensitivity 0.685), while gradient boosting performed best in the temporal internal validation cohort (area under the receiver operating characteristic Curve 0.910; Sensitivity 0.707). The most influential predictors were tumor perforation, intraoperative transfusion, protective stoma, bowel obstruction, and cerebrovascular accident.
LIMITATIONS:
Validation was single institution (temporal internal, not external) with a small prospective cohort.
CONCLUSIONS:
Machine learning-based models accurately predicted severe postoperative complications after rectal cancer surgery, with strong discrimination maintained on temporal internal validation. Applied immediately after surgery, they may support individualized postoperative risk stratification and monitoring; external validation is warranted. See Video Abstract.