Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

Background: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.

Aim: To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.

Analysis of Associated Factors, Construction, and Application of a Predictive Model for Lymph Node Metastasis in Colorectal Cancer.

Background: In colorectal cancer‎ (CRC)‎, understanding lymph node metastasis (LNM) is critical for effective treatment. Better approaches are required for identifying and assessing the risk contributions of factors influencing lymph node metastasis in colorectal cancer.

Objective: This study aims to analyze factors associated with LNM in CRC and develop a risk prediction model.

A New Nomogram Based on Early Postoperative NLR for Predicting Infectious Complications After Gastrectomy.

Purpose: Our study aimed to construct a visible model to evaluate the risk of infectious complications after gastrectomy.

Methods: The clinical data of 856 patients who underwent gastrectomy were used to retrieve medical records. Univariate and multivariate analyses were performed to correlate early postoperative NLR and operative variables with postoperative complications, and the construction of the nomogram was based on logistic regression.