Dynamic weight loss trajectories and machine learning-based prediction of malnutrition in colorectal cancer patients after curative surgery
Year: 2026
Type: article
Study design: Observational Study
Abstract: Objective: This study aimed to identify distinct postoperative weight loss trajectories and develop a machine learning-based model for predicting malnutrition risk at 6 months after curative resection for colorectal cancer (CRC). Methods: This prospective observational study enrolled 205 CRC patients who underwent curative resection. Weight loss percentages were measured at four postoperative time points relative to preoperative baseline: T1 (postoperative day 7 or hospital discharge), T2 (1 month), T3 (3 months), and T4 (6 months). Latent class growth modeling (LCGM) was employed to identify distinct weight change trajectory patterns. Malnutrition was assessed using the Global Leadership Initiative on Malnutrition (GLIM) criteria at 6 months post-surgery. Core predictors for 6-month malnutrition were screened by taking the intersection of Lasso regression and the Boruta algorithm. Ten machine learning models were constructed and systematically compared, with the optimal model interpreted using SHapley Additive exPlanations (SHAP). Results: LCGM identified three distinct weight change trajectories: persistent weight loss (28.3%), gradual recovery (38.5%), and rapid recovery (33.2%). Multinomial logistic regression (reference category: persistent weight loss group) showed that age > 65 years and ASA III-IV were associated with lower odds of gradual recovery, whereas colon cancer (right or left) was associated with higher odds. For rapid recovery, female, age > 65 years, ASA III-IV, and adjuvant chemotherapy were associated with lower odds, whereas colon cancer (right or left) was associated with higher odds. The overall incidence of GLIM-defined malnutrition at 6 months was 33.2%. Among the ten machine learning models, XGBoost achieved the best comprehensive performance, with an AUC of 0.836 (95% CI: 0.779-0.894). Calibration curves demonstrated good agreement between predicted and observed probabilities, and decision curve analysis confirmed net clinical benefit across a 9-91% threshold range. SHAP analysis revealed that lower preoperative BMI, receipt of adjuvant chemotherapy, older age, and rectal tumor location were associated with increased malnutrition risk. Conclusion: This study revealed three distinct postoperative weight loss trajectories in CRC patients. The developed XGBoost model, incorporating four readily available clinical variables, demonstrated favorable predictive performance and clinical utility for early identification of patients at high risk of long-term malnutrition, pending further external validation.
Source: Frontiers in Nutrition
Institutions: Fujian Medical University, Fujian Provincial Cancer Hospital
Language: English
Subfield: Physiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223137266