Published online Mar 21, 2025. doi: 10.3748/wjg.v31.i11.101804
Revised: January 22, 2025
Accepted: February 20, 2025
Published online: March 21, 2025
Processing time: 167 Days and 15.7 Hours
A recent study by Peng et al developed a predictive model for first-instance secondary esophageal variceal bleeding in cirrhotic patients by integrating clinical and multi-organ radiomic features. The combined radiomic-clinical model demonstrated strong predictive capabilities, achieving an area under the curve of 0.951 in the training cohort and 0.930 in the validation cohort. The results highlight the potential of noninvasive prediction models in assessing esophageal variceal bleeding risk, aiding in timely clinical decision-making. Additionally, manual delineation of regions of interest raises the risk of observer bias despite efforts to minimize it. The study adjusted for clinical covariates, while some potential confounders, such as socioeconomic status, alcohol use, and liver function scores, were not included. Additionally, an imbalance in cohort sizes between the training and validation groups may reduce the statistical power of validation. Expanding the validation cohort and incorporating multi-center external validation would improve generalizability. Future studies should focus on incorporating long-term patient outcomes, exploring additional imaging modalities, and integrating automated segmentation techniques to refine the predictive model.
Core Tip: A study by Peng et al developed a combined multi-organ radiomics and clinical model to predict the risk of first-instance secondary esophageal variceal bleeding in cirrhotic patients. The model demonstrated high predictive accuracy, with an area under the curve of 0.951 in the training cohort, highlighting its potential for noninvasive risk stratification. By integrating radiomic features and clinical data, the model offers a more detailed approach to predicting esophageal variceal bleeding, helping clinicians make timely decisions. While the findings are promising, validating the model further using diverse and larger patient cohorts and considering including additional factors to maximize its clinical relevance and applicability is recommended.