Improving radiomics-based models for esophagogastric variceal bleeding risk prediction in cirrhotic patients.

A recent study by Peng 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.