Hemorrhage risk prediction after computed tomography-guided lung biopsy: Clinical parameters and quantitative pulmonary vascular analysis

Lin et al. explored pulmonary hemorrhage risk after CT-guided lung biopsy by integrating clinical and vascular parameters. Analyzing 126 patients, they used Aview software for vascular segmentation to quantify vessel density, diameter, and BV5 (volume in vessels ≤5 mm²). Multivariate analysis indicated that greater lesion depth, higher vessel density, and increased BV5 were significant predictors of higher-grade hemorrhage. A nomogram model was developed for risk prediction, achieving an AUC of 0.738 in the discovery cohort and 0.783 in the validation cohort. This study highlights quantitative vascular parameters, especially BV5 and small vessel density, as new markers for hemorrhage risk, suggesting these can guide safer biopsy protocols. However, the study’s retrospective and single-center design limits its generalizability. Future research should include multi-center validation and consider machine learning integration to enhance model precision, ensuring broader clinical application and improved patient safety in lung biopsy procedures.