Predictive Model for Hematoma Formation Following Ultrasound-Guided Excision of Benign Breast Lesions

The objective of this study was to develop a model for predicting hematoma formation after ultrasound-guided vacuum-assisted excision (US-VAE) in patients with benign breast lesions.

This retrospective study included 302 benign breast lesions from 276 patients who had undergone US-VAE. The patients were divided into training (190 patients, 201 lesions) and validation (86 patients, 101 lesions) datasets. The risk factors for hematoma were analyzed, including the lesion depth, location, maximum diameter, and pathological results, distance from the nipple, number of lesions removed, tissue components surrounding the lesions, color Doppler blood flow image characteristics, and breast thickness. Binary logistic regression was used to construct the prediction model, and a nomogram was constructed. The performance of the prediction model was assessed by obtaining the area under the receiver operating characteristic curve (AUC) and calibration plots for both training and validation datasets.

Lesion depth ≥ 1.5 cm or < 0.7 cm, color Doppler blood flow image Adler grade 2 or 3, non-fibroadenoma pathological type, and breast thickness > 2 cm were important predictors of hematoma occurrence, with odds ratios of 2.303 (P = 0.037), 2.582 (P = 0.004), 2.133 (P = 0.017), and 2.133 (P = 0.024), respectively. The prediction model performed well in both the training (AUC = 0.701, 95% CI = 0.626-0.775) and validation (AUC = 0.740, 95% CI = 0.644-0.836) datasets.

This prediction model can be used to predict the probability of hematoma after US-VAE in patients with benign breast lesions.