The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.

ABSTRACT:

Background

Conservative treatment remains a viable option for selected patients with ectopic pregnancy (EP), but failure may lead to rupture and serious complications. Currently, serum β-hCG is the main predictor for treatment outcomes, yet its accuracy is limited. This study aimed to develop and validate a predictive model that integrates radiomic features derived from super-resolution (SR) ultrasound images with clinical biomarkers to improve risk stratification.

Methods

A total of 228 patients with EP receiving conservative treatment were retrospectively included, with 169 classified as treatment success and 59 as failure. SR images were generated using a deep learning-based generative adversarial network (GAN). Radiomic features were extracted from both normal-resolution

SUBMITTER: Zhang M 

PROVIDER: S-EPMC12269241 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature