Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive procedure marked by considerable inter-observer disagreement and diagnostic delays. Existing non-invasive approaches, …
This study investigates whether quantitative analysis of preoperative Magnetic Resonance Imaging (MRI) scans can differentiate deep infiltrating endometriosis (DIE) lesion types (active or fibrotic) and associate them with reported pain …
Diagnosis of endometriosis faces significant challenges including diagnostic delay and reliance on invasive procedures. Deep endometriosis (DE) poses additional difficulties in non-invasive diagnosis due to its subtle and complex imaging …
Machine Learning Model Using CT Radiomics Achieves High Accuracy in Differentiating Malignant and Benign Endometrial Tumors geneonline.com
AI Radiomics Accurately Differentiates Endometrial Tumors Bioengineer.org
AI Radiomics Accurately Differentiates Endometrial Tumors BIOENGINEER.ORG
CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study BioMedical Engineering OnLine
Preoperative prediction of the HER2 status and prognosis of patients with endometrial cancer using multiparametric MRI-based radiomics: a multicenter study Nature
Radiomics analysis based on T2-weighed imaging and T2 mapping for staging endometrial fibrosis Nature
Ultrasound techniques for diagnosing deep infiltrating endometriosis (DIE) currently lack a quantitative method to assess microstructural heterogeneity in relation to diagnosis and clinical symptoms. This study evaluates Shannon entropy-based radiomics …