Wen Tang — Endo Lab

article 2026

VGG11-based deep learning for automated MRI classification of adenomyosis: a systematic comparison of five classification criteria

·doi:10.1186/s12880-026-02633-4

Magnetic resonance imaging (MRI) has been increasingly applied in the classification of adenomyosis; however, due to the lack of a standardized gold standard for lesion classification, it remains challenging to make an early diagnosis and i…

other 2025

Combined magnetic resonance imaging with serum CA125 for dysmenorrhea in adenomyosis

Scientific reports ·doi:10.1038/s41598-025-26412-3

To evaluate the synergistic predictive capacity of MRI lesion classification and serum CA125 for symptom manifestation (dysmenorrhea/menorrhagia) and explore associated adenomyosis pathogenesis. Patients before focused ultrasound ablation s…

preprint 2025

Deep Learning for Classification Criteria of Adenomyosis: Evaluating Performance Across Based on MRI

·doi:10.2139/ssrn.5235028

preprint 2025

Deep Learning For Classification Criteria of Adenomyosis: Evaluating Performance Across Based on MRI

·doi:10.2139/ssrn.5235023

preprint 2024

Magnetic resonance imaging-based Classification criteria combined with Serum CA125 for dysmenorrhea before focused ultrasound ablation surgery in adenomyosis

·doi:10.21203/rs.3.rs-5025850/v1

Abstract Objectives To inform dysmenorrhea for exploring the possible pathogenesis of adenomyosis based on MRI classification criteria and serum CA125. Methods Patients before focused ultrasound ablation surgery were categorized into …

2020

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

·doi:10.21203/rs.3.rs-92781/v1

Abstract Background: Non-mass enhancement (NME) is a diagnostic dilemma. Texture analysis (TA) could serve as an objective method to quantify tumor characteristics and growth patterns. However, there are few reports about TA use in NME dia…