Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta‐analysis
Abstract Introduction We present the state of the art of ultrasound‐based machine learning (ML) radiomics models in the context of ovarian masses and analyze their accuracy in differentiating between benign and malignant adnexal masses. Material and Methods Web of Science, PubMed, and Scopus databas...
Saved in:
Main Authors: | Francesca Moro, Marianna Ciancia, Maria Sciuto, Giulia Baldassari, Huong Elena Tran, Antonella Carcagnì, Anna Fagotti, Antonia Carla Testa |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2025-08-01
|
Series: | Acta Obstetricia et Gynecologica Scandinavica |
Subjects: | |
Online Access: | https://doi.org/10.1111/aogs.15146 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors
by: Fan Yang, et al.
Published: (2025-07-01) -
Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules
by: Xingxing Chen, et al.
Published: (2025-07-01) -
Research on the application of distinguishing between benign and malignant breast nodules using MRI and US radiomics
by: Yifan Liu, et al.
Published: (2025-07-01) -
Radiomics and radiogenomics in intrahepatic cholangiocarcinoma
by: A. D. Smirnova, et al.
Published: (2024-03-01) -
Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification
by: V. L. Sowmya, et al.
Published: (2025-07-01)