Cargando…
Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning
PURPOSE: To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images. MATERIALS AND METHODS: A total of 298 patients were identified from a retrospect...
Autores principales: | Chen, Xiao, Zhang, Yang, Zhou, Jiahuan, Wang, Xiao, Liu, Xinmiao, Nie, Ke, Lin, Xiaomin, He, Wenwen, Su, Min-Ying, Cao, Guoquan, Wang, Meihao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792864/ https://www.ncbi.nlm.nih.gov/pubmed/36582788 http://dx.doi.org/10.3389/fonc.2022.991892 |
Ejemplares similares
-
Digital tomosynthesis spot view in architectural distortions: outcomes in management and radiation dose
por: Fiaschetti, Valeria, et al.
Publicado: (2022) -
Positive Predictive Value of Tomosynthesis-guided Biopsies of Architectural Distortions Seen on Digital Breast Tomosynthesis and without an Ultrasound Correlate
por: Vijayaraghavan, Gopal R., et al.
Publicado: (2019) -
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images
por: Buda, Mateusz, et al.
Publicado: (2021) -
Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification
por: Liu, Xinmiao, et al.
Publicado: (2022) -
Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis
por: Murtas, Federica, et al.
Publicado: (2023)