Cargando…
Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature
SIMPLE SUMMARY: Non-invasive imaging modalities are commonly used in clinical practice. Recently, the application of machine learning (ML) techniques has provided a new scope for more detailed imaging analysis in esophageal cancer (EC) patients. Our review aims to explore the recent advances and fut...
Autores principales: | Xie, Chen-Yi, Pang, Chun-Lap, Chan, Benjamin, Wong, Emily Yuen-Yuen, Dou, Qi, Vardhanabhuti, Varut |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158761/ https://www.ncbi.nlm.nih.gov/pubmed/34069367 http://dx.doi.org/10.3390/cancers13102469 |
Ejemplares similares
-
Discrimination of pulmonary ground-glass opacity changes in COVID‐19 and non-COVID-19 patients using CT radiomics analysis
por: Xie, Chenyi, et al.
Publicado: (2020) -
Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma
por: Hu, Yihuai, et al.
Publicado: (2020) -
CT scan AI-aided triage for patients with COVID-19 in China
por: Vardhanabhuti, Varut
Publicado: (2020) -
Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT
por: Chan, Benjamin, et al.
Publicado: (2023) -
Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
por: Xie, Chen-Yi, et al.
Publicado: (2021)