Cargando…
Delta radiomic features improve prediction for lung cancer incidence: A nested case–control analysis of the National Lung Screening Trial
BACKGROUND: Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm...
Autores principales: | Cherezov, Dmitry, Hawkins, Samuel H., Goldgof, Dmitry B., Hall, Lawrence O., Liu, Ying, Li, Qian, Balagurunathan, Yoganand, Gillies, Robert J., Schabath, Matthew B. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308046/ https://www.ncbi.nlm.nih.gov/pubmed/30507033 http://dx.doi.org/10.1002/cam4.1852 |
Ejemplares similares
-
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma
por: Paul, Rahul, et al.
Publicado: (2016) -
Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features
por: Paul, Rahul, et al.
Publicado: (2019) -
Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness
por: Cherezov, Dmitry, et al.
Publicado: (2019) -
Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's
por: Cherezov, Dmitry, et al.
Publicado: (2020) -
Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening
por: Pérez-Morales, Jaileene, et al.
Publicado: (2020)