Radiomic Detection of EGFR Mutations in NSCLC.
Adenocarcinoma
/ diagnostic imaging
Algorithms
Area Under Curve
Carcinoma, Non-Small-Cell Lung
/ diagnostic imaging
ErbB Receptors
/ antagonists & inhibitors
Female
Humans
Lung Neoplasms
/ diagnostic imaging
Machine Learning
Male
Mutation
ROC Curve
Reproducibility of Results
Tomography, X-Ray Computed
/ methods
Journal
Cancer research
ISSN: 1538-7445
Titre abrégé: Cancer Res
Pays: United States
ID NLM: 2984705R
Informations de publication
Date de publication:
01 02 2021
01 02 2021
Historique:
received:
27
03
2020
revised:
04
08
2020
accepted:
26
10
2020
pubmed:
6
11
2020
medline:
30
4
2021
entrez:
5
11
2020
Statut:
ppublish
Résumé
Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non-small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21
Identifiants
pubmed: 33148663
pii: 0008-5472.CAN-20-0999
doi: 10.1158/0008-5472.CAN-20-0999
doi:
Substances chimiques
ErbB Receptors
EC 2.7.10.1
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
724-731Informations de copyright
©2020 American Association for Cancer Research.
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