Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule.
Adenocarcinoma of Lung
/ diagnostic imaging
Aged
Biomarkers, Tumor
Female
Humans
Lung Neoplasms
/ diagnostic imaging
Male
Middle Aged
Multidetector Computed Tomography
/ methods
Neoplasm Invasiveness
Preoperative Care
/ methods
Radiographic Image Interpretation, Computer-Assisted
Retrospective Studies
Adenocarcinoma
Computational biology
Lung
Solitary pulmonary nodule
Tomography, x-ray computed
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Feb 2019
Feb 2019
Historique:
received:
09
11
2017
accepted:
07
05
2018
revised:
03
04
2018
pubmed:
4
7
2018
medline:
21
3
2019
entrez:
4
7
2018
Statut:
ppublish
Résumé
To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules. This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared. 355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value. The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma. • The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.
Identifiants
pubmed: 29967956
doi: 10.1007/s00330-018-5530-z
pii: 10.1007/s00330-018-5530-z
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
889-897Subventions
Organisme : the National Natural Science Foundation of China
ID : 81370035
Organisme : the National Natural Science Foundation of China
ID : 81771924
Organisme : National Natural Science Foundation of China
ID : 81230030
Organisme : The National Key R&D Program of China
ID : 2016YFE0103000
Organisme : The National Key R&D Program of China
ID : 2017YFC1308700
Organisme : The National Key R&D Program of China
ID : 2017YFC1308703
Organisme : Shanghai Pujiang Talent Program
ID : 15PJD002
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