Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule
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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.
KeywordsLung Adenocarcinoma Tomography, x-ray computed Computational biology Solitary pulmonary nodule
Artificial neural networks
Area under the curve
Grey-level co-occurrence matrix
Grey-level run-length matrix
International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society
The least absolute shrinkage and selection operator
Minimally invasive adenocarcinoma
This study has received funding by the National Natural Science Foundation of China (grant numbers 81370035, 81230030, and 81771924), The National Key R&D Program of China (grant number 2016YFE0103000, 2017YFC1308703, 2017YFA0205200, 2017YFC1309100 and 2017YFC1308700), Shanghai Pujiang Talent Program (grant number 15PJD002).
Compliance with ethical standards
The scientific guarantor of this publication is Prof. Shiyuan Liu.
Conflict of interest
The authors of this article declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• multicentre study
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