Abstract
Objectives
To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans.
Methods
This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis.
Results
The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively.
Conclusions
The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival.
Key Points
• Radiomic biomarker on CT was designed to identify phenotypes of lung adenocarcinoma
• Built up radiomic signature for lung adenocarcinoma manifested as subsolid nodules
• Retrospective study showed radiomic signature had greater diagnostic accuracy than volumetric analysis
• Radiomics help to evaluate intratumour heterogeneity within lung adenocarcinoma
• Medical decision can be given with more confidence
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Guarantor
The scientific guarantor of this publication is Hai-Bin Shi.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Funding
The authors state that this work has not received any funding.
Statistics and biometry
Yao Liu kindly provided statistical advice for this manuscript.
Ethical approval
Institutional Review Board approval was obtained.
Informed consent
Written informed consent was waived by the Institutional Review Board.
Methodology
• retrospective
• diagnostic or prognostic study
• performed at one institution
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Supplementary Fig. 1
P and FDR values from U-test for discriminating invasive and non-invasive adenocarcinoma. (GIF 60 kb)
Supplementary Fig. 2
Mean values of observed features in invasive and non-invasive adenocarcinoma. (GIF 168 kb)
Supplementary Fig. 3
Normal probability plot of each radiomic feature. (GIF 151 kb)
Supplementary Fig. 4
Values' plot of each radiomic feature. (GIF 135 kb)
Supplementary Fig. 5
Histogram of each radiomic feature. (GIF 138 kb)
Supplementary Fig. 6
Paired correlation map between each radiomic feature. (GIF 204 kb)
Supplementary Fig. 7
First 3 PCA components and distribution of all radiomic features. (GIF 95 kb)
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Yuan, M., Zhang, YD., Pu, XH. et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol 27, 4857–4865 (2017). https://doi.org/10.1007/s00330-017-4855-3
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DOI: https://doi.org/10.1007/s00330-017-4855-3