科研之友
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摘要
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.
- 出版日期2017-11
- 单位南京医科大学
