Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma
Authors Yang B, Guo L, Lu G, Shan W, Duan L, Duan S
Received 31 May 2019
Accepted for publication 30 July 2019
Published 19 August 2019 Volume 2019:11 Pages 7825—7834
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Bilikere Dwarakanath
Bin Yang,1,* Lili Guo,2,* Guangming Lu,1,* Wenli Shan,2 Lizhen Duan,2 Shaofeng Duan3
1Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, People’s Republic of China; 2Department of Radiology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, People’s Republic of China; 3GE Healthcare China, Shanghai 210000, People’s Republic of China
Correspondence: Lili Guo
Department of Radiology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, People’s Republic of China
Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, People’s Republic of China
Tel +86 13 95 160 8346
Fax +86 2 58 480 4659
*These authors contributed equally to this work
Purpose: We aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma.
Patients and Methods: A total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis.
Results: The radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82).
Conclusion: The CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.
Keywords: lung adenocarcinoma, radiomics, biomarker, computed tomography
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