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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

DOI https://doi.org/10.2147/CMAR.S217887

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Melinda Thomas

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
Email guolili163@163.com

Guangming Lu
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
Email cjr.luguangming@vip.163.com

*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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