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Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma
- Source :
- Cancer Management and Research, Vol Volume 11, Pp 7825-7834 (2019)
- Publication Year :
- 2019
- Publisher :
- Dove Medical Press, 2019.
-
Abstract
- Bin Yang,1,* Lili Guo,2,* Guangming Lu,1,* Wenli Shan,2 Lizhen Duan,2 Shaofeng Duan31Department 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 ChinaCorrespondence: Lili GuoDepartment of Radiology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, People’s Republic of ChinaEmail guolili163@163.comGuangming LuDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, People’s Republic of ChinaTel +86 13 95 160 8346Fax +86 2 58 480 4659Email cjr.luguangming@vip.163.com*These authors contributed equally to this workPurpose: 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
Details
- Language :
- English
- ISSN :
- 11791322
- Volume :
- ume 11
- Database :
- Directory of Open Access Journals
- Journal :
- Cancer Management and Research
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.08469d5d9f16478f98296dab10b63f84
- Document Type :
- article