Cancer Management and Research (Aug 2019)
Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma
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 [email protected] 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 [email protected]*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
