Radiomic Signature as a Diagnostic Factor for Classification of Histologic Subtypes of Lung Cancer
To discuss the application of radiomics using Computerized Tomography (CT) analysis, for improving its diagnostic efficacy in lung, specifically in distinguishing Squamous Cell Carcinoma (SCC), lung Adenocarcinoma (ADC), and Small Cell Lung Cancer (SCLC).
The pathology of 189 identified cases of lung cancer was analyzed, retrospectively (60 patients with SCC, 69 patients with lung ADC and 60 patients with SCLC). A neural network was used to determine whether the pulmonary or mediastinal window was selected to extract effective radiomic features. The key features of radiomic signature were retrieved by a Least Absolute Shrinkage and Selection Operator (LASSO) multiple logistic regression model. Next, receiver operating characteristic curve and Area Under the Curve (AUC) analysis were used to evaluate the performance of the radiomic signature in both, training(129 patients) and validation cohorts (60 patients).
About 295 features were extracted from a manually outlined tumor region. Features extracted from mediastinal window CT scans had a better prognostic ability than pulmonary window scans. The average accuracy for mediastinal window scans was 0.933. Our analysis revealed that the radiomic features extracted from mediastinal window scans had the potential to build a prediction model for distinguishing between SCC, lung ADC, and SCLC. The performance of the radiomic signature to diagnose SCC and SCLC in validation cohorts proved effective, with AUC values of 0.869 and 0.859, respectively.
A unique radiomic signature was constructed as a diagnostic factor for different histologic subtypes of lung cancer. Patients with lung cancer may benefit from this proposed radiomic signature.
Keywords: Histologic; Lung Cancer; ROC Curve; Radiomic
Document Type: Research Article
Affiliations: 1: Department of Radiology, Xiang’an Hospital of XiaMen University, XiaMen 361000, Fujian, China 2: The School of Economics, XiaMen University, XiaMen, Fujian, 361000, China 3: Department of Respiratory and Critical Care Medicine, Sichuan Science City Hospital, Mianyang, Sichuan, 621000, China 4: Yang Zhou University, Yangzhou, Jiangsu, 225000, China 5: Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China 6: West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, 610000, China 7: Department of Pathology, Sunning People’s Hospital, Xuzhou, Jiangsu, 221000, China 8: Department of Nephrology, Xuzhou Children’s Hospital, Xuzhou, Jiangsu, 221000, China
Publication date: 01 November 2021
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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