Jingchen Ma1, Zien Zhou2, Yacheng Ren1, Junfeng Xiong1, Ling Fu1, Qian Wang1, Jun Zhao1. 1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. 2. Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, 200127, China.
Abstract
PURPOSE: Lung cancer is a major cause of cancer deaths, and the 5-year survival rate of stage IV lung cancer patients is only 2%. However, the 5-year survival rate of stage I lung cancer patients significantly increases to 50%. As such, spiral computed tomography (CT) scans are necessary to diagnose high-risk lung cancer patients in early stages. In this study, a computer-aided detection (CAD) system with radiomics was proposed. This system could automatically detect pulmonary nodules and reduce radiologists' workloads and human errors. METHODS: In the proposed scheme, a nodular enhancement filter was used to segment nodule candidates and extract radiomic features. A synthetic minority over-sampling technique was also applied to balance the samples, and a random forest method was utilized to distinguish between real nodules and false positive detections. The radiomics approach quantified intratumor heterogeneity and multifrequency information, which are highly correlated with lung nodules. RESULTS: The proposed method was used to evaluate 1004 CT cases from the well-known Lung Image Database Consortium, and 88.9% sensitivity with four false positive detections per CT scan was obtained by randomly selecting 502 cases for training and 502 other cases for testing. CONCLUSIONS: The proposed scheme yielded a high performance on the LIDC database. Therefore, the proposed scheme is possibly effective for various CT configurations used in routine diagnosis and lung cancer screening.
PURPOSE:Lung cancer is a major cause of cancer deaths, and the 5-year survival rate of stage IV lung cancerpatients is only 2%. However, the 5-year survival rate of stage I lung cancerpatients significantly increases to 50%. As such, spiral computed tomography (CT) scans are necessary to diagnose high-risk lung cancerpatients in early stages. In this study, a computer-aided detection (CAD) system with radiomics was proposed. This system could automatically detect pulmonary nodules and reduce radiologists' workloads and human errors. METHODS: In the proposed scheme, a nodular enhancement filter was used to segment nodule candidates and extract radiomic features. A synthetic minority over-sampling technique was also applied to balance the samples, and a random forest method was utilized to distinguish between real nodules and false positive detections. The radiomics approach quantified intratumor heterogeneity and multifrequency information, which are highly correlated with lung nodules. RESULTS: The proposed method was used to evaluate 1004 CT cases from the well-known Lung Image Database Consortium, and 88.9% sensitivity with four false positive detections per CT scan was obtained by randomly selecting 502 cases for training and 502 other cases for testing. CONCLUSIONS: The proposed scheme yielded a high performance on the LIDC database. Therefore, the proposed scheme is possibly effective for various CT configurations used in routine diagnosis and lung cancer screening.