Computerized detection of lung nodules through radiomics
| Autor: | Yacheng Ren, Junfeng Xiong, Ling Fu, Qian Wang, Jun Zhao, Zien Zhou, Jingchen Ma |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Lung Neoplasms Sensitivity and Specificity 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine Humans Diagnosis Computer-Assisted Lung cancer Survival rate Lung Early Detection of Cancer Solitary pulmonary nodule business.industry Cancer Solitary Pulmonary Nodule Nodule (medicine) General Medicine medicine.disease Spiral computed tomography medicine.anatomical_structure 030220 oncology & carcinogenesis Radiographic Image Interpretation Computer-Assisted Radiology medicine.symptom business Nuclear medicine Tomography X-Ray Computed Lung cancer screening |
| Zdroj: | Medical physics. 44(8) |
| ISSN: | 2473-4209 |
| Popis: | 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’ workload 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 1,004 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. This article is protected by copyright. All rights reserved. |
| Databáze: | OpenAIRE |
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