This study aims to explore the clinical value of the computer-aided diagnosis (CAD) system for early detection of the pulmonary nodules on digital chest X-ray. A total of 100 cases of digital chest radiographs with pulmonary nodules of 5-20 mm diameter were selected from Pictures Archiving and Communication System (PACS) database in West China Hospital of Sichuan University were enrolled into trial group, and other 200 chest radiographs without pulmonary nodules as control group. All cases were confirmed by CT examination. Firstly, these cases were diagnosed by 5 different-seniority doctors without CAD, and after three months, these cases were re-diagnosed by the 5 doctors with CAD. Subsequently, the diagnostic results were analyzed by using SPSS statistical methods. The results showed that the sensitivity and specificity for detecting pulmonary nodules tended to be improved by using the CAD system, especially for specificity, but there was no significant difference before and after using CAD system.
Citation：
QINJu, BAIHongli, LIUChang, YUJianqun, ZHANGHongjing, ZHANGZejiang, LIWeimin, ZHANGLizhi. Application of Computer-aided Diagnosis in Early Detection of Pulmonary Nodules Based on Digital Chest Radiograph. Journal of Biomedical Engineering, 2014, 31(5): 1117-1120. doi: 10.7507/1001-5515.20140210
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Copyright © the editorial department of Journal of Biomedical Engineering of West China Medical Publisher. All rights reserved
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- 2. MCWILLIAMS A, LAM B, SUTEDJA T. Early proximal lung cancer diagnosis and treatment[J]. Eur Respir J, 2009, 33(3): 656-665.
- 3. SAGAWA M, ENDO C, SATO M, et al. Four years experience of the survey on quality control of lung cancer screening system in Japan[J]. Lung Cancer, 2009, 63(2): 291-294.
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- 5. DE BOO D W, PROKOP M, UFFMANN M, et al. Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs[J]. Eur J Radiol, 2009, 72(2): 218-225.
- 6. KAKEDA S, MORIYA J, SATO H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system[J]. Am J Roentgenol, 2004, 182(2): 505-510.
- 7. SONG Wei, FAN Li, XIE Yongming, et al. A study of inter-observer variations of pulmonary nodule marking and characterizing on DR images[C]//ECKSTEIN M P, JIANG Yulei. Proc.SPIE 5749,Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment. Bellingham WA: SPIE, 2005, 5749: 272-280.
- 8. FREEDMAN M, OSICKA T. Reader variability: what we can learn from computer-aided detection experiments[J]. J Am Coll Radiol, 2006, 3(6): 446-455.
- 9. SHIRAISHI J, ABE H, ENGELMANN R, et al. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience[J]. Radiology, 2003, 227(2): 469-474.
