Performance of radiologists in detection of small pulmonary nodules on chest radiographs: Effect of rib suppression with a massive-training artificial neural network

American Journal of Roentgenology

Volumn 193, Issue 5, 2009, Pages

  Oda, Seitaro ^a   Awai, Kazuo ^a   Suzuki, Kenji ^b   Yanaga, Yumi ^a   Funama, Yoshinori ^c   MacMahon, Heber ^b   Yamashita, Yasuyuki ^a  

^a KUMAMOTO UNIVERSITY   (Japan)

^b UNIVERSITY OF CHICAGO   (United States)

^c KUMAMOTO UNIVERSITY   (Japan)

Author keywords

Chest radiography; Massive training artificial neural network; Pulmonary nodules; Receiver operating characteristic; Rib suppressed image

Indexed keywords

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CERTIFICATION; CONTROLLED STUDY; DATA BASE; DIGITAL IMAGING; FEMALE; HUMAN; HUMAN TISSUE; JOB PERFORMANCE; LUNG NODULE; MAJOR CLINICAL STUDY; MALE; MEDICAL SOCIETY; OBSERVER VARIATION; PRIORITY JOURNAL; RADIOLOGIST; RANDOM SAMPLE; RECEIVER OPERATING CHARACTERISTIC; RESIDENT; RIB; RIB SUPPRESSION; THORAX; THORAX RADIOGRAPHY; TRAINING;

ADULT; AGED; AGED, 80 AND OVER; CLINICAL COMPETENCE; FEMALE; HUMANS; INSERVICE TRAINING; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); RADIOGRAPHY, THORACIC; RADIOLOGY; ROC CURVE; SOLITARY PULMONARY NODULE;

EID: 70449516643     PISSN: 0361803X     EISSN: None     Source Type: Journal    
DOI: 10.2214/AJR.09.2431     Document Type: Article

References (28)

1. Fontana RS, Sanderson DR, Taylor WF, et al. Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Mayo Clinic study. Am Rev Respir Dis 1984;130:561-565 (Pubitemid 15200005)
2. Håkansson M, Båth M, Börjesson S, et al. Nodule detection in digital chest radiography: summary of the RADIUS chest trial. Radiat Prot Dosimetry 2005; 114:114-120 (Pubitemid 40883664)
3. Kaneko M, Eguchi K, Ohmatsu H, et al. Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 1996; 201:798-802 (Pubitemid 26392633)
4. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999; 354:99-105
5. Shah PK, Austin JH, White CS, et al. Missed nonsmall cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. Radiology 2003; 226:235-241
6. Naidich D, Zerhouni E, Siegelman S. Computed tomography of the thorax. New York, NY: Raven, 1984:171-199
7. Sone S, Takashima S, Li F, et al. Mass screening for lung cancer with mobile spiral computed tomography scanner. Lancet 1998; 351:1242-1245 (Pubitemid 28181964)
8. Woodring JH. Pitfalls in the radiologic diagnosis of lung cancer. AJR 1990; 154:1165-1175 (Pubitemid 20179456)
9. Tsubamoto M, Kuriyama K, Kido S, et al. Detection of lung cancer on chest radiographs: analysis on the basis of size and extent of ground-glass opacity at thin-section CT. Radiology 2002; 224:139-144 (Pubitemid 34680385)
10. Ide K, Mogami H, Murakami T, Yasuhara Y, Miyagawa M, Mochizuki T. Detection of lung cancer using single-exposure dual-energy subtraction chest radiography. Radiat Med 2007; 25:195-201 (Pubitemid 351928046)
11. Ishigaki T, Sakuma S, Ikeda M. One-shot dualenergy subtraction chest imaging with computed radiography: clinical evaluation of film images. Radiology 1988; 168:67-72 (Pubitemid 18153943)
12. Uemura M, Miyagawa M, Yasuhara Y, et al. Clinical evaluation of pulmonary nodules with dualexposure dual-energy subtraction chest radiography. Radiat Med 2005; 23:391-397 (Pubitemid 41441898)
13. Kakeda S, Nakamura K, Kamada K, et al. Improved detection of lung nodules by using a temporal subtraction technique. Radiology 2002;224:145-151 (Pubitemid 34680386)
14. Suzuki K, Shiraishi J, Abe H, MacMahon H, Doi K. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol 2005; 12:191-201 (Pubitemid 40269219)
15. Suzuki K, Armato SG 3rd, Li F, Sone S, Doi K. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys 2003; 30:1602-1617 (Pubitemid 36885778)
16. Arimura H, Katsuragawa S, Suzuki K, et al. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol 2004;11:617-629 (Pubitemid 38685165)
17. Suzuki K, Li F, Sone S, Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging 2005; 24:1138-1150 (Pubitemid 41337154)
18. Suzuki K, Yoshida H, Nappi J, Dachman AH. Massive-training artificial neural network (MTANN) for reduction of false positives in computer- aided detection of polyps: suppression of rectal tubes. Med Phys 2006; 33:3814-3824 (Pubitemid 44505106)
19. Suzuki K, Yoshida H, Nappi J, Armato SG 3rd, Dachman AH. Mixture of expert 3D massivetraining ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med Phys 2008; 35:694-703 (Pubitemid 351190889)
20. Suzuki K, Abe H, MacMahon H, Doi K. Imageprocessing technique for suppressing ribs in chest radiographs by means of massive training artifi- cial neural network (MTANN). IEEE Trans Med Imaging 2006; 25:406-416
21. Shiraishi J, Katsuragawa S, Ikezoe J, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. AJR 2000; 174:71-74 (Pubitemid 30030212)
22. Metz CE, Herman BA, Shen JH. Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. Stat Med 1998; 17:1033-1053
23. Li F, Engelmann R, Metz CE, Doi K, MacMahon H. Lung cancers missed on chest radiographs: results obtained with a commercial computeraided detection program. Radiology 2008; 246:273-280 (Pubitemid 350305053)
24. Kuhlman JE, Collins J, Brooks GN, Yandow DR, Broderick LS. Dual-energy subtraction chest radiography: what to look for beyond calcified nodules. RadioGraphics 2006; 26:79-92 (Pubitemid 43235003)
25. Li F, Engelmann R, Doi K, MacMahon H. Improved detection of small lung cancers with dualenergy subtraction chest radiography. AJR 2008;190:886-891 (Pubitemid 351571484)
26. Tsubamoto M, Johkoh T, Kozuka T, et al. Temporal subtraction for the detection of hazy pulmonary opacities on chest radiography. AJR 2002;179:467-471 (Pubitemid 34785654)
27. Kakeda S, Moriya J, Sato H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR 2004; 182:505-510 (Pubitemid 38116601)
28. Kuriyama K, Seto M, Kasugai T, et al. Groundglass opacity on thin-section CT: value in differentiating subtypes of adenocarcinoma of the lung. AJR 1999; 173:465-469 (Pubitemid 29347378)