Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist’s role and successful observer study

Radiological Physics and Technology

Volumn 8, Issue 2, 2015, Pages 161-173

  Li, Feng ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Computer aided detection (CAD) scheme; New medical image; Observer performance study; Radiologists subjective judgment; Receiver operating characteristic (ROC) analysis

Indexed keywords

BONE LESION; CLINICAL DECISION MAKING; CLINICAL EFFECTIVENESS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC ACCURACY; DIAGNOSTIC RADIOLOGIST; DIAGNOSTIC TEST ACCURACY STUDY; DIFFERENTIAL DIAGNOSIS; DISEASE CLASSIFICATION; FALSE POSITIVE RESULT; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; INTERSTITIAL LUNG DISEASE; LUNG CANCER; LUNG DISEASE; LUNG INFILTRATE; LUNG NODULE; LUNG TUMOR; PNEUMOTHORAX; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; RADIATION DOSE; RECEIVER OPERATING CHARACTERISTIC; REVIEW; SENSITIVITY AND SPECIFICITY; THORAX RADIOGRAPHY; DIAGNOSTIC IMAGING; FACTUAL DATABASE; OBSERVER VARIATION; PROCEDURES; RADIOLOGY; THORAX;

DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSTIC IMAGING; HUMANS; OBSERVER VARIATION; RADIOLOGY; THORAX;

EID: 84937522759     PISSN: 18650333     EISSN: 18650341     Source Type: Journal    
DOI: 10.1007/s12194-015-0319-0     Document Type: Review

References (62)

1. MacMahon H, Vyborny CJ, Metz CE, Doi K, Sabeti V, Solomon SL. Digital radiography of subtle pulmonary abnormalities: an ROC study of the effect of pixel size on observer performance. Radiology. 1986;158:21–6.
2. MacMahon H, Metz CE, Doi K, Kim T, Giger ML, Chan HP. Digital chest: effect on diagnostic accuracy of hard copy, conventional video, and reversed gray scale video display formats. Radiology. 1988;168:669–73.
3. MacMahon H, Doi K, Chan HP, Giger ML, Katsuragawa S, Nakamori N. Computer-aided diagnosis in chest radiology. J Thorac Imag. 1990;5:67–76.
4. Nakamori N, Kunio K, Sabeti S, MacMahon H. Image feature analysis and computer-aided diagnosis in digital radiography: automated analysis of sizes of heart and lung in chest images. Med Phys. 1990;17:342–50.
5. Giger ML, Doi K, MacMahon H, Metz CE, Yin FF. Pulmonary nodules: computer-aided detection in digital chest images. RadioGraphics. 1990;10:41–51.
6. MacMahon H, Sanada S, Doi K, Giger ML, Xu XW, Yin FF, Montner SM, Carlin M. Direct comparison of conventional and computed radiography with a dual-image recording technique. RadioGraphics. 1991;11:259–68.
7. MacMahon H, Doi K, Sanada S, Montner SM, Giger ML, Metz CE, Nakamori N, Yin FF, Xu XW, Yonekawa H, Takeuchi H. Data compression effect on diagnostic accuracy in digital chest radiography. Radiology. 1991;178:175–9.
8. Matsumoto T, Yoshimura H, Giger ML, Doi K, MacMahon H, Montner SM, Nakanishi T. Potential usefulness of computerized nodule detection in screening programs for lung cancer. Invest Radiol. 1992;27:471–5.
9. Abe K, Doi K, MacMahon H, Giger ML, Jia H, Chen X, Kano A, Yanagisawa T. Computer-aided diagnosis in chest radiography: preliminary experience. Invest Radiol. 1993;28:987–93.
10. Hoffmann KR, MacMahon H, Doi K, Metz CE, Yao L, Abe K. Evaluation of enhanced digital film-duplication system by receiver operating characteristic analysis. Invest Radiol. 1993;28:1134–8.
11. Giger ML, Doi K, MacMahon H, Nishikawa RM, Hoffmann KR, Vyborny CJ, Scbmidt RA, Jia H, Abe K, Chen X, Kano A, Katsuragawa S, Yin FF, Alperin N, Metz CE, Beblen FM, Sluis D. An, “intelligent” workstation for computer-aided diagnosis. RadioGraphics. 1993;13:647–56.
12. Xu XW, Kunio K. Image feature analysis for computer-aided diagnosis: accurate detection of ribcage boundary in chest radiographs. Med Phys. 1995;22:617–26.
13. Kobayashi T, Xu XW, MacMahon H, Metz CE, Doi K. Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs. Radiology. 1996;199:843–8.
14. Monnier-Cholley L, MacMahon H, Katsuragawa S, Morishita J, Ishida T, Doi K. Computer-aided diagnosis for detection of interstitial opacities on chest radiographs. AJR. 1998;171:1651–6.
15. Ashizawa K, MacMahon H, Ishida T, Nakamura K, Vyborny CJ, Katsuragawa S, Doi K. Effect of an artificial neural network on radiologists’ performance in the differential diagnosis of interstitial lung disease using chest radiographs. AJR. 1999;172:1311–5.
16. Doi K, MacMahon H, Katsuragawa S, Nishikawa RM, Jiang Y. Computer-aided diagnosis in radiology: potential and pitfalls. EJR. 1999;31:97–109.
17. MacMahon H, Engelmann R, Behlen FM, Hoffmann KR, Ishida T, Roe C, Metz CE, Doi K. Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. Radiology. 1999;213:723–6.
18. Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K, Matsui M, Fujita H, Kodera Y, Doi K. Development of a digital image database for chest radiographs with and without a lung nodules: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR. 2000;174:71–4.
19. Uoshimi U, Nakamura K, Watanabe H, Nakata H, Katsuragawa S, Doi K. ROC analysis of detection of metastatic pulmonary nodules on digital chest radiographs with temporal subtraction. Acad Radiol. 2001;8:871–8.
20. Mtsuki Y, Nakamura K, Watanabe H, Aoki T, Nakata H, Katsuragawa S, Doi K. Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. AJR. 2002;178:657–63.
21. Masutani Y, MacMahon H, Doi K. Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans Med Imag. 2002;21:1517–23.
22. Kakeda S, Nakamura K, Kamada K, Watanabe H, Nakata H, Katsuragawa S, Doi K. Improved detection of lung nodules by using a temporal subtraction technique. Radiology. 2002;224:145–51.
23. Li F, Sone S, Abe H, MacMahon H, Armato SG, Doi K. Missed lung cancers in low-dose helical CT screening obtained from a general population. Radiology. 2002;225:673–83.
24. Armato SG, Li F, Giger ML, MacMahon H, Sone S, Doi K. Performance of automated CT nodule detection on missed cancers from a lung cancer screening program. Radiology. 2002;225:685–92.
25. Shiraishi J, Abe H, Engelmann R, Doi K. Effect of high sensitivity in a computerized scheme for detection extremely subtle solitary pulmonary nodules in chest radiographs. Acad Radiol. 2003;10:1302–11.
26. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang C-T, Suzuki K, Doi K. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys. 2003;30:2440–54.
27. Aoyama M, Li Q, Katsuragawa S, Li F, Sone S, Doi K. Computerized scheme for determination of the likelihood measure of malignance for pulmonary nodules on low-dose CT images. Med Phys. 2003;30:387–94.
28. Li Q, Li F, Shiraishi J, Katsuragawa S, Sone S, Doi K. Investigation of new psychophysical measures for evaluation of similar images on thoracic CT for distinction between benign and malignant nodules. Med Phys. 2003;30:2584–8.
29. Abe H, MacMahon H, Engelmann R, Li Q, Shiraishi J, Katsuragawa S, Aoyama M, Ishida T, Ashizawa K, Metz CE, Doi K. Computer-aided diagnosis in chest radiography: results of large-scale observer tests at 1996–2001 RSNA scientific assemblies. RadioGraphics. 2003;23:255–65.
30. Shiraishi J, Abe H, Engelmann R, Aoyama M, MacMahon H, Doi K. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists’ performance-initial experience. Radiology. 2003;227:469–74.
31. Abe H, Ashizawa K, Li F, Matsuyama N, Fukushima A, Shiraishi J, MacMahon H, Doi K. Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease: results of a simulation test with actual clinical cases. Acad Radiol. 2004;1:29–37.
32. Abe H, Ishida T, Shiraishi J, Li F, Katsuragawa S, Sone S, Macmahon H, Doi K. Effect of temporal subtraction images on radiologists’ detection of lung cancer on CT: Results of the observer performance study with use of film computed tomography images. Acad Radiol. 2004;11:1337–43.
33. Arimura K, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Sone S, Doi K. Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening. Acad Radiol. 2004;11:617–29.
34. Fukushima A, Ashizawa K, Yamaguchi T, Matsuyama N, Hayashi H, Kida I, Imafuku Y, Egawa A, Kimura S, Nagaoki K, Honda S, Katsuragawa S, Doi K, Hayashi K. Application of an artificial neural network to high-resolution CT: usefulness in differential diagnosis of diffuse lung disease. AJR. 2004;183:297–305.
35. Li F, Aoyama H, Shiraishi J, Abe H, Li Q, Suzuki K, Engelmann R, Sone S, MacMahon H, Doi K. Radiologists’ performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. AJR. 2004;183:1209–15.
36. Kakeda S, Moriya J, Sato H, Aoki T, Watanabe H, Nakata H, Oda N, Katsuragawa S, Yamamoto K, Doi K. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR. 2004;182:505–10.
37. Li F, Sone S, Abe H, MacMahon H, Doi K. Comparison of high-resolution CT findings in malignant and benign nodules in CT screening for lung cancer. Radiology. 2004;233:793–8.
38. Armato SG, Roy AS, MacMahon H, Li F, Doi K, Sone S, Altman MB. Evaluation of automated lung nodule detection on low-dose CT scans from a lung cancer screening program. Acad Radiol. 2005;12:337–46.
39. Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol. 2005;78:S3–9.
40. 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 Imag. 2005;24:1138–50.
41. Li F, Arimura H, Suzuki K, Shiraishi J, Li Q, Abe H, Engelmann R, Sone S, MacMahon H, Doi K. Computer-aided diagnosis for detection of missed peripheral lung cancers on CT: ROC and LROC analysis. Radiology. 2005;237:684–90.
42. Shiraishi J, Abe H, Li F, Engelmann R, MacMahon H, Doi K. Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs: ROC analysis of radiologists’ performance. Acad Radiol. 2006;13:995–1003.
43. Li F, Li Q, Engelmann R, Aoyama M, Sone S, MacMahon H, Doi K. Improving radiologists’ recommendations with computer-aided diagnosis for management of small nodules detected by CT. Acad Radiol. 2006;13:943–50.
44. Nie Y, Li Q, Li F, Pu Y, Appelbaum D, Doi K. Integrating PET and CT image information for improvement of diagnostic accuracy of lung nodules: a semi-automatic computer-aided method. J Nucl Med. 2006;47:1075–80.
45. Kasai S, Li F, Shiraishi J, Li Q, Doi K. Computerized detection of vertebral compression fractures on lateral chest radiographs: preliminary results with a tool for early detection of osteoporosis. Med Phys. 2006;33:4664–74.
46. Shiraishi J, Li F, Doi K. Computer-aided diagnosis for improved detection of lung nodules by use of posterior-anterior and lateral chest radiographs. Acad Radiol. 2007;14:28–37.
47. Li Q, Li F, Doi K. Computerized detection of lung nodules in thin-section CT by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol. 2008;15:165–75.
48. Kumazawa S, Muramatsu C, Li Q, Li F, Shiraishi J, Caligiuri P, Schmidt RA, MacMahon H, Doi K. An investigation of radiologists’ perception of lesion similarity: observations with paired breast masses on mammograms and paired lung nodules on CT images. Acad Radiol. 2008;15:887–94.
49. Li F, Engelmann R, Doi K, MacMahon H. Improved detection of small lung cancers with dual-energy subtraction chest radiography. AJR. 2008;190:886–91.
50. Kasai S, Li F, Shiraishi J, Doi K. Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs. AJR. 2008;191:260–5.
51. MacMahon H, Li F, Engelmann R, Roberts R, Armato S. Dual energy subtraction and temporal subtraction chest radiography. J Thorac Imaging. 2008;23(2):77–85.
52. Li F, Engelmann R, Metz CE, Doi K, MacMahon H. Results obtained by a commercial computer-aided detection (CAD) program with missed lung cancers on chest radiographs. Radiology. 2008;246:273–80.
53. Li F, Kumazawa S, Shiraishi J, Li Q, Engelmann R, Caligiuri P, MacMahon H, Doi K. Subjective similarity of patterns of diffuse interstitial lung disease on thin-section CT: an observer performance study. Acad Radiol. 2009;16:477–85.
54. Wang J, Li F, Li Q. Automated segmentation of lungs with severe interstitial lung disease in CT. Med Phys. 2009;36:4592–9.
55. Li F, Hara T, Shiraishi J, Engelmann R, MacMahon H, Doi K. Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: ROC analysis with and without localization. AJR. 2011;196:W535–41.
56. Li F, Engelmann R, Pesce L, Doi K, Metz CE, MacMahon H. Improved detection of small lung cancers by use of bone suppression imaging: comparison with dual-energy subtraction chest radiographs. Radiology. 2011;261:937–49.
57. Li F, Engelmann RM, Pesce L, Armato SG III, MacMahon H. Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol. 2012;12:2729–35.
58. Li F, Engelmann RM, Armato SG III, MacMahon H. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Acad Radiol. 2015;22:475–80.
59. Metz CE, Herman BA, Shen JH. Maximum-likelihood estimation of receiver operating (ROC) curves from continuously distributed data. Stat Med. 1998;17:1033–53.
60. Dorfman DD, Berbaum KS, Metz CE. ROC rating analysis: generalization to the population of readers and cases with the jackknife method. Invest Radiol. 1992;27:723–31.
61. Hillis SL, Berbaun KS, Metz CE. Recent development in the Dorfman–Berbaum–Metz procedure for multireader ROC study analysis. Acad Radiol. 2008;15:647–61.
62. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.