Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

IEEE Reviews in Biomedical Engineering

Volumn 2, Issue , 2009, Pages 136-146

  Lee, Noah ^a   Márquez, Guillermo ^b   Levsky, Jeffrey M ^c   Gohagan, John K ^b  

^a Columbia University ^*  (United States)

^b NATIONAL CANCER INSTITUTE   (United States)

^c ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

Author keywords

Computer aided diagnosis; lung cancer screening; machine learning; receiver operating characteristics

Indexed keywords

COMPUTER ASSISTED DIAGNOSIS; COST BENEFIT ANALYSIS; EARLY DIAGNOSIS; ECONOMICS; HUMAN; LUNG; LUNG TUMOR; MASS SCREENING; METHODOLOGY; RADIOGRAPHY; REVIEW; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SPIRAL COMPUTER ASSISTED TOMOGRAPHY;

COST-BENEFIT ANALYSIS; EARLY DETECTION OF CANCER; HUMANS; LUNG; LUNG NEOPLASMS; MASS SCREENING; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, SPIRAL COMPUTED;

EID: 81555218424     PISSN: 19373333     EISSN: 19411189     Source Type: Journal    
DOI: 10.1109/RBME.2009.2034022     Document Type: Article

References (125)

1. P. M. Marcus, R. M. Fagerstrom, P. C. Prorok, J. K. Gohagan, and B. S. Kramer, “Screening for lung cancer with helical CT scanning,” Clin. Pulmon. Med., vol. 9, pp. 323–329, 2002.
2. M. Aoyama, Q. Li, S. Katsuragawa, H. MacMahon, and K. Doi, “Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images,” Med. Phys., vol. 29, pp. 701–8, 2002.
3. “Lung cancer screening: Recommendation statement,” Ann. Intern. Med., vol. 140, pp. 738–9, 2004.
4. S. Sone et al., “Mass screening for lung cancer with mobile spiral computed tomography scanner,” Lancet, vol. 351, pp. 1242–5, 1998.
5. C. I. Henschke et al., “Early lung cancer action project: Overall design and findings from baseline screening,” Lancet, vol. 354, pp. 99–105, 1999.
6. D. S. Gierada, T. K. Pilgrim, M. Ford, R. M. Fagerstrom, T. R. Church, H. Nath, K. Garg, and D. C. Strollo, “Lung cancer: interob-server agreement on interpretation of pulmonary findings at low-dose CT screening,” Radiol., vol. 246, no. 1, pp. 265–272, 2008.
7. B. S. Kramer and O. W. Brawley, “Cancer screening,” Hematol. Oncol. Clin. North Amer., vol. 14, pp. 831–48, 2000.
8. L. Berlin, “Liability of performing CT screening for coronary artery disease and lung cancer,” Amer. J. Roentgenol., vol. 179, pp. 837–42, 2002.
9. S. J. Swensen et al., “Screening for lung cancer with low-dose spiral computed tomography,” Amer. J. Respir. Crit. Care Med., vol. 165, pp. 508–13, 2002.
10. S. J. Swensen et al., “Lung cancer screening with CT: Mayo clinic experience,” Radiology, vol. 226, pp. 756–61, 2003.
11. C. I. Henschke et al., “CT screening for lung cancer: Suspiciousness of nodules according to size on baseline scans,” Radiology, vol. 231, pp. 164–8, 2004.
12. R. MacRedmond et al., “Screening for lung cancer using low dose CT scanning,” Thorax, vol. 59, pp. 237–41, 2004.
13. J. Gohagan et al., “Baseline findings of a randomized feasibility trial of lung cancer screening with spiral CT scan vs chest radiograph: The lung screening study of the National Cancer Institute,” Chest, vol. 126, pp. 114–21, 2004.
14. B. J. Flehinger, M. Kimmel, and M. R. Melamed, “Survival from early lung cancer: Implications for screening,” Chest, vol. 101, pp. 1013–1018, 1992.
15. L. L. Humphrey, S. Teutsch, and M. Johnson, “Lung cancer screening with sputum cytologic examination, chest radiography, and computed tomography: An update for the U.S. Preventive Services Task Force,” Ann. Intern. Med., vol. 140, pp. 740–53, 2004.
16. J. C. Nesbitt et al., “Survival in early-stage lung cancer,” Ann. Thorac. Surg., vol. 60, pp. 466–472, 1995.
17. W. W. Peterson, T. G. Birdsall, and W. C. Fox, “The theory of signal detectability,” Trans. Prof. Group Inform. Theory, vol. 4, 1954.
18. J. A. Swets, “ROC analysis applied to the evaluation of medical imaging techniques,” Invest. Radiol., vol. 14, pp. 109–21, 1979.
19. R. Sah et al., “Results of surgical treatment of stage I and II lung cancer,” J. Cardiovasc. Surg., vol. 37, pp. 169–172, 1996.
20. R. S. Fontana et al., “Lung cancer screening: The mayo program,” J. Occup. Med., vol. 28, pp. 746–750, 1986.
21. A. P. Reeves and W. J. Kostis, “Computer-aided diagnosis of small pulmonary nodules,” Semin. Ultrasound CT MR, vol. 21, pp. 116–28, 2000.
22. A. P. Reeves and W. J. Kostis, “Computer-aided diagnosis for lung cancer,” Radiol. Clin. North Amer., vol. 38, pp. 497–509, 2000.
23. M. Freedman, “Improved small volume lung cancer detection with computer-aided detection: Database characteristics and imaging of response to breast cancer risk reduction strategies,” Ann. NY Acad. Sci., vol. 1020, pp. 175–89, 2004.
24. M. Kavuru et al., “Three-dimensional imaging of the lung in vivo: Work in progress,” J. Dig. Imag., vol. 4, pp. 139–142, 1991.
25. K. Suzuki, E. Kohda, M. Izutsu, N. Shiraga, and K. Hiramatsu, “3D image, MIP and MPR for evaluation of spiral CT data of the chest,” in Proc. CAR'95, 1995, pp. 1250–1251.
26. C. Schaefer, M. Prokop, and M. Galanski, “Minimum and maximum intensity projections for evaluation of spiral CT data of the chest,” in Proc. Adv. CT III, Berlin, Germany, 1994, pp. 269–275.
27. M. L. Giger, K. T. Bae, and H. MacMahon, “Computerized detection of pulmonary nodules in computed tomography images,” Invest. Radiol., vol. 29, pp. 459–65, 1994.
28. J. Hasegawa, K. Mori, and J. Toriwaki, “Automated extraction of lung cancer lesions from multi-slice chest CT images by using three-dimensional image processing,” Trans. IEICE Jpn., pp. 1587–94, 1993.
29. K. Mori et al., “Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system,” IEEE Trans Med. Imag., vol. 19, pp. 103–14, 2000.
30. P. Croisille et al., “Pulmonary nodules: Improved detection with vascular segmentation and extraction with spiral CT,” Radiology, vol. 197, pp. 397–401, 1995.
31. M. S. Brown et al., “Method for segmenting chest CT image data using an anatomical model: Preliminary results,” IEEE Trans. Med. Imag., vol. 16, pp. 828–39, 1997.
32. D. Y. Kim, J. H. Kim, S. M. Noh, and J. W. Park, “Pulmonary nodule detection using chest CT images,” Acad. Radiol., vol. 44, pp. 252–7, 2003.
33. S. Toshioka et al., “Computer aided diagnosis system for lung cancer based on helical CT images,” Proc. SPIE, vol. 3034, pp. 975–984, 1997.
34. S. G. Armato et al., “Computerized detection of pulmonary nodules on CT scans,” Radiographics, vol. 19, pp. 1303–11, 1999.
35. J. P. Ko and M. Betke, “Chest CT: Automated nodule detection and assessment of change over time-preliminary experience,” Radiology, vol. 218, pp. 267–73, 2001.
36. Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,” IEEE Trans Med. Imag., vol. 20, pp. 595–604, 2001.
37. T. Okhumura et al., “Image processing for computer-aided diagnosis of lung cancer screening system by CT (LSCT),” in Proc. SPIE, 1998, pp. 1314–1322.
38. S. G. Armato, M. L. Giger, and H. MacMahon, “Automated detection of lung nodules in CT scans: Preliminary results,” Med. Phys., vol. 28, pp. 1552–61, 2001.
39. M. N. Gurcan et al., “Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system,” Med. Phys., vol. 29, pp. 2552–8, 2002.
40. K. Suzuki, S. G. Armato, F. L. S. Sone, and K. Doi, “Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography,” Med. Phys., vol. 30, pp. 1602–17, 2003.
41. B. Zhao, G. Gamsu, M. S. Ginsberg, L. Jiang, and L. H. Schwartz, “Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm,” J. Appl. Clin. Med. Phys., vol. 4, pp. 248–60, 2003.
42. P. H. Winston, Artificial Intelligence. New York: Addison-Wesley, 1992.
43. H. Arimura et al., “Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening,” Acad. Radiol., vol. 11, pp. 617–29, 2004.
44. S. G. Armato et al., “Lung cancer: Performance of automated lung nodule detection applied to cancers missed in a CT screening program,” Radiology, vol. 225, pp. 685–92, 2002.
45. Q. Li, S. Katsuragawa, and K. Doi, “Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique,” Med. Phys., vol. 28, pp. 2070–6, 2001.
46. M. S. Brown et al., “Patient-specific models for lung nodule detection and surveillance in CT images,” IEEE Trans Med Imag., vol. 20, pp. 1242–50, 2001.
47. D. Cavouras, P. Prassopoulos, and N. Pantelidis, “Image analysis methods for solitary pulmonary nodule characterization by computed tomography,” Eur. J. Radiol., vol. 14, pp. 169–72, 1992.
48. M. F. McNitt-Gray, N. Wyckoff, J. W. Sayre, J. G. Goldin, and D. R. Aberle, “The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography,” Comput. Med. Imag. Graph., vol. 23, pp. 339–48, 1999.
49. S. Kido, K. Kuriyama, M. Higashiyama, T. Kasugai, and C. Kuroda, “Fractal analysis of small peripheral pulmonary nodules in thin-section CT: Evaluation of the lung-nodule interfaces,” J. Comput. Assist. To-mogr., vol. 26, pp. 573–8, 2002.
50. M. Aoyama et al., “Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images,” Med. Phys., vol. 30, pp. 387–94, 2003.
51. K. Minami et al., “Computerized characterization of contrast enhancement patterns for classifying pulmonary nodules,” IEEE Trans Med. Imag., vol. 2, pp. 897–900, 2001.
52. D. Shaham and L. Guralnik, “The solitary pulmonary nodule: Radiologic considerations,” Semin. Ultrasound CT MR, vol. 21, pp. 97–115, 2000.
53. D. Wormanns, F. Beyer, S. Diederich, K. Ludwig, and W. Heindel, “Diagnostic performance of a commercially available computer-aided diagnosis system for semi-automatic detection of pulmonary nodules: Comparison with single and double reading,” Fortschr. Rontgenstr., vol. 176, pp. 953–8, 2004.
54. I. Sluimer, A. Schilham, M. Prokop, and B. van Ginneken, “Computer analysis of computed tomography scans of the lung: A survey,” IEEE Trans. Med. Imag., vol. 4, pp. 385–405, 2006.
55. D. F. Yankelevitz, A. P. Reeves, W. J. Kostis, B. Zhao, and C. I. Henschke, “Small pulmonary nodules: Volumetrically determined growth rates based on CT evaluation,” Radiology, vol. 217, pp. 251–6, 2000.
56. H. Abe et al., “Computer-aided diagnosis in chest radiography: Results of large-scale observer tests at the 1996–2001 RSNA scientific assemblies,” Radiographics, vol. 23, pp. 255–65, 2003.
57. J. Shiraishi et al., “Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance—Initial experience,” Radiology, vol. 227, pp. 469–74, 2003.
58. K. Kubota et al., “The results in the clinical trial of CAD system for lung cancer using helical CT images,” IEEE Trans Med. Imag., vol. 1, pp. 313–316, 2001.
59. K. Awai et al., “Pulmonary nodules at chest CT: Effect of computer-aided diagnosis on radiologists' detection performance,” Radiology, vol. 230, pp. 347–52, 2004.
60. K. Marten et al., “Computer-assisted detection of pulmonary nodules: Performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists,” Eur. Radiol., vol. 14, pp. 1930–8, 2004.
61. S. Kakeda et al., “Improved detection of lung nodules by using a temporal subtraction technique,” Radiology, vol. 224, pp. 145–51, 2002.
62. M. S. Brown et al., “Lung micronodules: Automated method for detection at thin-section CT—Initial experience,” Radiology, vol. 226, pp. 256–62, 2003.
63. F. Beyer et al., “Clinical evaluation of a software for automated localization of lung nodules at follow-up CT examinations,” Rofo, vol. 176, pp. 829–36, 2004.
64. C. I. Henschke et al., “Computed tomography screening for lung cancer,” Clin. Chest Med., vol. 23, pp. 49–57, 2002.
65. L. P. Clarke, B. Y. Croft, E. Staab, H. Baker, and D. C. Sullivan, “National Cancer Institute Initiative: Lung image database resource for imaging research,” Acad. Radiol., vol. 8, pp. 447–50, 2001.
66. S. G. Armato et al., “Lung image database consortium: Developing a resource for the medical imaging research community,” Radiology, vol. 232, pp. 739–48, 2004.
67. A. K. Ganti and J. L. Mulshin, “Lung cancer screening,” Oncologist, vol. 11, no. 5, pp. 481–487, 2006.
68. I. M. Burger, N. E. Kass, J. H. Sunshine, and S. S. Siegelman, “The use of CT for screening: A national survey of radiologists' activities and attidudes,” Radiology, vol. 248, pp. 160–168, 2008.
69. J. Murtagh et al., “CT and MRI for selected clinical disorders: A systematic review of economic evaluations,” Technol. Rep., no. 68.
70. C. I. Henschke et al., “International early lung cancer action program investigators Survival of patients with stage I lung cancer detected on CT screening,” New Eng. J. Med., vol. 355, no. 17, pp. 1763–1771, 2006.
71. M. Unger, “A pause progress reassessment in lung cancer screening,” New Eng. J. Med., vol. 355, no. 17, pp. 1822–1824, 2006.
72. “No authors listed. Expert panel does not yet endorse CT screening for lung cancer,” Health Techol. Trends, vol. 19, no. 2, pp. 4–5, 2007.
73. C. Black et al., “Population screening for lung cancer using computed tomography is there evidence of clinical effectiveness?,” System. Rev. Lit., Thorax, vol. 62, no. 2, pp. 131–138, 2007.
74. W. C. Black and J. A. Baron, “CT screening for lung cancer: Spiraling into confusion?,” J. Amer. Med. Assoc., vol. 297, no. 9, pp. 995–997, 2007.
75. W. M. Alberts, “Diagnosis and management of lung cancer executive summary,” ACCP Evidence-Based Clinical Practice Guidelines (2nd Ed.), Chest, vol. 132, no. 3, pp. 1S–19S, 2007.
76. World Health Organization, Cancer 2008.
77. R. M. Nishikawa, “Current status and future directions of computer-aided diagnosis in mammography,” Comput. Med. Imag. Graph., vol. 31, pp. 224–235, 2007.
78. National Cancer Institute, A snapshot of lung cancer 2006.
79. T. C. Kennedy, Y. Miller, and S. Prindiville, “Screening for lung cancer revisited and the role of sputum cytology and fluorescence bronchoscopy in a high-risk group,” Chest, vol. 117, no. 4, pp. 725–795, 2000.
80. P. B. Bach et al., “Computed tomography screening and lung cancer outcomes,” J. Amer. Med. Assoc., vol. 297, no. 9, pp. 953–961, 2007.
81. F. Li et al., “Lung cancers missed on chest radiographs: Results obtained with a commercial computer-aided detection program,” Radiology, vol. 246, no. 1, pp. 273–280, 2008.
82. K. Suzuki et al., “False-positive reduction in computer-aided diagnosticscheme for detecting nodules in chest radiographs by means ofmassive training artificial neural network,” Acad. Radiol., vol. 12, no. 2, pp 191–201, 2005.
83. S. Kakeda et al., “Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system,” Amer. J. Roentgenol., vol. 182, no. 2, pp. 505–510, 2004.
84. S. Sakai et al., “Computer-aided nodule detection on digital chest radiography: Validation test on consecutive T1 cases of resectable lung cancer,” J. Dig. Imag., vol. 19, no. 4, pp. 376–382, 2006.
85. J. J. Chen and C. S. White, “Use of CAD to evaluate lung cancer on chest radiography,” J. Thorac Imag., vol. 23, no. 2, pp. 93–6, 2008.
86. H. P. Chan et al., “Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography—A review,” Acad. Radiol., vol. 15, pp. 535–555, 2008.
87. J. Shiraishi et al., “Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance,” Acad. Radiol., vol. 13, no. 8, pp. 995–1003, 2006.
88. S. Wolfl, “Screening of lung cancer samples using gene expression profiling,” Nature Gen., vol. 27, no. 96, 2001.
89. K. Doi, “Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,” Comput. Med. Imag. Graph., vol. 31, no. 4–5, pp. 198–211, 2007.
90. R. R. Bharat et al., “Lungcad: A clinically approved, machine learning system for lung cancer detection,” in Proc. ACM KDD'07, 2007, pp. 1033–1037.
91. A. Depeursinge et al., “Lung tissue classification using wavelet frames,” in Proc. 29th Annu. Int. Conf. IEEE EMBS, 2007, pp. 6259–6262.
92. A. Depeursinge et al., “Lung tissue classification in HRCT data integrating the clinical context,” in Proc. 21st IEEE Int. Symp. Comput.-Based Med. Syst., 2008, pp. 542–547.
93. L. Qiang, “Recent progress in computer-aided diagnosis of lung nodules on thin-section CT,” Comput. Med. Imag. Graph., vol. 31, no. 4–5, pp. 248–257, 2007.
94. I. Petkovska et al., “Pulmonary nodule characterization: A comparison of conventional with quantitative and visual semi-quantitative analysis using contrast enhancement maps original research,” Eur. J. Radiol., vol. 59, no. 2, pp. 244–252, 2006.
95. F. Li et al., “Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy,” Amer. J. Roentgenol., vol. 183, pp. 1209–1215, 2004.
96. Y. Kawata et al., “Hybrid classification approach of malignant and benign pulmonary nodules based on topological and histogram features,” in Proc. MICCAI, 2000, pp. 297–306.
97. J. Shiraishi et al., “Computer-aided diagnosis for improved detection of lung nodules by use of PA and lateral chest radiographs,” Acad. Radiol., vol. 14, no. 1, pp. 28–37, 2007.
98. Y. Arzhaeva et al., “Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography,” Med. Phys., vol. 34, no. 12, pp. 4798–4809, 2007.
99. “Cleveland clinic leading clinical program to improve early-stage lung cancer detection,” in Proc. 93rd Sci. Assemb. Annu. Meeting Radiol. Soc. North Amer.(RSNA), 2007.
100. C. H. Yarbro, M. Goodman, and M. H. Frogge, Cancer Nursing: Principles and Practice. Boston, MA: Jones & Bartlett, 2005.
101. D. G. Altman and J. M. Bland, “Diagnostic tests 1: Sensitivity and specificity,” Br. Med. J., vol. 308, pp. 1552–1552, 1994.
102. M. J. Gardner and D. G. Altman, “Calculating confidence intervals for proportions and their differences,” in Statistics With Confidence, M.J. Gardner and D.G. Altman, Eds. London, U.K.: BMJ, 1989, pp. 28–33.
103. S. G. Baker, “Identifying combinations of cancer markers for further study as triggers of early intervention,” Biometrics, vol. 56, pp. 1082–1087, 2000.
104. S. G. Baker, B. S. Kramer, and S. Srivastava, “Markers for early detection of cancer: Statistical guidelines for nested case-control studies,” BMC Med. Res. Methodol., vol. 2, no. 4, 2002.
105. S. G. Baker and P. F. Pinsky, “A proposed design and analysis for comparing digital and analog mammography: Special receiver operating characteristic methods for cancer screening,” J. Amer. Statist. Assoc., vol. 96, no. 454, pp. 421–428, 2001.
106. M. Barreno, A. A. Cardenas, and J. D. Tygar, “Optimal ROC curve for a combination of classifiers,” in Proc. NIPS, 2007, pp. 57–64.
107. Z. Liu and M. Tan, “ROC-based utility function maximization for feature selection and classification with applications to high-dimensional protease data, biometrics,” J. Int. Biometr. Soc., vol. 64, no. 4, pp. 1155–1161, 2008.
108. M. C. Mozer, R. Dodier, M. D. Colagrosso, C. G. Salcedo, and R. Wolniewicz, “Prodding the ROC curve: Constrained optimization of classifier performance,” in Proc. NIPS, 2001, pp. 1409–1415.
109. S. Dreiseitl, “Training multiclass classifiers by maximizing the volume under the ROC surface,” in Proc. EUROCAST, 2007, vol. 4739, pp. 878–885.
110. M. M. Dundar, G. Fung, B. Krishnapuram, and R. B. Rao, “Multiple-instance learning algorithms for computer-aided detection,” IEEE Trans. Biomed. Eng., vol. 55, pp. 1015–1021, 2008.
111. N. Kato, M. Fukui, and T. Isozaki, “Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease,” in Proc. SPIE Med. Imag. Comput.-Aided Diagnos., 2009, vol. 7260, pp. 72600C-72600C.
112. K. Murphy et al., “A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbor classification,” Med. Image Anal., vol. 13, no. 5, pp. 757–770, 2009.
113. M. Dolejsi, J. Kybic, M. Polovincak, and S. Tuma, “The lung time: Annotated lung nodule dataset and nodule detection framework,” Proc. SPIE Med. Imag. Comput.-Aided Diagnos., vol. 7260, 2009.
114. Y. J. Zheng, C. Kambhamettu, T. Bauer, and K. Steiner, “Accurate estimation of pulmonary nodule's growth rate in CT images with nonrigid registration and precise nodule detection and segmentation,” in Proc. MMBIA, 2009, pp. 101–108.
115. A. El-Baz, G. Gimelfarb, R. Falk, and M. A. El-Ghar, “Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer,” Pattern Recognit., vol. 42, no. 6, pp. 1041–1051, 2009.
116. E. M. van Rikxoort, B. de Hoop, S. van de Vorst, M. Prokop, and B. van Ginneken, “Automatic segmentation of pulmonary segments from volumetric chest CT scans,” Med. Imag., vol. 28, no. 4, pp. 621–630, 2009.
117. J. T. Pu et al., “A computational geometry approach to automated pulmonary fissure segmentation in CT examinations,” Med. Imag., vol. 28, no. 5, pp. 710–719, 2009.
118. S. G. Baker, “The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer,” J. Nat. Cancer Inst., vol. 95, no. 7, pp. 511–515, 2003.
119. S. Kjetil, “Receiver-operating characteristic (ROC) curve analysis in diagnostic, prognostic and predictive biomarker research,” J. Clin. Pathol., vol. 62, no. 1, pp. 1–5, 2009.
120. N. R. Cook, “Statistical evaluation of prognostic versus diagnostic models: Beyond the ROC curve,” Clin. Chem., vol. 54, no. 1, pp. 17–23, 2008.
121. W. Hara, S. G. Soltys, and I. C. Gibbs, “Cyberknife robotic radiosurgery system for tumor treatment,” Expert Rev. Anticancer Ther., vol. 7, no. 11, pp. 1507–1515, 2007.
122. G. Tripepi, J. J. Kitty, W. D. Friedo, and Z. Carmine, “Diagnostic methods 2: Receiver operating characteristic (ROC) curves,” Kidney Int., vol. 76, pp. 252–256, 2009.
123. V. Vapnik, A. Vashist, and N. Pavlovitch, “Learning using hidden information (learning with teacher),” in Proc. Int. Joint Conf. Neural Netw., Atlanta, GA, Jun. 2009, pp. 14–19.
124. K. K. Wong et al., “Hybrid SPECT-CT and PET-CT imaging of differentiated thyroid carcinoma,” Br. J. Radiol., vol. 82, pp. 860–876, 2009.
125. H. Schoeder and M. Goenen, “Screening for cancer with PET and PET/CT: Potential and limitations,” J. Nucl. Med., vol. 48, no. 1, pp. 4–18, 2007.