LungCAD: a clinically approved, machine learning system for lung cancer detection

ABSTRACT

We present LungCAD, a computer aided diagnosis (CAD) system that employs a classification algorithm for detecting solid pulmonary nodules from CT thorax studies. We briefly describe some of the machine learning techniques developed to overcome the real world challenges in this medical domain. The most significant hurdle in transitioning from a machine learning research prototype that performs well on an in-house dataset into a clinically deployable system, is the requirement that the CAD system be tested in a clinical trial. We describe the clinical trial in which LungCAD was tested: a large scale multi-reader, multi-case (MRMC) retrospective observational study to evaluate the effect of CAD in clinical practice for detecting solid pulmonary nodules from CT thorax studies. The clinical trial demonstrates that every radiologist that participated in the trial had a significantly greater accuracy with LungCAD, both for detecting nodules and identifying potentially actionable nodules; this, along with other findings from the trial, has resulted in FDA approval for LungCAD in late 2006.

REFERENCES

• 1: American Lung Association. Trends in lung cancer morbidity and mortality report. 2006.
• 2: S. G. Armato-III, M. L. Giger, and H. MacMahon. Automated detection of lung nodules in CT scans: preliminary results. Medical Physics, 28(8):1552--1561, 2001.
• 3: J. Bi and J. Liang. Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007.
• 4: Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao, Computer aided detection via asymmetric cascade of sparse hyperplane classifiers, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150518]
• 5: M. Dundar and J. Bi. Joint optimization of cascaded classifiers for computer aided detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007.
• 6: M. Dundar, G. Fung, J. Bi, S. Sandilya, and R. B. Rao. Sparse fisher discriminant analysis for computer aided detection. In Proceedings of SIAM International Conference on Data Mining, 2005.
• 7: M. Dundar, B. Krishnapuram, J. Bi, and R. B. Rao. Learning classifiers when the training data is not IID. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007.
• 8: Food and Drug Administiration. Siemens Syngo lung CAD summary of safety and effectiveness, PMA No.0500022. October 2006.
• 9: G. Fung, M. Dundar, B. Krishnapuram, and R. B. Rao. Multiple instance algorithms for computer aided diagnosis. In Advances in Neural Information Processing Systems, 2006.
• 10: A. Jemal, R. Siegel, E. Ward, T. Murray, J. Xu, and M. J.Thun. Cancer statistics. CA Cancer J. Clin., 57:4366, 2007.
• 11: D. P. Naidich, J. P. Ko, and J. Stoechek. Computer aided diagnosis: Impact on nodule detection amongst community level radiologist. A multi-reader study. In Proceedings of CARS 2004 Computer Assisted Radiology and Surgery, pages 902--907, 2004.
• 12: N. A. Obuchowski. Nonparametric analysis of clustered roc curve data. Biometrics, 53:170--180, 1997.
• 13: S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, S. J. Mandrekar, S. L. Hillman, A.-M. Sykes, G. L. Aughenbaugh, A. O. Bungum, and K. L. Allen. CT screening for lung cancer: five-year prospective experience. Radiology, 235(1):259--265, 2005.
• 14: V. Vural, G. Fung, B. Krishnapuram, J. Dy, and R. B. Rao. Batch-wise classification with applications to computer aided diagnosis. In Proceedings of European Conference on Machine Learning, 2006.
• 15: M. Wolf, A. Krishnan, M. Salganicoff, J. Bi, M. Dundar, G. Fung, J. Stoeckel, S. Periaswamy, H. Shen, P. Herzog, and D. P. Baidich. CAD performance analysis for pulmonary nodule detection on thin-slice MDCT scans. In H. Lemke, K. Inamura, K. Doi, M. Vannier, and A. Farman, editors, Proceedings of CARS 2005 Computer Assisted Radiology and Surgery, pages 1104--1108, 2005.
• 16: X.-H. Zhou, N. A. Obuchowski, and D. K. McClish. Statistical Methods in Diagnostic Medicine. Wiley, New York, NY, 2002.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.m Miscellaneous

General Terms:
Algorithms

Keywords:
classification, clinical trial, computer aided detection, lung cancer prognosis