RATIONALE AND OBJECTIVES: To compare the effectiveness of a new computational scheme for pulmonary nodule detection in computed tomography images against human observers. MATERIALS AND METHODS: The study involved evaluation of 81 potential nodules by four radiologists. Each radiologist separately evaluated the potential nodules and provided a confidence level for the presence of pulmonary nodules. Their performance was compared with that of the new computational scheme by mixture distribution analysis. RESULTS: Mixture distribution analysis of the results of the four radiologists demonstrated a relative proportion agreement of 0.84. The kappa statistic was used to compare the agreement of the computational scheme with the results of the four radiologists. A kappa value of .65 (se = .11) was shown to be significantly different from chance (P = .99). CONCLUSION: The new computational scheme correlates well with the radiologists' subjective rankings of pulmonary nodules on computed tomography scans and may prove a useful tool in the evaluation of algorithms for the screening and diagnosis of lung cancer.
RATIONALE AND OBJECTIVES: To compare the effectiveness of a new computational scheme for pulmonary nodule detection in computed tomography images against human observers. MATERIALS AND METHODS: The study involved evaluation of 81 potential nodules by four radiologists. Each radiologist separately evaluated the potential nodules and provided a confidence level for the presence of pulmonary nodules. Their performance was compared with that of the new computational scheme by mixture distribution analysis. RESULTS: Mixture distribution analysis of the results of the four radiologists demonstrated a relative proportion agreement of 0.84. The kappa statistic was used to compare the agreement of the computational scheme with the results of the four radiologists. A kappa value of .65 (se = .11) was shown to be significantly different from chance (P = .99). CONCLUSION: The new computational scheme correlates well with the radiologists' subjective rankings of pulmonary nodules on computed tomography scans and may prove a useful tool in the evaluation of algorithms for the screening and diagnosis of lung cancer.
Authors: Samuel G Armato; Michael F McNitt-Gray; Anthony P Reeves; Charles R Meyer; Geoffrey McLennan; Denise R Aberle; Ella A Kazerooni; Heber MacMahon; Edwin J R van Beek; David Yankelevitz; Eric A Hoffman; Claudia I Henschke; Rachael Y Roberts; Matthew S Brown; Roger M Engelmann; Richard C Pais; Christopher W Piker; David Qing; Masha Kocherginsky; Barbara Y Croft; Laurence P Clarke Journal: Acad Radiol Date: 2007-11 Impact factor: 3.173