Feasible Method to Assess the Performance of a Lung Cancer CT Screening CAD System in Clinical Practice: Dependence on Nodule Size and Density

DOI: 10.4236/ijmpcero.2014.32016   PDF   HTML   XML   3,236 Downloads   4,956 Views   Citations


Detection of small pulmonary nodules is the goal of lung cancer screening. Computer-aided detection (CAD) systems are recommended to use in lung cancer computed tomography (CT) screening to increase the accuracy of nodule detection. Size and density of lung nodules are primary factors in determining the risk of malignancy. Therefore, purpose of this study is to apply computer-simulated virtual nodules based on the point spread function (PSF) measured in same scanner (maintaining spatial resolution condition) to assess the CAD system performance dependence on nodule size and density. Virtual nodules with density differences between lung background and nodule density (ΔCT) values (200, 300 and 400 HU) and different sizes (4 to 8 mm) were generated and fused on clinical images. CAD detection was performed and free-response receiver operating characteristic (FROC) curves were obtained. Results show that both density and size of virtual nodules can affect detection efficiency. Detailed results are possible to use for quantitative analysis of a CAD system performance. This study suggests that PSF-based virtual nodules could be effectively used to assess the lung cancer CT screening CAD system performance dependence on nodule size and density.

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Marasinghe, J. , Ohkubo, M. , Kobayashi, H. , Murao, K. , Matsumoto, T. , Sone, S. and Wada, S. (2014) Feasible Method to Assess the Performance of a Lung Cancer CT Screening CAD System in Clinical Practice: Dependence on Nodule Size and Density. International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, 3, 107-116. doi: 10.4236/ijmpcero.2014.32016.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] The National Lung Screening Trial Research Team (2011) Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. The New England Journal of Medicine, 365, 395-409.
[2] Way, T., Chan, H.P., Hadjiiski, L., Sahiner, B., Chughtai, A., Song, T.K., Poopat, C., Stojanovska, J., Frank, L., Attili, A., Bogot, N., Cascade, P.N. and Kazerooni, E.A. (2010) Computer-Aided Diagnosis of Lung Nodules on CT Scans: ROC Study of Its Effect on Radiologists’ Performance. Academic Radiology, 17, 323-332.
[3] Das, M., Mühlenbruch, G., Heinen, S., Mahnken, A.H., Salganicoff, M., Stanzel, S., Günther, R.W. and Wildberger, J.E. (2008) Performance Evaluation of a Computer-Aided Detection Algorithm for Solid Pulmonary Nodules in Low-Dose and Standard-Dose MDCT Chest Examinations and Its Influence on Radiologists. British Journal of Radiology, 81, 841-847. http://dx.doi.org/10.1259/bjr/50635688
[4] Roos, J.E., Paik, D., Olsen, D., Liu, E.G., Chow, L.C., Leung, A.N., Mindelzun, R., Choudhury, K.R., Naidich, D.P., Napel, S. and Rubin, G.D. (2010) Computer-Aided Detection (CAD) of Lung Nodules in CT Scans: Radiologist Performance and Reading Time with Incremental CAD Assistance. European Radiology, 20, 549-557.
[5] Zhao, Y., de Bock, G.H., Vliegenthart, R., van Klaveren, R.J., Wang, Y., Bogoni, L., de Jong, P.A., Mali, W.P., van Ooijen, P.M. and Oudkerk, M. (2012) Performance of Computer-Aided Detection of Pulmonary Nodules in Low-Dose CT: Comparison with Double Reading by Nodule Volume. European Radiology, 22, 2076-2084.
[6] Cascio, D., Magro, R., Fauci, F., Iacomi, M. and Raso, G. (2012) Automatic Detection of Lung Nodules in CT Datasets Based on Stable 3D Mass-Spring Models. Computers in Biology and Medicine, 42, 1098-1109.
[7] Kusano, S., Nakagawa, T., Aoki, T., Nawa, T., Nakashima, K., Goto, Y. and Korogi, Y. (2010) Efficacy of Computer-Aided Diagnosis in Lung Cancer Screening with Low-Dose Spiral Computed Tomography: Receiver Operating Characteristic Analysis of Radiologists’ Performance. Japanese Journal of Radiology, 28, 649-655.
[8] Awai, K., Murao, K., Ozawa, A., Komi, M., Hayakawa, H., Hori, S. and Nishimura, Y. (2004) Pulmonary Nodules at Chest CT: Effect of Computer-Aided Diagnosis on Radiologists’ Detection Performance. Radiology, 230, 347-352.
[9] Li, Q., Li, F. and Doi, K. (2008) Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier. Academic Radiology, 15, 165-175.
[10] Sahiner, B., Chan, H.P., Hadjiiski, L.M., Cascade, P.N., Kazerooni, E.A., Chughtai, A.R., Poopat, C., Song, T., Frank, L., Stojanovska, J. and Attili, A. (2009) Academic Radiology, 16, 1518-1530.
[11] Messay, T., Hardie, R.C. and Rogers, S.K. (2010) A New Computationally Efficient CAD System for Pulmonary Nodule Detection in CT Imagery. Medical Image Analysis, 14, 390-406. http://dx.doi.org/10.1016/j.media.2010.02.004
[12] Takizawa, H., Yamamoto, S. and Shiina, T. (2010) Recognition of Pulmonary Nodules in Thorasic CT Scans Using 3D Deformable Object Models of Different Classes. Algorithms, 10, 125-144. http://dx.doi.org/10.3390/a3020125
[13] Retico, A., Delogu, P., Fantacci, M.E., Gori, I. and Preite Martinez, A. (2008) Lung Nodule Detection in Low-Dose and Thin-Slice Computed Tomography. Computers in Biology and Medicine, 38, 525-534.
[14] Naidich, D.P., Bankier, A.A., MacMahon, H., Schaefer-Prokop, C.M., Pistolesi, M., Goo, J.M., Macchiarini, P., Crapo, J.D., Herold, C.J., Austin, J.H. and Travis, W.D. (2013) Recommendations for the Management of Subsolid Pulmonary Nodules Detected at CT: A Statement from the Fleischner Society. Radiology, 266, 304-317.
[15] Christe, A., Leidolt, L., Huber, A., Steiger, P., Szucs-Farkas, Z., Roos, J.E., Heverhagen, J.T. and Ebner, L. (2013) Lung Cancer Screening with CT: Evaluation of Radiologists and Different Computer Assisted Detection Software (CAD) as First and Second Readers for Lung Nodule Detection at Different Dose Levels. European Journal of Radiology, 82, e873-e878.
[16] Ohkubo, M., Wada, S., Kunii, M., Matsumoto, T. and Nishizawa, K. (2008) Imaging of Small Spherical Structures in CT: Simulation Study Using Measured Point Spread Function. Medical & Biological Engineering & Computing, 46, 273-282.
[17] Ohno, K., Ohkubo, M., Marasinghe, J.C., Murao, K., Matsumoto, T. and Wada, S. (2012) Accuracy of Lung Nodule Density on HRCT: Analysis by PSF-Based Image Simulation. Journal of Applied Clinical Medical Physics, 13, 277-292.
[18] Funaki, A., Ohkubo, M., Wada, S., Murao, K., Matsumoto, T. and Niizuma, S. (2012) Application of CT-PSF-Based Computer-Simulated Lung Nodules for Evaluating the Accuracy of Computer-Aided Volumetry. Radiological Physics and Technology, 5, 166-171. http://dx.doi.org/10.1007/s12194-012-0150-9
[19] Kalender, W.A. (2005) Computed Tomography: Fundamentals, System Technology, Image Quality, Applications. 2nd Edition, Erlangen Publicis.
[20] Polacin, A., Kalender, W.A., Brink, J. and Vannier, M.A. (1994) Measurement of Slice Sensitivity Profiles in Spiral CT. Medical Physics, 21, 133-140.
[21] Prevrhal, S., Fox, J.C., Shepherd, J.A. and Genant, H.K. (2003) Accuracy of CT-Based Thickness Measurement of Thin Structures: Modeling of Limited Spatial Resolution in All Three Dimensions. Medical Physics, 30, 1-8.
[22] Ohkubo, M., Wada, S., Kayugawa, A., Matsumoto, T. and Murao, K. (2011) Image Filtering as an Alternative to the Application of a Different Reconstruction Kernel in CT Imaging: Feasibility Study in Lung Cancer Screening. Medical Physics, 38, 3915-3923.
[23] Rollano-Hijarrubia, E., Stokking, R., van der Meer, F. and Niessen, W.J. (2006) Imaging of Small High-Density Structures in CT: A Phantom Study. Academic Radiology, 13, 893-908. http://dx.doi.org/10.1016/j.acra.2006.03.009
[24] Ohkubo, M., Wada, S., Ida, S., Kunii, M., Kayugawa, A., Matsumoto, T., Nishizawa, K. and Murao, K. (2009) Determination of Point Spread Function in Computed Tomography Accompanied with Verification. Medical Physics, 36, 2089-2097.
[25] Ohkubo, M., Wada, S., Matsumoto, T. and Nishizawa, K. (2006) An Effective Method to Verify Line and Point Spread Functions Measured in Computed Tomography. Medical Physics, 33, 2757-2764.
[26] Awai, K., Murao, K., Ozawa, A., Nakayama, Y., Nakaura, T., Liu, D., Kawanaka, K., Funama, Y., Morishita, S. and Yamashita, Y. (2006) Pulmonary Nodules: Estimation of Malignancy at Thin-Section Helical CT—Effect of Computer-Aided Diagnosis on Performance of Radiologists. Radiology, 239, 276-284.
[27] Chakraborty, D.P. (2013) A Brief History of Free-Response Receiver Operating Characteristic Paradigm Data Analysis. Academic Radiology, 20, 915-919.
[28] White, C.S., Pugatch, R., Koonce, T., Rust, S.W. and Dharaiya, E. (2008) Lung Nodule CAD Software as a Second Reader: A Multicenter Study. Academic Radiology, 15, 326-333.
[29] Hwang, J., Chung, M.J., Bae, Y., Shin, K.M., Jeong, S.Y. and Lee, K.S. (2010) Computer-Aided Detection of Lung Nodules: Influence of the Image Reconstruction Kernel for Computer-Aided Detection Performance. Journal of Computer Assisted Tomography, 34, 31-34.
[30] Kim, J.S., Kim, J.H., Cho, G. and Bae, K.T. (2005) Automated Detection of Pulmonary Nodules on CT Images: Effect of Section Thickness and Reconstruction Interval-Initial Results. Radiology, 236, 295-299.
[31] Marten, K., Grillhösl, A., Seyfarth, T., Obenauer, S., Rummeny, E.J. and Engelke, C. (2005) Computer-Assisted Detection of Pulmonary Nodules: Evaluation of Diagnostic Performance Using an Expert Knowledge-Based Detection System with Variable Reconstruction Slice Thickness Settings. European Radiology, 15, 203-212.
[32] Lee, J.Y., Chung, M.J., Yi, C.A. and Lee, K.S. (2008) Ultra-Low-Dose MDCT of the Chest: Influence on Automated Lung Nodule Detection. Korean Journal of Radiology, 9, 95-101.
[33] Godoy, M.C., Kim, T.J., White, C.S., Bogoni, L., de Groot, P., Florin, C., Obuchowski, N., Babb, J.S., Salganicoff, M., Naidich, D.P., Anand, V., Park, S., Vlahos, I. and Ko, J.P. (2013) Benefit of Computer-Aided Detection Analysis for the Detection of Subsolid and Solid Lung Nodules on Thin- and Thick-Section CT. American Journal of Roentgenology, 200, 74-83. http://dx.doi.org/10.2214/AJR.11.7532
[34] Pakdel, A., Mainprize, J.G., Robert, N., Fialkov, J. and Whyne, C.M. (2014) Model-Based PSF and MTF Estimation and Validation from Skeletal Clinical CT Images. Medical Physics, 41, 011906. http://dx.doi.org/10.1118/1.4835515
[35] Sone, S., Hanaoka, T., Ogata, H., Takayama, F., Watanabe, T., Haniuda, M., Kaneko, K., Kondo, R., Yoshida, K. and Honda, T. (2012) Small Peripheral Lung Carcinomas with Five-Year Post-Surgical Follow-Up: Assessment by Semi-Automated Volumetric Measurement of Tumour Size, CT Value and Growth Rate on TSCT. European Radiology, 22, 104-119. http://dx.doi.org/10.1007/s00330-011-2241-0

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