Abstract
Objectives
The purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice.
Methods
A new automated lung nodule detection and measurement system was developed that incorporates intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. System performance was evaluated against the Lung Imaging Database Consortium (LIDC) CT reference data set.
Results
The test set comprised thin-section CT scans from 108 LIDC subjects. The median (±IQR) sensitivity per subject was 100 (±37.5) for nodules ≥ 4 mm and 100 (±8.33) for nodules ≥ 8 mm. The corresponding false positive rates were 0 (±2.0) and 0 (±1.0), respectively. The concordance correlation coefficient between the CAD nodule diameter and the LIDC reference was 0.91, and for volume it was 0.90.
Conclusions
The new CAD system shows high nodule sensitivity with a low false positive rate. Automated volume measurements have strong agreement with the reference standard. Thus, it provides comprehensive, clinically-usable lung nodule detection and assessment functionality.
Key Points
• CAD requirements can be based on lung cancer screening trial results.
• CAD systems can be evaluated using publically available annotated CT image databases.
• A new CAD system was developed with a low false positive rate.
• The CAD system has reliable measurement tools needed for clinical use.
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Change history
14 November 2019
The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section ���Discussion,��� a sentence was worded incorrectly.
14 November 2019
The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section ���Discussion,��� a sentence was worded incorrectly.
References
Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD (2011) Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N Engl J Med 365:395–409
van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K, Vernhout R, van Iersel CA, van den Bergh KAM, van’t Westeinde S, van der Aalst C, Thunnissen E, Xu DM, Wang Y, Zhao Y, Gietema HA, de Hoop BJ, Groen HJM, de Bock GH, van Ooijen P, Weenink C, Verschakelen J, Lammers JWJ, Timens W, Willebrand D, Vink A, Mali W, de Koning H (2009) Management of Lung Nodules Detected by Volume CT Scanning. N Engl J Med 361:2221–2229
Church TR, Black WC, Aberle DR, Berg CD, Clingan KL, Duan F, Fagerstrom RM, Gareen IF, Gierada DS, Jones GC, Mahon I, Marcus PM, Sicks JD, Jain A, Baum S (2013) Results of Initial Low-Dose Computed Tomographic Screening for Lung Cancer. N Engl J Med 368:1980–1991
Henschke CI, Yip R, Yankelevitz DF, Smith JP (2013) Definition of a positive test result in computed tomography screening for lung cancer: a cohort study. Ann Intern Med 158:246–252
Jin Mo Goo MD (2011) A Computer-Aided Diagnosis for Evaluating Lung Nodules on Chest CT: the Current Status and Perspective. Korean J Radiol 12:145–155, PMCID: PMC3052604
van Ginneken B, Schaefer-Prokop CM, Prokop M (2011) Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261:719–732
Armato SG 3rd, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP, Lung Image Database Consortium Research Group (2004) Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232:739–748
Brown MS, Goldin JG, Suh RD, McNitt-Gray MF, Sayre JW, Aberle DR (2003) Automated Method for Detecting Lung Micronodules on Thin Section CT Images. Radiology 226:256–262
Henschke CI, Yankelevitz DF, Naidich DP, McCauley DI, McGuinness G, Libby DM, Smith JP, Pasmantier MW, Miettinen OS (2004) CT Screening for Lung Cancer: Suspiciousness of Nodules according to Size on Baseline Scans. Radiology 231:164–168
Ochs R, Angel EA, Boedeker KL, Petkovska I, Panknin C, Goldin JG, Aberle DR, McNitt-Gray MF, Brown MS. The influence of CT dose and reconstruction parameters on automated detection of small pulmonary nodules. Medical Imaging 2006: Image Processing, Proceedings of SPIE 2006; 6144: 1886–1893
Cagnon CH, Cody DD, McNitt-Gray MF, Seibert JA, Judy PF, Aberle DR (2006) Description and Implementation of a Quality Control Program in an Imaging-Based Clinical Trial. Acad Radiol 13:1431–1441
Brown MS, McNitt-Gray MF, Mankovich NJ, Goldin JG, Hiller J, Wilson LS, Aberle DR (1997) Method for segmenting chest CT image data using an anatomical model: preliminary results. IEEE Trans Med Imaging 16:828–839
Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR (2001) Patient-specific models for lung nodule detection and surveillance in CT images: preliminary results. IEEE Trans Med Imaging 20:1242–1250
Brown MS, Rogers SR, Goldin JG, Kim HJ, Suh RD, McNitt-Gray MF, Brown K, Gjertson DW, Sayre JW, Batra P, Aberle DR (2005) Computer-Aided Lung Nodule Detection in CT: Results of Large-Scale Observer Test. Acad Radiol 12:681–686
Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255–268
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327:307–310
Boedeker KL, McNitt-Gray MF, Rogers SR, Truong DA, Brown MS, Gjertson DW, Goldin JG (2004) Emphysema: Effect of Reconstruction Algorithm on CT Imaging Measures Reconstruction algorithm effects on CT imaging measures in emphysema patients. Radiology 232:295–301
Murphy K, van Ginneken B, Schilham AMR, de Hoop BJ, Gietema, Prokop M (2009) A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal 13:757–770
Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M, Cataldo R, Cerello P, De Carlo F, Falaschi, Fantacci ME, Gargano G, Kasae P, Torsello M (2009) A novel multithreshold method for nodule detection in lung CT. Med Phys 36:3607–3618
Opfer R, Wiemker R (2007) Performance analysis for computer-aided lung nodule detection on LIDC data. In: Jiang, Yulei, Sahiner, Berkman (Eds.), Proceedings of the SPIE – Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, vol. 6515, p. 65151C
Acknowledgments
The scientific guarantor of this publication is Matthew Brown. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors has significant statistical expertise. Institutional Review Board approval was not required because only publically available de-identified data was used. Written informed consent was not required for this study because only publically available, de-identified data was used. Methodology: retrospective, experimental, performed at one institution.
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Brown, M.S., Lo, P., Goldin, J.G. et al. Toward clinically usable CAD for lung cancer screening with computed tomography. Eur Radiol 24, 2719–2728 (2014). https://doi.org/10.1007/s00330-014-3329-0
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DOI: https://doi.org/10.1007/s00330-014-3329-0