Abstract
Purpose
The aim of this study was to develop a new diagnostic support system using content-based image-retrieval technology. In this article, we describe the mechanism and preliminary evaluation of this system for use with CT images of solitary pulmonary nodules.
Materials and methods
With the approval of the institutional review board of Shizuoka Cancer Center, we built a database that included CT images of 461 solitary pulmonary nodules. With this database, we developed a system that automatically extracts the pulmonary nodule when the nodule area is clicked, retrieves previous cases based on an image analysis of the extracted lesion, and generates reports of the pulmonary nodule semi-automatically. We compared the percentage of correct diagnoses with and without the system using 30 solitary pulmonary nodules, which were not included in the database, with one radiologist and two residents. As a per-user evaluation, the number of clicks required to extract the nodule region and the extracted regions was compared, and presented candidate cases were evaluated. As an evaluation of the retrieval results, the presented candidate cases were evaluated by comparing the number of diagnostic matches (benign/malignant) between the queries and four presented cases. Additionally, to evaluate the validity of the retrieval technology, the radiologist selected the most similar cases presented by the system and evaluated the visual similarity on a five-point scale.
Results
With this system, the percentage of correct diagnoses for the radiologist improved from 80 to 93%. For the two residents, the diagnostic accuracy improved from 66.7 to 80% and from 76.7 to 90%, respectively. The evaluation of the number of clicks required indicated that for 19 cases with the radiologist and 12 and 11 cases with the two residents, respectively, only one click was required to extract the region. When the extracted regions were compared between the radiologist and the residents, 22 and 19 cases had a Dice’s Coefficient of 0.85 or higher, respectively. For the radiologist, the number of cases that matched the diagnosis (benign/malignant) averaged 3.7 ± 0.5 among 23 malignant cases and 1.7 ± 1.4 among 7 benign cases, while for the residents, these values were 3.6 ± 0.5 and 1.1 ± 0.9, and 3.4 ± 0.8 and 1.1 ± 1.3, respectively. With regard to visual evaluations by the radiologist, there were 15 similar cases and 11 somewhat similar cases.
Conclusion
These results suggest that, despite some differences in the search results among the users, this system has been confirmed that it can improve the accuracy of diagnosis as it displays similar cases at high probability. In addition, with the use of this system, past cases and their reports can be effectively referred to. Therefore, this diagnostic-assistant system has the potential to improve the efficiency of the CT image-reading workflow.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Sethi SK (2008) Radiology in today’s flat world. Internet J Radiol 8(1)
William RH (2006) An opportunity for radiology. Radiology 238(2): 389–394
Giger ML, Huo Z, Vyborny CJ et al (2002) Intelligent CAD workstation for breast imaging using similar to known lesions and multiple visual prompt aids. Med Imaging 4684: 768–773
Takeo H, Shimura K, Imamura K et al (2005) Detection system of clustered microcalcifications on CR mammogram. IEICE Trans Inf Syst E88-D: 2591–2602
Baydush AH, Catarious DM, Abbey CK et al (2003) Computer-aided detection of masses in mammography using subregion Hotelling observers. Med Phys 30(7): 1781–1787
Zhanyu G, Berkman S, Chan HP et al (2003) Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. Med Phys 30(7): 1592–1601
Taylor SA, Greenhaigh R, Ilangovan R et al (2008) CT colonography and computer-aided detection: effect of false-positive results on reader specificity and reading efficiency in a low-prevalence screening population. Radiology 247(1): 133–140
Näppai J, Yoshida H (2003) Feature-guide analysis for reduction of false positives in CAD of polyps for computed tomographic colonography. Med Phys 30(7): 1592–1601
Baker JA, Lo JY, David M et al (2004) Computer-aided detection in screening mammography: variability in cues. Radiology 223(2): 411–417
Li F, Engelmann R, Melz CE et al (2008) Lung cancers missed on chest radiographs: results obtained with a commercial computer-aided detection program. Radiology 246(1): 273–280
Aisen AM, Broderick LS, Winer-Muram H et al (2003) Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. Radiology 228: 265–270
Li Q, Li F, Shiraishi J et al (2003) Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules. Med Phys 30(10): 2584–2592
Strucrad Web site. http://portal.structurad.com/. Accessed August 24 (2006)
Langlotz CP (2000) Structured reporting in radiology. Newsletter of Society for Health Service Research in Radiology; Winter
Endo M, Aramaki T, Asakura K (2009) A semi-automated chest CT diagnosis assistant using content-based retrieval of CT images with structured report. Int J Comput Assist Radiol Surg 4(1): S366
Miyoshi S, Kadokura M, Kondo H, Saito Y, Haniuda M, Fujii Y (2011) 2008 nendo kokyu-ki geka shujutsu toukei (in Japanese). J Jpn Assoc Chest Surg 25(1): 124–132
Li Y, Hara S, Ito W et al (2007) A machine learning approach for interactive lesion segmentation. Proc SPIE 6512: 651246–651248
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3): 297–302
Acknowledgements
The authors thank Drs. Satoshi Igawa and Akihiro Tamiya at the Division of Thoracic Oncology, Shizuoka Cancer Center for their assistance with the reading experiments. The present study was conducted as part of the Fuji Pharma Valley Initiatives, a business-academia collaborative project in Shizuoka Prefecture (http://www.fuji-pvc.jp/).
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Endo, M., Aramaki, T., Asakura, K. et al. Content-based image-retrieval system in chest computed tomography for a solitary pulmonary nodule: method and preliminary experiments. Int J CARS 7, 331–338 (2012). https://doi.org/10.1007/s11548-011-0668-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-011-0668-z