Iranian Journal of Radiology

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Comparison of the Diagnostic Performance of Breast Ultrasound and CAD Using BI-RADS Descriptors and Quantitative Variables

Yumi Kim 1 , Bong Joo Kang 2 , * , Jung Min Lee 2 and Sung Hun Kim 2
Authors Information
1 Department of Radiology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
2 Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
Article information
  • Iranian Journal of Radiology: In Press (In Press); e67729
  • Published Online: October 9, 2018
  • Article Type: Research Article
  • Received: February 20, 2018
  • Revised: June 19, 2018
  • Accepted: September 8, 2018
  • DOI: 10.5812/iranjradiol.67729

To Cite: Kim Y, Kang B J, Lee J M, Kim S H. Comparison of the Diagnostic Performance of Breast Ultrasound and CAD Using BI-RADS Descriptors and Quantitative Variables, Iran J Radiol. Online ahead of Print ; In Press(In Press):e67729. doi: 10.5812/iranjradiol.67729.

Abstract
Copyright © 2018, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Patients and Methods
4. Results
5. Discussion
Footnotes
References
  • 1. Kim SM, Han H, Park JM, Choi YJ, Yoon HS, Sohn JH, et al. A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability. J Digit Imaging. 2012;25(5):599-606. doi: 10.1007/s10278-012-9457-7. [PubMed: 22270787]. [PubMed Central: PMC3447099].
  • 2. Yoon JH, Kim MJ, Lee HS, Kim SH, Youk JH, Jeong SH, et al. Validation of the fifth edition BI-RADS ultrasound lexicon with comparison of fourth and fifth edition diagnostic performance using video clips. Ultrasonography. 2016;35(4):318-26. doi: 10.14366/usg.16010. [PubMed: 27184655]. [PubMed Central: PMC5040135].
  • 3. D'Orsi CJ; American College of Radiology. ACR BI-RADS atlas: Breast imaging reporting and data system. ACR, American College of Radiology; 2013.
  • 4. Hong AS, Rosen EL, Soo MS, Baker JA. BI-RADS for sonography: Positive and negative predictive values of sonographic features. AJR Am J Roentgenol. 2005;184(4):1260-5. doi: 10.2214/ajr.184.4.01841260. [PubMed: 15788607].
  • 5. Lee HJ, Kim EK, Kim MJ, Youk JH, Lee JY, Kang DR, et al. Observer variability of breast imaging reporting and data system (BI-RADS) for breast ultrasound. Eur J Radiol. 2008;65(2):293-8. doi: 10.1016/j.ejrad.2007.04.008. [PubMed: 17531417].
  • 6. Kim EK, Ko KH, Oh KK, Kwak JY, You JK, Kim MJ, et al. Clinical application of the BI-RADS final assessment to breast sonography in conjunction with mammography. AJR Am J Roentgenol. 2008;190(5):1209-15. doi: 10.2214/AJR.07.3259. [PubMed: 18430833].
  • 7. Sahiner B, Chan HP, Roubidoux MA, Hadjiiski LM, Helvie MA, Paramagul C, et al. Malignant and benign breast masses on 3D US volumetric images: Effect of computer-aided diagnosis on radiologist accuracy. Radiology. 2007;242(3):716-24. doi: 10.1148/radiol.2423051464. [PubMed: 17244717]. [PubMed Central: PMC2800986].
  • 8. Wang Y, Jiang S, Wang H, Guo YH, Liu B, Hou Y, et al. CAD algorithms for solid breast masses discrimination: Evaluation of the accuracy and interobserver variability. Ultrasound Med Biol. 2010;36(8):1273-81. doi: 10.1016/j.ultrasmedbio.2010.05.010. [PubMed: 20691917].
  • 9. Chabi ML, Borget I, Ardiles R, Aboud G, Boussouar S, Vilar V, et al. Evaluation of the accuracy of a computer-aided diagnosis (CAD) system in breast ultrasound according to the radiologist's experience. Acad Radiol. 2012;19(3):311-9. doi: 10.1016/j.acra.2011.10.023. [PubMed: 22310523].
  • 10. Dromain C, Boyer B, Ferre R, Canale S, Delaloge S, Balleyguier C. Computed-aided diagnosis (CAD) in the detection of breast cancer. Eur J Radiol. 2013;82(3):417-23. doi: 10.1016/j.ejrad.2012.03.005. [PubMed: 22939365].
  • 11. Drukker K, Gruszauskas NP, Sennett CA, Giger ML. Breast US computer-aided diagnosis workstation: Performance with a large clinical diagnostic population. Radiology. 2008;248(2):392-7. doi: 10.1148/radiol.2482071778. [PubMed: 18574139]. [PubMed Central: PMC2797650].
  • 12. Drukker K, Giger ML, Metz CE. Robustness of computerized lesion detection and classification scheme across different breast US platforms. Radiology. 2005;237(3):834-40. doi: 10.1148/radiol.2373041418. [PubMed: 16304105].
  • 13. Lee SE, Moon JE, Rho YH, Kim EK, Yoon JH. Which supplementary imaging modality should be used for breast ultrasonography? Comparison of the diagnostic performance of elastography and computer-aided diagnosis. Ultrasonography. 2017;36(2):153-9. doi: 10.14366/usg.16033. [PubMed: 27764908]. [PubMed Central: PMC5381849].
  • 14. Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-detect to breast masses on ultrasonography: A study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017;36(1):3-9. doi: 10.14366/usg.16012. [PubMed: 27184656]. [PubMed Central: PMC5207353].
  • 15. Lee JH, Seong YK, Chang CH, Ko EY, Cho BH, Ku J, Woo KG. Computer-aided lesion diagnosis in B-mode ultrasound by border irregularity and multiple sonographic features. SPIE Med Imag. 2013;8670. doi: 10.1117/12.2007452.
  • 16. Linda A, Zuiani C, Bazzocchi M, Furlan A, Londero V. Borderline breast lesions diagnosed at core needle biopsy: Can magnetic resonance mammography rule out associated malignancy? Preliminary results based on 79 surgically excised lesions. Breast. 2008;17(2):125-31. doi: 10.1016/j.breast.2007.11.002. [PubMed: 18083514].
  • 17. Sewell CW. Pathology of high-risk breast lesions and ductal carcinoma in situ. Radiol Clin North Am. 2004;42(5):821-30. v. doi: 10.1016/j.rcl.2004.03.013. [PubMed: 15337418].
  • 18. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-74. [PubMed: 843571].
  • 19. Lazarus E, Mainiero MB, Schepps B, Koelliker SL, Livingston LS. BI-RADS lexicon for US and mammography: Interobserver variability and positive predictive value. Radiology. 2006;239(2):385-91. doi: 10.1148/radiol.2392042127. [PubMed: 16569780].
  • 20. American College of Radiology. Breast imaging reporting and data system. American College of Radiology; 2003.
  • 21. Rahbar G, Sie AC, Hansen GC, Prince JS, Melany ML, Reynolds HE, et al. Benign versus malignant solid breast masses: US differentiation. Radiology. 1999;213(3):889-94. doi: 10.1148/radiology.213.3.r99dc20889. [PubMed: 10580971].
  • 22. Cho E, Kim EK, Song MK, Yoon JH. Application of computer-aided diagnosis on breast ultrasonography: Evaluation of diagnostic performances and agreement of radiologists according to different levels of experience. J Ultrasound Med. 2018;37(1):209-16. doi: 10.1002/jum.14332. [PubMed: 28762552].
  • 23. Ko EY. S-detect™ in breast ultrasound: Initial experience. 2014. Available from: http://www.danson.ro/images/Pdf/WhitePaper%20S-Detect.pdf.
  • 24. Jales RM, Sarian LO, Torresan R, Marussi EF, Alvares BR, Derchain S. Simple rules for ultrasonographic subcategorization of BI-RADS(R)-US 4 breast masses. Eur J Radiol. 2013;82(8):1231-5. doi: 10.1016/j.ejrad.2013.02.032. [PubMed: 23540948].
  • 25. Yoon JH, Kim MJ, Moon HJ, Kwak JY, Kim EK. Subcategorization of ultrasonographic BI-RADS category 4: Positive predictive value and clinical factors affecting it. Ultrasound Med Biol. 2011;37(5):693-9. doi: 10.1016/j.ultrasmedbio.2011.02.009. [PubMed: 21458145].
  • 26. Burivong W, Amornvithayacharn O. Accuracy of subcategories A, B, C in BI-RADS 4 lesions by combined mammography and breast ultrasound findings. Afr J Med Med Sci. 2011;2(3):728-33.
  • 27. Kim SM, Kim Y, Jeong K, Jeong H, Kim J. Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography. Ultrasonography. 2018;37(1):36-42. doi: 10.14366/usg.16045. [PubMed: 28618771]. [PubMed Central: PMC5769953].
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