Evaluation of Automated Mammographic Density Classification in Tomosynthesis: Comparison with Radiologists


Abstract views: 171 / PDF downloads: 52

Authors

DOI:

https://doi.org/10.58600/eurjther2002

Keywords:

Bellus software, mammographic density, automatic

Abstract

Objective: Breast cancer screening is a valuable field of health research conducted through mammography. However, mammography evaluation is the examination with the most frequent lack of to agrement among radiologists. In this study we aimed to show the compatibility of mammographic density classification with a new software, Bellus Breast Density Measurement Software (Option), with visual examination.

Methods: The mammographic density classification of 500 patients was retrospectively determined by five radiologists with varying levels of experience, according to the 5th version of the breast imaging reporting and data system (BIRADS). The mean age of 500 women included in the study was calculated as 53.8±10.08. The obtained data were compared with the Bellus software mammographic density classification of the same patients. Then, the visual evaluation and the compatibility of the Bellus software and the readers were compared.

Results: The agreement between the Bellus software and all five readers was poor (kappa value 0.07-0.12). The agreement of the readers with each other is moderate-good (kappa value 0.054-0.64). The Intraclass Correlation Coefficient (ICC) value for the five separate readers was calculated to be 0.80, indicating good compatibility, while the ICC value for the Bellus software with the five separate readers was calculated to be 0.74, indicating moderate compatibility. The Friedman test revealed that while the mammographic density classification of each reader remained consistent, the classification provided by the Bellus software differed.

Conclusion: Bellus Breast Density Measurement Software (Option) diagnostic accuracy is lower than visual examination. We recommend that the manufacturer develop the software.

Metrics

Metrics Loading ...

References

American Cancer Society. Cancer Facts and Figures 2023. Atlanta, Ga: American Cancer Society. 2023.

Smith RA, Saslow D, Sawyer KA, Burke W, Costanza ME et al. American Cancer Society High-Risk Work Group; American Cancer Society Screening Older Women Work Group; American Cancer Society Mammography Work Group; American Cancer Society Physical Examination Work Group; American Cancer Society New Technologies Work Group; American Cancer Society Breast Cancer Advisory Group. American Cancer Society guidelines for breast cancer screening: update 2003. CA Cancer J Clin. 2003;53(3):141-169. https://doi.org/10.3322/canjclin.53.3.141

Scheel JR, Lee JM, Sprague BL, Lee CI, Lehman CD. Screening ultrasound as an adjunct to mammography in women with mammographically dense breasts. Am J Obstet Gynecol. 2015; 212: 9-17. https://doi.org/10.1016/j.ajog.2014.06.048

Sprague BL, Stout NK, Schechter C, van Ravesteyn NT, Cevik M et al. Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann Intern Med. 2015;62:157-166. https://doi.org/10.7326/M14-0692

Colin C, Schott AM. Re: Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst. 2011;103(1):77. https://doi.org/10.1093/jnci/djq464

Spak DA, Plaxco JS, Santiago L, Dryden MJ, Dogan BE. BI-RADS® fifth edition: A summary of changes. Diagn Interv Imaging. 2017;98(3):179-190. https://doi.org/10.1016/j.diii.2017.01.001.

Jeffreys M, Harvey J, Highnam R. Comparing a new volumetric breast density method (VolparaTM) to cumulus. In: Digital mammography: 2010/2010. Berlin: Springer; 2010.pp408-413. https://doi.org/10.1007/978-3-642-13666-5_55

Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. Automated analysis of mammographic densities. Phys Med Biol 1996;41:909-923. https://doi.org/10.1088/0031-9155/41/5/007

Kerlikowske K, Scott CG, Mahmoudzadeh AP, Ma L, Winham S et al. Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study. Ann Intern Med. 2018;168(11):757-765. https://doi.org/10.7326/M17-3008.

Puliti D, Zappa M, Giorgi Rossi P, Pierpaoli E, Manneschi G et al. Volumetric breast density and risk of advanced cancers after a negative screening episode: a cohort study. Breast Cancer Res. 2018;20(1):95. https://doi.org/10.1186/s13058-018-1025-8

Baker JA, Lo JY. Breast tomosynthesis: state-of-the-art and review of the literature. Acad Radiol. 2011;18(10):1298-1310. https://doi.org/10.1016/j.acra.2011.06.011

Skaane P, Bandos AI, Niklason LT, Sebuødegård S, Østerås BH et al. Digital Mammography versus Digital Mammography Plus Tomosynthesis in Breast Cancer Screening: The Oslo Tomosynthesis Screening Trial. Radiology. 2019:182394. https://doi.org/10.1148/radiol.2019182394

Pattacini P, Nitrosi A, Giorgi Rossi P, Iotti V, Ginocchi V et al. Digital Mammography versus Digital Mammography Plus Tomosynthesis for Breast Cancer Screening: The Reggio Emilia Tomosynthesis Randomized Trial. Radiology. 2018;288(2):375-385. https://doi.org/10.1148/radiol.2018172119

Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B et al. Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. Breast. 2005;14:269-275. https://doi.org/10.1016/j.breast.2004.12.004

Jeffreys M, Harvey J, Highnam R. Comparing a New Volumetric Breast Density Method (Volpara) to Cumulus. In: Martí J, Oliver A, Freixenet J, Martí R, editors. Lecture Notes in Computer Science: 10th International Workshop on Digital Mammography; 2010 Jun 16–18; Girona, Spain: Springer-Verlag; 2010.p.408-413. https://doi.org/10.1007/978-3-642

Regini E, Mariscotti G, Durando M, Ghione G, Luparia A et al. Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice. Radiol Med. 2014;119:741-749. https://doi.org/10.1007/s11547-014-0390-3

Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine. 2016;15(2):155-163. https://doi.org/10.1016/j.jcm.2016.02.012

Brandt KR, Scott CG, Ma L, Mahmoudzadeh AP, Jensen MR et al. Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening. Radiology. 2016;279(3):710-719. https://doi.org/10.1148/radiol.2015151261

Youk JH, Gweon HM, Son EJ, Kim JA. Automated Volumetric Breast Density Measurements in the Era of the BI-RADS Fifth Edition: A Comparison With Visual Assessment. AJR Am J Roentgenol. 2016;206(5):1056-1062. https://doi.org/10.2214/AJR.15.15472

Magni V, Interlenghi M, Cozzi A, Alì M, Salvatore C et al. Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus. Radiology: Artificial Intelligence. 4(2):e210199. https://doi.org/10.1148/ryai.210199

Yoshida R, Yamauchi T, Akashi-Tanaka S, Matsuyanagi M, Taruno K et al. Optimal Breast Density Characterization Using a Three-Dimensional Automated Breast Densitometry System. Current Oncology. 2021;28(6):5384-5394. https://doi.org/10.3390/curroncol28060448

Dontchos BN, Yala A, Barzilay R, Xiang J, Lehman CD. External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice. Academic Radiology. 2021;28(4):475-480. https://doi.org/10.1016/j.acra.2019.12.012

Alomaim W, O'Leary D, Ryan J, Rainford L, Evanoff M, et al. Variability of Breast Density Classification Between US and UK Radiologists. Journal of Medical Imaging and Radiation Sciences. 2019;50(1):53-61. https://doi.org/10.1016/j.jmir.2018.11.002

Alomaim W, O'Leary D, Ryan J, Rainford L, Evanoff M, et al. Subjective Versus Quantitative Methods of Assessing Breast Density. Diagnostics. 2020;10(5):331. https://doi.org/10.3390/diagnostics10050331

Destounis SV, Santacroce A, Arieno A. Update on Breast Density, Risk Estimation, and Supplemental Screening. American Journal of Roentgenology. 2020;214(2):296-305. https://doi.org/10.2214/AJR.19.21994

Li H, Mukundan R, Boyd S. Breast Density Classification Using Multifractal Spectrum with Histogram Analysis. 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), Dunedin, New Zealand. 2019;1-6. http://doi.org/10.1109/IVCNZ48456.2019.8961037

Balleyguier C, Arfi-Rouche J, Boyer B, Gauthier E, Helin V et al. A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations. European Radiology. 2019;29(7):3830-3838. https://doi.org/10.1007/s00330-019-06016-y

Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B et al. Reader variability in reporting breast imaging according to BI-RADS assessment categories (the Florence experience). Breast. 2006;15:44-51. https://doi.org/10.1016/j.breast.2005.04.019

Downloads

Published

2024-03-29

How to Cite

Kızıloğlu, H. A., Beyhan, M., Gökçe, E., Birişik, Y., Battal, M. F., Çeker, M. E., & Demir, O. (2024). Evaluation of Automated Mammographic Density Classification in Tomosynthesis: Comparison with Radiologists. European Journal of Therapeutics, 30(3), 258–266. https://doi.org/10.58600/eurjther2002

Issue

Section

Original Articles

Categories