Effectiveness of the calcification identification and discrimination module incorporated into the computer aided detection system for mammography: the results of the single-center, prospective, randomised study

Aim: to develop the module of automated calcification identification (MACI) capable to mark the different types of breast calcifications and suitable for incorporation into the computer-aided detection (CAD) system for mammography, as well as to assess its clinical efficiency.

Material and methods. We performed prospective, randomized study included 9078 women who underwent the mammography. All the subjects were randomized (1:1) into the control (CAD) and experimental (CAD + MACI) arms. In the CAD arm the mammography images were processed with the help of CAD MammCheck (with no MACI). In the CAD + MACI arm we used the combined CAD and MACI image processing. After the primary screening completion the subjects were followed for minimum 3 years.

Results. During the visual mammography image analysis in the CAD + MACI и CAD arms 170 (3.74%) и 159 (3.50%; р = 0.3716) breast carcinoma (BC) cases were proven, respectively. After the CAD markings analysis we additionally proven 10 and 6 BC cases, respectively (р = 0.8175). During the subsequent MACI markings analysis in the CAD + MACI arm 7 (0.15%) BC cases were verified. Totally, during the primary screening we found 187 and 165 BC cases, respectively (р = 0.0477). During the 3-year follow-up in the CAD + MACI arm 16 BC cases were proven (0.35%), of them in 2 (0.04%) cases the microcalcifications were found in the area of the subsequently verified BC. In the CAD arm the corresponding values were 22 (0.48%) and 9 (0.20%) BC cases (р = 0.054).

Conclusion. MACI incorporation into the CAD design significantly increases (5.81%) the detection rate of BC associated with microcalcifications at the expense of small (0.89%) increase of the recall rate.

1. Sung H., Ferlay J., Siegel R.L. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: Cancer J. Clin. 2021; 7 (3): 209–249. https://doi.org/10.3322/caac.21660

2. Kaprin A.D., Starinsky V.V., Shakhzadova A.O. The current state of the oncology care in the Russia in 2020. Moscow, 2021. 239 p. (In Russian)

3. Lauby-Secretan B., Scoccianti C., Loomis D. et al. Breast-cancer screening – viewpoint of the IARC Working Group. New Engl. J. Med. 2015; 372 (24): 2353–2358. https://doi.org/10.1056/NEJMsr1504363

4. Gromov A.I., Komin Yu.A., Mozerov S.A., Krasnickaya S.K. Twinkling artifact in differential diagnosis of mammary calcinates. Medical Visualization. 2021; 25 (3): 157–166. https://doi.org/10.24835/1607-0763-1025 (In Russian)

5. Duffy S.W., Tabár L., Yen A. M.-F. et al. Beneficial Effect of Consecutive Screening Mammography Examinations on Mortality from Breast Cancer: A Prospective Study. Radiology. 2021; 299: 541–547. https://doi.org/10.1148/radiol.2021203935

6. Masud R., Al-Rei M., Lokker C. Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review. JMIR Med Inform. 2019; 7 (3): e12660. https://doi.org/10.2196/12660

7. Egoshin I., Pasynkov D., Kolchev A. et al. A segmentation approach for mammographic images and its clinical value (2018). 2017 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2017, 2018-January: 1–6. https://doi.org/10.1109/COMCAS.2017.8244764

8. Pasynkov D.V., Egoshin I.A., Kolchev A.A., et al. Diagnostic Value of 1st and 2nd Generation Computer Aided Detection Systems for Mammography: a Comparative Assessment. Medical Visualization. 2017; 1: 90–102. https://doi.org/10.24835/1607-0763-2017-1-90-102 (In Russian)

9. Pasynkov D.V., Egoshin I.A., Kolchev А.А. et al. The value of computer aided detection system in breast cancer difficult to detect at screening mammography. Russian Electronic Journal of Radiology. 2019; 9 (2): 107–118. https://doi.org/10.21569/2222-7415-2019-9-2-107-118 (In Russian)

10. Duffy S.W., Dibden A., Michalopoulos D. et al. Screen detection of ductal carcinoma in situ and subsequent incidence of invasive interval breast cancers: a retrospective population-based study. Lancet Oncol. 2016; 17 (1): 109–114. https://doi.org/10.1016/S1470-2045(15)00446-5

11. Ward W.H., DeMora L., Handorf E. et al. Preoperative delays in the treatment of DCIS and the associated incidence of invasive breast cancer. Ann. Surg. Oncol. 2020; 27 (2): 386–396. https://doi.org/10.1245/s10434-019-07844-4

12. Horvat J.V., Keating D.M., Rodrigues-Duarte H. et al. Calcifications at Digital Breast Tomosynthesis: Imaging Features and Biopsy Techniques. Radiographics. 2019; 39 (2): 307–318. https://doi.org/10.1148/rg.2019180124

13. Scott R., Kendall C., Stone N. Elemental vs. phase composition of breast calcifications. Sci. Rep. 2017; 7: 136. https://doi.org/10.1038/s41598-017-00183-y

14. Ekpo E., Alakhras M., Brennan P. Errors in Mammography Cannot be Solved Through Technology Alone. Asian Pacific J. Cancer Prevention. 2018; 19 (2): 291–301. https://doi.org/10.22034/APJCP.2018.19.2.291

15. Kuznetsov A.A., Klimova N.V. Possibilities of Using the Program for Detecting Clusters of Microcalcifi cations on Digital Mammograms to Advance Breast Cancer Diagnosis. Vestnik SurGU. Medicina. 2022; 3 (53): 46–50. https://doi.org/10.34822/2304-9448-2022-3-46-50 (In Russian)

16. Khasanov R.Sh., Tukhbatullin M.G., Pasynkov D.V. The value of computer aided detection system for mammography in the breast cancer screening: single-center, prospective, randomized clinical trial. Problems in Oncology. 2021; 67 (6): 777–784. https://doi.org/10.37469/0507-3758-2021-67-6-777-784 (In Russian)