Keywords
How to Cite
Abstract
In order to standardize the description of the breast imaging, the BI-RADS (Breast Imaging Reporting And Data System) imaging system developed by the American College of Radiologists ACR is widely used in world practice. At the same time, numerous visual characteristics of breast lesions with different diagnostic methods complicate the adoption of diagnostic decisions while using the BI-RADS system. The greatest difficulties arise when assessing a variety of multiparametric ultrasound signs of diseases. In this regard, in order to increase the efficiency of these technologies and make fast diagnostic decisions, it becomes relevant to develop a system model based on algorithms using the BI-RADS lexicon.
Materials and methods: from 2017 to 2019 on the basis of the Research Oncology Center named after N.N. Petrov 277 women with various complaints of breast disease were examined using multiparametric ultrasound with elastography and contrast enhancement (2.5 ml Sonovue) on a Hitachi Hi Vision Ascendus ultrasound scanner. The software implementation of the diagnostic decision-making model was carried out using the C # programming language using the Microsoft Visual integrated development environment.
Results: The effectiveness of the developed diagnostic model using the optimal algorithm for the use of various ultrasound technologies in determining the malignancy of the formation showed Sensitivity (Se) = 90.8%, Specificity (Sp) = 95.5%, Positive Predictive Value (PPV) = 88.5%, Negative Predictive Value (NPV) = 96.4%, Accuracy (Ac) = 94.2%. The effectiveness of the developed model in grouping diseases showed Se = 84.2%, Sp = 81.1%, PPV = 62.7%, NPV = 93.1%, Ac = 81.9%.
Conclusions: The proposed system model of the optimal algorithm for making a diagnostic decision based on statistically significant multiparametric ultrasound signs increases the diagnostic efficiency.
References
Mendelson E.B., Böhm-Vélez M., Berg W.A. et al. ACR BI-RADS Ultrasound. In: ACR BI-RADS Atlas, Breast Imaging Reporting and Data System, 5th Edition, American College of Radiology, Reston, VA. 2013:128-130.
D’Orsi C.J., Sickles E.A., Mendelson E.B. et al. ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA, American College of Radiology. 2013.
Dietzel M., Baltzer P.A.T. How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay. Insights Imaging. 2018;(9):325–335. doi: 10.1007/s13244-018-0611-8.
Itoh A., Ueno E., Tohno E. et al. Breast disease: clinical application of US elastography for diagnosis. Radiology. 2006;239(2):341-350. doi:10.1148/radiol.2391041676.
Бусько Е.А., Мищенко А.В., Семиглазов В.В. Определение порогового значения соноэластографического коэффициента жесткости в дифференциальной диагностике доброкачественных и злокачественных образований молочной железы. Кремлевская медицина. Клинический вестник. 2013;1:112-115 [Busko E.A., Mishchenko A.V., Semiglazov V.V. Determination of the cut of the sonoelastographic stiffness coefficient in the differential diagnosis of benign and malignant breast lesions. Kremlin Medicine. Clinical Bulletin. 2013;1:112-115 (In Russ.)].
Бусько Е.А. Паттерны контрастного ультразвукового исследования молочной железы. Радиология-Практика. 2017;4:6-17 [Busko E.A. Patterns of contrast ultrasound examination of the breast. Radiology-Practice. 2017;4:6-17 (In Russ.)].
Гончарова А.Б., Аржаник А.А. Сравнение способов преобразования количественных данных в бинарные при предсказании рисков осложнений внебольничной пневмонии. Процессы управления и устойчивость. 2020;7(1):148-152 [Goncharova A.B. Arzhanik A.A. Comparison of methods for converting quantitative data into binary data in predicting the risks of complications of community-acquired pneumonia. Control processes and stability. 2020;7(1):148-152 (In Russ.)].
Бейли Н. Математика в биологии и медицине. М.: Мир. 1970:327 [Bailey N. Mathematics in biology and medicine / N. Bailey. M.: Mir;1970:327 (In Russ.)].
Гончарова А.Б. Постановка предварительного медицинского диагноза на основе теории нечетких множеств с использованием меры Сугено. Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. 2019;15(4):529–543. doi: https://doi.org/10.21638/11702/spbu10.2019.409 [Goncharova A. B. Formulation of a preliminary medical diagnosis based on the theory of fuzzy sets using the Sugeno measure. Bulletin of St. Petersburg University. Applied Mathematics. Computer science. Management processes. 2019;15(4):529–543. doi:https://doi.org/10.21638/11702/spbu10.2019.409 (In Russ.)].
Cai Z. et al. Values of contrast-enhanced ultrasound combined with BI-RADS in differentiating benign and malignant breast lesions. Int J Clin Exp Med. 2018;11(11):11957-11964.
Kapetas P., Clauser P., Woitek R. et al. Quantitative multiparametric breast ultrasound: application of contrast-enhanced ultrasound and elastography leads to an improved differentiation of benign and malignant lesions. Investigative radiology. 2019;54(5):257-264. doi.org/10.1097/RLI.0000000000000543.
Li J., Liping Guo, Li Yin et al. Can different regions of interest influence the diagnosis of benign and malignant breast lesions using quantitative parameters of contrast-enhanced sonography? European journal of radiology. 2018;108:1-6. doi:10.1016/j.ejrad.2018.09.005.
Cheng R., Li J., Ji L., Liu H. et al. Comparison of the diagnostic efficacy between ultrasound elastography and magnetic resonance imaging for breast masses. Experimental and therapeutic medicine. 2018;15(3):2519-2524. doi: 10.3892/etm.2017.5674.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
© АННМО «Вопросы онкологии», Copyright (c) 2020