The role of mammography in breast cancer radiomics

Govorukhina, Veronika G.; Георгиевна, Говорухина Вероника; Semenov, Serafim S.; Сергеевич, Семенов Серафим; Gelezhe, Pavel; Борисович, Гележе Павел; Didenko, V. V.; Владимировна, Диденко Вера; Morozov, S. P.; Павлович, Морозов Сергей; Andreychenko, Anna; Евгеньевна, Андрейченко Анна

doi:10.17816/dd70479

Digital Diagnostics

2021

DOI: 10.17816/dd70479

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The role of mammography in breast cancer radiomics

Veronika G. Govorukhina

Говорухина Вероника Георгиевна²,

Serafim S. Semenov

et al.

Abstract: Mammography is still the only screening method for breast cancer. Although digital mammography is the most common and widely used method for detecting breast cancer, it is ineffective at detecting and assessing intratumoral heterogeneity. Due to the small size of the tissue sample or tumor, biopsies often fail to represent the entire tumor. For this reason, selecting a treatment and determining a patients prognosis becomes difficult. In this case, medical imaging is a noninvasive approach that can provide a mo… Show more

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Cited by 3 publications

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Radiomics of fetal magnetic resonance imaging in congenital diaphragmatic hernia

Syrkashev,

Burov,

Podurovskaya

et al. 2023

Medicinskaâ vizualizaciâ

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Objectives. Analysis of possibilities of radiomics as a source of additional diagnostic information about the structural maturity of the lungsMaterials and methods. A retrospective study included 72 pregnant women: 35 with congenital fetal diaphragmatic hernia (group 1) and 37 without fetal lung pathology (group 2). Frontal or co-frontal T2 images (T2 FSE) were obtained. Segmentation of regions of interest at the fetal lung level was performed manually with ITK-Snap. A total of 107 radiomic features were extracted using pyradiomics. The statistical analysis was performed using the STATISTICA 10 statistical analysis package (USA) to detect correlation between trait values and the target variable (presence of lung pathology in CDH), and to show differences in the comparison groups according to the detected parameters.Results. Statistically significant features were identified for 2D and 3D segmentations (p < 0.05). For 2D and 3D segmentations, the number of significant features was 14 and 73, respectively. After exclusion of features with cross-correlations, their number decreased to 6 and 8 for single slices and 3D images, respectively. Correlation coefficients between the features and the presence of lung pathology were also calculated. In the case of 3D images, the number of features with significant correlation coefficients (r > 0.4, p < 0.05) equaled 20, while for single-slice images this number was 3.Conclusion. The data obtained allow to conclude that it is reasonable to use texture analysis of the 3D MRI images as a source of additional diagnostic information concerning the structural maturity of the lungs.

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Radiomics of fetal magnetic resonance imaging in congenital diaphragmatic hernia

Syrkashev,

Burov,

Podurovskaya

et al. 2023

Medicinskaâ vizualizaciâ

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Radiomic Characteristics of Different T<sub>1</sub> Breast Cancer Biotypes

Popova,

Gazhonova,

Demidov

et al. 2023

jour

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Background: Breast cancer (BC) occupies a leading position among my oncological diseases detected in women. Identification and search for predictors of malignant neoplasms using radiation and molecular genetic methods of research allows timely diagnosis and treatment, which improves the prognosis for breast cancer. Purpose: To identify a correlation between the molecular subtype of a breast cancer tumor at an early clinical stage and the patterns of the mammographic method. Methods: A prospective, single-center study of 363 patients diagnosed with breast cancer followed up during 2021. X-ray mammography in two projections, ultrasound-guided trephine biopsy for histological verification, and immunohistochemical (IHC) analysis to determine molecular subtypes were performed. Results: There were statistically significant differences in age between subtypes luminal A, luminal BHER2+ (p < 0.001) and triple negative (p = 0.037), luminal B, luminal BHER2+ (p = 0.001) and triple negative (p = 0.046), luminal BHER2+ and nonluminal HER2+ (p = 0.002), between nonluminal HER2+ and triple negative subtype (p = 0.034). When comparing the structure of radiological density, statistically significant differences were revealed between the subgroups luminal B, luminal BHER2+ (p = 0.010) and triple negative (p = 0.010), between luminal A and triple negative subtypes (p = 0.010). When comparing the leading mammographic symptom (p < 0.001), radiological contours of the formation (p < 0.001), the density of pathological changes (p < 0.001), the size, the newly detected pathological process (p < 0.001) statistically significant differences were also found in the subgroups. A division into groups according to the size of pathological changes within the biotypes was noted, where the aggressive phenotypes of the triple negative subtype (p = 0.001), non-luminal HER2+ (p = 0.02) and luminal B (p = 0.02), in contrast to luminal A, were manifested by a greater extent. the maximum linear size of the tumor. A symptom of nipple retraction (p = 0.048) was described, which was not characteristic of triple negative and non-luminal HER2 cancer. Conclusions: Visualization features of differences in the radiological manifestation of breast cancer of different biological subtypes up to 20 mm can be predictors of molecular subtypes. Pathological verification and IHC study remain a mandatory study, but it may be necessary to conduct an X-ray histological correlation before starting treatment and, if obvious discrepancies are detected, repeat the IHC analysis from the surgical material.

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Artificial Intellect, Radiomics and Radiogenomics

Dykan

Kolotilov

2021

RDRT

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Artificial intellect (AI) is a complex of technological solutions that allows simulating human cognitive functions (including self-learning and finding solutions without a predetermined algorithm) and obtaining, when performing specific tasks, results comparable, at least, to the results of human intellectual activity. The most promising area of AI in medicine, in which technologies have achieved obvious success and are already being actively applied, is the analysis of diagnostic images (X-ray, MRI, CT, PET, SPECT): detection, recognition and identification of various pathologies on them. The purpose of the review is to guide the reader in the potential and problem of AI technologies in radiation diagnosis of human pathologies. The review covers articles that meet the following criteria: the publication is an original research article; the publication is devoted to radiation diagnostics; the publications analyze the use of AI technology in solving the clinical problems of diagnosis, prognosis of a particular pathology; radiology is a potential or actual field of the AI technology application analyzed in the publication. The problem of data verification and marking, radiomics and radiogenomics (as a basis for machine and deep learning of AI), the use of AI in hepatology, limitations and risks of AI application in medicine are considered.

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The role of mammography in breast cancer radiomics

Cited by 3 publications

References 73 publications

Radiomics of fetal magnetic resonance imaging in congenital diaphragmatic hernia

Radiomics of fetal magnetic resonance imaging in congenital diaphragmatic hernia

Radiomic Characteristics of Different T<sub>1</sub> Breast Cancer Biotypes

Artificial Intellect, Radiomics and Radiogenomics

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