ArticleSovremennye tekhnologii v meditsine2022
Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion.
Article in Sovremennye tekhnologii v meditsine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.Journal of clinical medicine · 2025Review
- Radiomics and Quantitative MDA Criteria in Breast Cancer with Bone Metastases by MRI: Examples of Calculation Algorithms and Their Practical Use.Sovremennye tekhnologii v meditsine · 2024Article
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Authors and funding
2 authors.
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Abstract
Using mathematic criteria for image processing (radiomics) makes it possible to more accurately assess the nature of therapy-associated changes and determine the sites of maximal response. Comparison of the acquired quantitative and clinical data may assist radiologists in making the optimal decision. Materials and Methods: MRI data of three patients with breast cancer T Results: The structure of the metastatically modified spine was analysed segment by segment in the sagittal and axial projections using machine image analysis operators. Rapid changes in the "complexity" of vertebrae images have been found, allowing one to suggest the efficacy of treatment in one of the three options - stabilization, improvement, progression. Changes in the vertebrae structure with a positive response to the treatment in the form of the formation of bone objects, calderas, reduction of the contrast agent circulation at the microlevel, confirmed by mathematical analysis, have been monitored. A correlation was obtained between the established changes and the level of the CA 15-3 cancer marker. Conclusion: The study has shown a high effectiveness of machine image analysis algorithms, high correlation of the obtained results with the radiologist's report and clinical and laboratory data in 9 cases out of 12. The Pearson correlation coefficient between the classical marker and matrix filter curve was 0.8.
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