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Савельева, Татьяна Александровна

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Инженерно-физический институт биомедицины
Цель ИФИБ и стратегия развития – это подготовка высококвалифицированных кадров на базе передовых исследований и разработок новых перспективных методов и материалов в области инженерно-физической биомедицины. Занятие лидерских позиций в биомедицинских технологиях XXI века и внедрение их в образовательный процесс, что отвечает решению практикоориентированной задачи мирового уровня – диагностике и терапии на клеточном уровне социально-значимых заболеваний человека.
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Савельева
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Татьяна Александровна
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Тема: Infrared Spectroscopy ×

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    CLASSIFICATION OF INTRACRANIAL TUMORS BASED ON OPTICAL-SPECTRAL ANALYSIS КЛАССИФИКАЦИЯ ВНУТРИЧЕРЕПНЫХ ОПУХОЛЕИ НА ОСНОВЕ ОПТИКО-СПЕКТРАЛЬНОГО АНАЛИЗА
    (2023) Romanishkin, I. D.; Savelieva, T. A.; Ospanov A.; Loschenov, V. B.; Савельева, Татьяна Александровна; Лощенов, Виктор Борисович
    The motivation for the present study was the need to develop methods of urgent intraoperative biopsy during surgery for removal of intracranial tumors. Based on the experience of previous joint work of GPI RAS and N.N. Burdenko National Medical Research Center of Neurosurgery to introduce fluorescence spectroscopy methods into clinical practice, an approach combining various optical-spectral techniques, such as autofluorescence spectroscopy, fluorescence of 5-ALA induced protoporphyrin IX, diffuse reflection of broadband light, which can be used to determine hemoglobin concentration in tissues and their optical density, Raman spectroscopy, which is a spectroscopic method that allows detection of various molecules in tissues by vibrations of individual characteristic molecular bonds. Such a variety of optical and spectral characteristics makes it difficult for the surgeon to analyze them directly during surgery, as it is usually realized in the case of fluorescence methods – tumor tissue can be distinguished from normal with a certain degree of certainty by fluorescence intensity exceeding a threshold value. In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. Our earlier statistical analysis of the optical-spectral features allowed identifying statistically significant spectral ranges for analysis of diagnostically important tissue components. Studies of dimensionality reduction techniques of the optical-spectral feature vector and methods of clustering of the studied samples also allowed us to approach the implementation of the automatic classification method. Importantly, the classification task can be used in two applications – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. This paper presents the results of our research in the first direction. We investigated the combination of several methods and showed the possibility of differentiating glial and meningeal tumors based on the proposed optical-spectral analysis method.
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    Optical Differentiation of Brain Tumors Based on Raman Spectroscopy and Cluster Analysis Methods
    (2023) Ospanov, A.; Romanishkin, I.; Savelieva, T.; Kosyrkova, A.; Loschenov, V.; Савельева, Татьяна Александровна; Лощенов, Виктор Борисович
    In the present study, various combinations of dimensionality reduction methods with data clustering methods for the analysis of biopsy samples of intracranial tumors were investigated. Fresh biopsies of intracranial tumors were studied in the Laboratory of Neurosurgical Anatomy and Preservation of Biological Materials of N.N. Burdenko Neurosurgery Medical Center no later than 4 h after surgery. The spectra of Protoporphyrin IX (Pp IX) fluorescence, diffuse reflectance (DR) and Raman scattering (RS) of biopsy samples were recorded. Diffuse reflectance studies were carried out using a white light source in the visible region. Raman scattering spectra were obtained using a 785 nm laser. Patients diagnosed with meningioma, glioblastoma, oligodendroglioma, and astrocytoma were studied. We used the cluster analysis method to detect natural clusters in the data sample presented in the feature space formed based on the spectrum analysis. For data analysis, four clustering algorithms with eight dimensionality reduction algorithms were considered.