biophBiomedical PhotonicsBiomedical Photonics2413-9432Non-profit partnership for development of domestic photodynamic therapy and photodiagnosis10.24931/2413-9432-2025-14-4-22-33bioph-758Research ArticleОРИГИНАЛЬНЫЕ СТАТЬИORIGINAL ARTICLESПоиск корреляций в данных спектроскопии комбинационного рассеяния, диффузного отражения и флуоресценции тканей внутричерепных опухолейSearch for correlations in raman, diffuse reflectance, and fluorescence spectroscopy data from intracranial tumorsОспановА.OspanovA.

Москва 

Moscow 

ospanovanuar99@gmail.comРоманишкинИ. Д.RomanishkinI. D.

 

Москва

Moscow 

СавельеваТ. А.SavelievaT. A.

Москва

Moscow 

ШугайС. В.ShugayS. V.

Москва

Moscow 

КосырьковаА. В.KosyrkovaA. V.

Москва

Moscow 

ПавловаГ. В.PavlovaG. V.

Москва

Moscow 

ПронинИ. Н.ProninI. N.

Москва

Moscow 

ЛощеновВ. Б.LoschenovV. B.

Москва

Moscow 

Национальный исследовательский ядерный университет «МИФИ» (Московский инженерно-физический институт)РоссияNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute)Russian FederationИнститут общей физики им. А.М. Прохорова Российской академии наукРоссияProkhorov General Physics Institute of Russian Academy of SciencesRussian FederationНациональный исследовательский ядерный университет «МИФИ» (Московский инженерно-физический институт) ; Институт общей физики им. А.М. Прохорова Российской академии наукРоссияNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute) ; Prokhorov General Physics Institute of Russian Academy of SciencesRussian FederationНациональный медицинский исследовательский центр нейрохирургии имени академика Н. Н. БурденкоРоссияN.N. Burdenko National Medical Research Center of NeurosurgeryRussian FederationНациональный медицинский исследовательский центр нейрохирургии имени академика Н. Н. Бурденко ; Институт высшей нервной деятельности и нейрофизиологии Российской академии наукРоссияN.N. Burdenko National Medical Research Center of Neurosurgery ; Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of SciencesRussian Federation2025120220261442233Copyright © Оспанов А., Романишкин И.Д., Савельева Т.А., Шугай С.В., Косырькова А.В., Павлова Г.В., Пронин И.Н., Лощенов В.Б., 20262026Оспанов А., Романишкин И.Д., Савельева Т.А., Шугай С.В., Косырькова А.В., Павлова Г.В., Пронин И.Н., Лощенов В.Б.Ospanov A., Romanishkin I.D., Savelieva T.A., Shugay S.V., Kosyrkova A.V., Pavlova G.V., Pronin I.N., Loschenov V.B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.pdt-journal.com/jour/article/view/758

Задача построения системы поддержки принятия решений в нейроонкологии на основе оптико-спектрального анализа тканей внутричерепных опухолей сопряжена с некоторыми сложностями, свойственными работе с биомедицинскими данными. Среди них – высокая размерность вектора признаков при относительно малом объеме выборки, пропуски в данных, а также несбалансированность выборок из-за разной частоты встречаемости различных диагнозов. Анализ корреляций между признаками исследуемых опухолей позволит выполнять как восстановление пропусков в данных, так и их аугментацию (искусственное расширение обучающего набора данных путём создания модифицированных версий имеющихся примеров). В данной работе представлен анализ зависимости различных оптико-спектральных характеристик от содержания опухолевых клеток/ткани в образце и взаимных корреляций между различными признаками.

The task of developing a decision support system in neurooncology based on optical-spectral analysis of intracranial tumor tissue is associated with several challenges inherent in working with biomedical data. These include the high dimensionality of the feature vector with a relatively small sample size, data gaps, and sample imbalances due to the varying frequencies of various diagnoses. Analysis of correlations between features of the tumors under study will allow both the restoration of data gaps and their augmentation (artificial expansion of the training dataset by creating modified versions of existing examples). This paper presents an analysis of the dependence of various optical-spectral characteristics on the tumor cell/tissue content in the sample and the cross-correlations between various features.

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The authors declare that there are no conflicts of interest present.