Search for correlations in raman, diffuse reflectance, and fluorescence spectroscopy data from intracranial tumors

A. Ospanov; I. D. Romanishkin; T. A. Savelieva; S. V. Shugay; A. V. Kosyrkova; G. V. Pavlova; I. N. Pronin; V. B. Loschenov

doi:10.24931/2413-9432-2025-14-4-22-33

Search for correlations in raman, diffuse reflectance, and fluorescence spectroscopy data from intracranial tumors

A. Ospanov, I. D. Romanishkin, T. A. Savelieva, S. V. Shugay, A. V. Kosyrkova, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

https://doi.org/10.24931/2413-9432-2025-14-4-22-33

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Abstract

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.

Keywords

glial tumor, optical spectroscopy, Raman scattering, diffuse reflectance, fluorescence

About the Authors

A. Ospanov

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation

Moscow

I. D. Romanishkin

Prokhorov General Physics Institute of Russian Academy of Sciences
Russian Federation

Moscow

T. A. Savelieva

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) ; Prokhorov General Physics Institute of Russian Academy of Sciences
Russian Federation

Moscow

S. V. Shugay

N.N. Burdenko National Medical Research Center of Neurosurgery
Russian Federation

Moscow

A. V. Kosyrkova

N.N. Burdenko National Medical Research Center of Neurosurgery
Russian Federation

Moscow

G. V. Pavlova

N.N. Burdenko National Medical Research Center of Neurosurgery ; Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences
Russian Federation

Moscow

I. N. Pronin

N.N. Burdenko National Medical Research Center of Neurosurgery
Russian Federation

Moscow

V. B. Loschenov

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) ; Prokhorov General Physics Institute of Russian Academy of Sciences
Russian Federation

Moscow

References

1. Bailey D., Zacharia B.E. Intraoperative imaging techniques to improve tumor detection in the surgical management of gliomas // Adv. Cancer Res. – 2025. – vol. 166, pp. 103–135. doi: 10.1016/bs.acr.2025.05.001

2. Rynda A.Yu., Olyushin V.E., Rostovtsev D.M., et al. Results of microsurgical resection of glioblastomas under endoscopic and fluorescent control // Biomedical Photonics. – 2024. – vol. 13. – pp. 20-30. doi: 10.24931/2413-9432-2024-13-3-20-30

3. Goryaynov S.A., Okhlopkov V.A., Golbin D.A., et al. Fluorescence Diagnosis in Neurooncology: Retrospective Analysis of 653 Cases // Front. Oncol. – 2019. – vol. 9. – pp. 830. doi: 10.3389/fonc.2019.00830

4. Potapov AA, Goriaĭnov SA, Loshchenov VB, et al. Intraoperative combined spectroscopy (optical biopsy) of cerebral gliomas. Burdenko's Journal of Neurosurgery. – 2013. – vol. 77. – pp. 3-10.

5. Stupak E.V., Glotov V.M., Askandaryan A.S. et al. Raman Spectroscopy in the Diagnosis of Brain Gliomas: A Literature Review // Cureus. – 2025. – vol. 17 – pp. – e79165. doi: 10.7759/cureus.79165.

6. Ospanov A., Romanishkin I., Savelieva T. et al. Optical Differentiation of Brain Tumors Based on Raman Spectroscopy and Cluster Analysis Methods // Int. J. Mol. Sci. – 2023. – vol. 24. – pp. 14432. doi: 10.3390/ijms241914432

7. Gautam, R., Vanga, S., Ariese, F. et al. Review of multidimensional data processing approaches for Raman and infrared spectroscopy // EPJ Techn Instrum. – 2015. – vol. 2. doi: 10.1140/epjti/s40485-015-0018-6

8. Lee J, Huh J. Pathogen Recognition via Surface-Enhanced Raman Scattering: Self-Supervised Learning and Transfer Learning approaches // ChemRxiv. – 2025. – doi:10.26434/chemrxiv-2025-cmr8g.

9. Riva M., Sciortino T., Secoli R. et al. Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples // Cancers (Basel). – 2021. – vol. 13. – pp. 1073. doi: 10.3390/cancers13051073.

10. Savelieva T., Romanishkin I., Ospanov A. et al. Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology // Photonics. – 2025. – vol. 12. – pp. 37. doi: 10.3390/photonics12010037.

11. Savelieva T.A., Romanishkin I.D., Ospanov A. et al. Machine learning methods for spectrally-resolved imaging analysis in neuro-oncology. Biomedical Photonics // 2024. – vol. 13. – pp. 40- 54. doi: 10.24931/2413-9432-2024-13-4-40-54.

12. Nocera G., Sanvito F., Yao J. et al. Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors // J Neurooncol. – 2025. doi: 10.1007/s11060-025-05105-x.

13. Romanishkin I., Savelieva T., Kosyrkova A. et al. Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification // Front Oncol. – 2022. – vol. 12. – pp. 944210. doi: 10.3389/fonc.2022.944210.

14. Romanishkin I.D., Savelieva T.A., Ospanov A. et al. Classification of intracranial tumors based on optical-spectral analysis // Biomedical Photonics. – 2023. – vol. 12. – pp. 4-10. doi: 10.24931/2413-9432-2023-12-3-4-10.

15. Romanishkin I.D., Savelieva T.A., Ospanov A. et al. Comparison of optical-spectral characteristics of glioblastoma at intraoperative diagnosis and ex vivo optical biopsy // Biomedical Photonics. – 2024. – vol. 13. – pp. 4-12. doi: 10.24931/2413-9432-2024-13-4-4-12.

16. Zhou Y, Liu C.-H., Wu B. et al. Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy // Journal of Biomedical Optics. – 2019. – vol. 24. – pp. 095001 doi: 10.1117/1.JBO.24.9.095001.

17. Evans S.M., Judy K.D., Dunphy I. et al., Hypoxia Is Important in the Biology and Aggression of Human Glial Brain Tumors. // Clin Cancer Res. – 2004. – vol.10. – pp. 8177 doi: 10.1158/1078-0432.CCR-04-1081

18. Takano S., Yoshii Y., Kondo S. et al. Concentration of vascular endothelial growth factor in the serum and tumor tissue of brain tumor patients. // Cancer Res. – 1996. – vol. 56. – pp. 2185-2190.

19. Takahashi J.A., Fukumoto M., Igarashi K. et al. Correlation of basic fibroblast growth factor expression levels with the degree of malignancy and vascularity in human gliomas. // J Neurosurg. – 1992. – vol. 76. – pp. 792-798.

20. Scatliff J.H., Radcliffe W.B., Pittman H.H., Park C.H. Vascular structure of glioblastomas. // Am J Roentgenol Radium Ther Nucl Med. – 1969. – vol. 105. – pp. 795-805

21. Weidner N. Tumoural vascularity as a prognostic factor in cancer patients: the evidence continues to grow. // J Pathol. – 1998. – vol. 184. – pp. 119-22.

22. Brusatori M., Auner G., Noh T., et al. Intraoperative Raman Spectroscopy // Neurosurg Clin N Am. – 2017. – vol. 28. – pp. 633- 652. doi: 10.1016/j.nec.2017.05.014.

23. Aydin O., Altas M., Kahraman M. et al. Differentiation of healthy brain tissue and tumors using surface enhanced Raman scattering // Appl Spectrosc. – 2009. – Vol. 63. – pp. 1095–100

24. Mehidine H., Refregiers M., Jamme F. et al. Molecular changes tracking through multiscale fluorescence microscopy differentiate Meningioma grades and non-tumoral brain tissues // Sci Rep. – 2021. – vol. 11. – pp. 3816. doi: 10.1038/s41598-020-78678-4.

25. Reichert D., Wadiura L.I., Erkkilae M.T., et al. Flavin fluorescence lifetime and autofluorescence optical redox ratio for improved visualization and classification of brain tumors // Front Oncol. – 2023. – vol. 20. – pp. 1105648. doi: 10.3389/fonc.2023.1105648.

26. Mazurek M., Szczepanek D., Orzyłowska A., Rola R. Analysis of Factors Affecting 5-ALA Fluorescence Intensity in Visualizing Glial Tumor Cells—Literature Review // International Journal of Molecular Sciences. – 2022. – vol. 23. – pp. 926. doi: 10.3390/ijms23020926.

27. Kemmner W., Wan K., Rüttinger S. et al. Silencing of human ferrochelatase causes abundant protoporphyrin-IX accumulation in colon cancer // FASEB J. – 2008. – vol. 22. – pp. 500–509.

28. Kim S., Kim J.E., Kim Y.H. et al. Glutaminase 2 expression is associated with regional heterogeneity of 5-aminolevulinic acid fluorescence in glioblastoma // Sci. Rep. – 2017. – vol. 7. – pp. 12221.

29. Utsuki S., Oka, H., Fujii K. Intraoperative Photodynamic Diagnosis of Brain Tumors Using 5-Aminolevulinic Acid // In Diagnostic Techniques and Surgical Management of Brain Tumors; Abujamra, A.L., Ed.; InTech: Rijeka, Croatia, 2011. – pp. 227–244

30. Ohgari Y., Nakayasu Y., Kitajima S. et al. Mechanisms involved in δ-aminolevulinic acid (ALA)-induced photosensitivity of tumor cells: Relation of ferrochelatase and uptake of ALA to the accumulation of protoporphyrin // Biochem. Pharmacol. – 2005. – vol. 71. – pp. 42–49.

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For citations:

Ospanov A., Romanishkin I.D., Savelieva T.A., Shugay S.V., Kosyrkova A.V., Pavlova G.V., Pronin I.N., Loschenov V.B. Search for correlations in raman, diffuse reflectance, and fluorescence spectroscopy data from intracranial tumors. Biomedical Photonics. 2025;14(4):22-33. https://doi.org/10.24931/2413-9432-2025-14-4-22-33

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