Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification
The neurosurgery of intracranial tumors is often complicated by the difficulty of distinguishing tumor center, infiltration area, and normal tissue. The current standard for intraoperative navigation is fluorescent diagnostics with a fluorescent agent. This approach can be further enhanced by measur...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.944210/full |
| _version_ | 1818050018102214656 |
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| author | Igor Romanishkin Tatiana Savelieva Tatiana Savelieva Alexandra Kosyrkova Vladimir Okhlopkov Svetlana Shugai Arseniy Orlov Alexander Kravchuk Sergey Goryaynov Denis Golbin Galina Pavlova Galina Pavlova Igor Pronin Victor Loschenov Victor Loschenov |
| author_facet | Igor Romanishkin Tatiana Savelieva Tatiana Savelieva Alexandra Kosyrkova Vladimir Okhlopkov Svetlana Shugai Arseniy Orlov Alexander Kravchuk Sergey Goryaynov Denis Golbin Galina Pavlova Galina Pavlova Igor Pronin Victor Loschenov Victor Loschenov |
| author_sort | Igor Romanishkin |
| collection | DOAJ |
| description | The neurosurgery of intracranial tumors is often complicated by the difficulty of distinguishing tumor center, infiltration area, and normal tissue. The current standard for intraoperative navigation is fluorescent diagnostics with a fluorescent agent. This approach can be further enhanced by measuring the Raman spectrum of the tissue, which would provide additional information on its composition even in the absence of fluorescence. However, for the Raman spectra to be immediately helpful for a neurosurgeon, they must be additionally processed. In this work, we analyzed the Raman spectra of human brain glioblastoma multiforme tissue samples obtained during the surgery and investigated several approaches to dimensionality reduction and data classificatin to distinguish different types of tissues. In our study two approaches to Raman spectra dimensionality reduction were approbated and as a result we formulated new technique combining both of them: feature filtering based on the selection of those shifts which correspond to the biochemical components providing the statistically significant differences between groups of examined tissues (center of glioblastoma multiforme, tissues from infiltration area and normally appeared white matter) and principal component analysis. We applied the support vector machine to classify tissues after dimensionality reduction of registered Raman spectra. The accuracy of the classification of malignant tissues (tumor edge and center) and normal ones using the principal component analysis alone was 83% with sensitivity of 96% and specificity of 44%. With a combined technique of dimensionality reduction we obtained 83% accuracy with 77% sensitivity and 92% specificity of tumor tissues classification. |
| first_indexed | 2024-12-10T10:46:48Z |
| format | Article |
| id | doaj.art-fec05bfe309b4ebd8fc74a3c90fa9cc0 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-10T10:46:48Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-fec05bfe309b4ebd8fc74a3c90fa9cc02022-12-22T01:52:08ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-09-011210.3389/fonc.2022.944210944210Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classificationIgor Romanishkin0Tatiana Savelieva1Tatiana Savelieva2Alexandra Kosyrkova3Vladimir Okhlopkov4Svetlana Shugai5Arseniy Orlov6Alexander Kravchuk7Sergey Goryaynov8Denis Golbin9Galina Pavlova10Galina Pavlova11Igor Pronin12Victor Loschenov13Victor Loschenov14Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, RussiaProkhorov General Physics Institute of the Russian Academy of Sciences, Moscow, RussiaNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaInstitute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, RussiaProkhorov General Physics Institute of the Russian Academy of Sciences, Moscow, RussiaNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, RussiaThe neurosurgery of intracranial tumors is often complicated by the difficulty of distinguishing tumor center, infiltration area, and normal tissue. The current standard for intraoperative navigation is fluorescent diagnostics with a fluorescent agent. This approach can be further enhanced by measuring the Raman spectrum of the tissue, which would provide additional information on its composition even in the absence of fluorescence. However, for the Raman spectra to be immediately helpful for a neurosurgeon, they must be additionally processed. In this work, we analyzed the Raman spectra of human brain glioblastoma multiforme tissue samples obtained during the surgery and investigated several approaches to dimensionality reduction and data classificatin to distinguish different types of tissues. In our study two approaches to Raman spectra dimensionality reduction were approbated and as a result we formulated new technique combining both of them: feature filtering based on the selection of those shifts which correspond to the biochemical components providing the statistically significant differences between groups of examined tissues (center of glioblastoma multiforme, tissues from infiltration area and normally appeared white matter) and principal component analysis. We applied the support vector machine to classify tissues after dimensionality reduction of registered Raman spectra. The accuracy of the classification of malignant tissues (tumor edge and center) and normal ones using the principal component analysis alone was 83% with sensitivity of 96% and specificity of 44%. With a combined technique of dimensionality reduction we obtained 83% accuracy with 77% sensitivity and 92% specificity of tumor tissues classification.https://www.frontiersin.org/articles/10.3389/fonc.2022.944210/fullglioblastoma multiformeraman spectroscopydimensionality reductionprincipal component analysisbiochemical componentsoptical biopsy |
| spellingShingle | Igor Romanishkin Tatiana Savelieva Tatiana Savelieva Alexandra Kosyrkova Vladimir Okhlopkov Svetlana Shugai Arseniy Orlov Alexander Kravchuk Sergey Goryaynov Denis Golbin Galina Pavlova Galina Pavlova Igor Pronin Victor Loschenov Victor Loschenov Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification Frontiers in Oncology glioblastoma multiforme raman spectroscopy dimensionality reduction principal component analysis biochemical components optical biopsy |
| title | Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification |
| title_full | Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification |
| title_fullStr | Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification |
| title_full_unstemmed | Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification |
| title_short | Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification |
| title_sort | differentiation of glioblastoma tissues using spontaneous raman scattering with dimensionality reduction and data classification |
| topic | glioblastoma multiforme raman spectroscopy dimensionality reduction principal component analysis biochemical components optical biopsy |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.944210/full |
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