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  • 1
    Online Resource
    Online Resource
    Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-12-08)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-12-08)
    Abstract: Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 $$\pm $$ ± 0.03% and a specificity of 95.44 $$\pm $$ ± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-02977-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 2
    Online Resource
    Online Resource
    CNSC-15. DECIPHERING RAMAN-SPECTRAL TISSUE HETEROGENEITY IN GLIOBLASTOMA
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii25-vii25
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii25-vii25
    Abstract: Raman Spectroscopy is able to provide a fast identification method for healthy and pathological tissues without the need for sample preparation. This makes it highly interesting for intraoperative use in identifying tumor types and borders. However, the capability of a machine-learning classifier to recognize known tissues based on Raman spectra relies on a solid ground truth that is usually provided by measuring small tissue samples first and analyzing the sample later by histopathology. This approach is limited in very closely interspersed tissues that cannot be identified macroscopically, such as glioblastoma where vital tumor tissue, necrosis and peritumoral tissue with characteristic changes to healthy brain tissue are tightly arranged. These areas have been shown to be distinct in their spectral properties, which makes tumor border identification aided by Raman Spectroscopy challenging. As infiltrative tumors leaving no tumor-cell-free brain tissue, borders need to be defined on a clinical scale, where extensive resection must be weighed against retaining functional brain tissue. For this, spectral analysis beyond tumor identification is necessary. In a computational approach we use methods of unsupervised learning and K-means Clustering to investigate heterogeneity within glioblastomas in the native state in order to reduce the complexity of this tumor type. Thereby, it was possible to extract spectra of necrotic tumor areas and to calculate a prediction probability for necrotic tissue within glioblastomas. Several clusters with spectral similarity to each other could be identified, which might represent the different areas of a glioblastoma. Two of these clusters were identified as probable grey and white matter, respectively, by comparison with autopsy tissue. In addition, this assignment independently confirmed the prediction of the classifier as non-necrotic tissue. Deciphering the spectral heterogeneity of glioblastoma in this way may be a great advantage for the surgeon to identify tumor borders and assist in resection control.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.096
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 3
    Online Resource
    Online Resource
    PATH-44. RAMAN SPECTROSCOPY AS A DIAGNOSTIC TOOL IN NEUROPATHOLOGY
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi125-vi125
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi125-vi125
    Abstract: Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has so far been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. METHODS To address this issue, we examined FFPE samples of a broad range of intracranial tumors (e.g. glioblastoma and primary CNS lymphoma) and also different areas of morphologically highly heterogeneous glioblastoma tumor tissue. The latter in order to classify not only the tumor entity but also histologically defined GBM areas according to their spectral properties. We applied linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine) on our spectroscopic data and compared statistical performance of resulting classifiers. RESULTS We found that Random Forest classification distinguished between glioblastoma and primary CNS lymphoma with a balanced accuracy of 94%, only using Raman measurements on FFPE tissue. Furthermore, our established support vector machine-based classifier identified distinct histological areas in glioblastoma such as tumor core and necroses with an overall accuracy of 70.5% and showed a clear separation between the areas of necrosis and peritumoral zone. CONCLUSIONS This relatively cheap and easy-to-apply tool may serve useful to complement histopathological and molecular diagnostics. It provides an unbiased approach to tumor diagnostics with very little requirements (e.g. histopathological feature completeness of the tumor entity) to the sample. As a conclusion, we propose RS as a potential future additional method in the (neuro)-pathological toolbox for tumor diagnostics.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.496
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2094060-9
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 4
    Online Resource
    Online Resource
    PATH-43. RAMAN SPECTROSCOPY AS A TOOL IN NEUROSURGERY
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi125-vi125
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi125-vi125
    Abstract: Raman Spectra have been shown to be sufficiently characteristic to their samples of origin that they can be used in a wide range of applications including distinction of intracranial tumors. While not replacing pathological analysis, the advantage of non-destructive sample analysis and extremely fast feedback make this technique an interesting tool for surgical use. METHODS We sampled intractanial tumors from more than 300 patients at the Centre Hospitalier Luxembourg over a period of three years and compared the spectra of different tumor entities, different tumor subregions and healthy surrounding tissue. We created machine-learning based classifiers that include tissue identification as well as diagnostics. RESULTS To this end, we solved several classes in the intracranial tumor classification, and developed classifiers to distinguish primary central nervous system lymphoma from glioblastoma, which is an important differential diagnosis, as well as meningioma from the surrounding healthy dura mater for identification of tumor tissue. Within glioblastoma, we resolve necrotic, vital tumor tissue and peritumoral infiltration zone.We are currently developing a multi-class classifier incorporating all tissue types measured. CONCLUSIONS Raman Spectroscopy has the potential to aid the surgeon in the surgery theater by providing a quick assessment of the tissue analyzed with regards to both tumor identity and tumor margin identification. Once a reliable classifier based on sufficient patient samples is developed, this may even be integrated into a surgical microscope or a neuronavigation system.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.495
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2094060-9
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    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 5
    Online Resource
    Online Resource
    PATH-01. EVALUATION OF RAMAN SPECTROSCOPY AS A DIAGNOSTIC TOOL IN NEUROPATHOLOGY FOR TUMOR CLASSIFICATION
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii149-vii149
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii149-vii149
    Abstract: Raman spectroscopy (RS) has shown its applicability in neurooncological diagnostics ranging from intraoperative tumor identification to peri- and postoperative tissue analyses. In the present study, we applied RS to track changes in the molecular vibrational status of a broad spectrum of formalin fixed paraffin-embedded (FFPE) intracranial neoplasms (primary brain tumors, meningiomas, brain metastases) and evaluated its potential as an additional method in the neuropathology toolbox, considering specific challenges when employing RS on FFPE tissue. Material and METHODS We examined 82 cases of intracranial neoplasms (679 individual measurements) by RS and applied a machine learning pipeline for recognition of spectral properties. The discrimination potential of the machine learning algorithms was evaluated using standard performance metrics such as AUROC and AUPR values, macro and weighted average of accuracy, precision, recall, and f1 scores. To address occurring misclassifications and further evaluate our models we searched for important Raman bands usable for tumor identification. RESULTS Using our trained machine learning model, we differentiated between different types of gliomas and determined the primary origin in case of a brain metastasis. We further spectroscopically diagnosed tumor types solely based on biopsy fragments of necrosis, something not possible by means of light microscopy. During the validation process we confirmed a high complexity within the spectroscopic data, possibly resulting not only from biological tissue which has undergone a rough chemical procedure but also from residual components of the fixation/paraffination process. CONCLUSIONS Our study demonstrates possibilities and limits of RS as a potential diagnostic tool in neuropathology, considering accompanying difficulties in the vibrational spectroscopic examination of FFPE tissue.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.574
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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    Online Resource
    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
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