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  • 1
    Online Resource
    Online Resource
    Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 17 ( 2023-08-30), p. 2811-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 17 ( 2023-08-30), p. 2811-
    Abstract: Introduction: Nowadays chemotherapy in breast cancer patients is optionally applied neoadjuvant, which allows for testing of tumor response to the chemotherapeutical treatment in vivo, as well as allowing a greater number of patients to benefit from a subsequent breast-conserving surgery. Material and methods: We compared breast ultrasonography, mammography, and clinical examination (palpation) results with postoperative histopathological findings after neoadjuvant chemotherapy, aiming to determine the most accurate prediction of complete remission and tumor-free resection margins. To this end, clinical and imaging data of 184 patients (193 tumors) with confirmed diagnosis of breast cancer and neoadjuvant therapy were analyzed. Results: After chemotherapy, tumors could be assessed by palpation in 91.7%, by sonography in 99.5%, and by mammography in 84.5% (chi-square p 〈 0.0001) of cases. Although mammography proved more accurate in estimating the exact neoadjuvant tumor size than breast sonography in total numbers (136/163 (83.44%) vs. 142/192 (73.96%), n.s.), 29 tumors could be assessed solely by means of breast sonography. A sonographic measurement was feasible in 192 cases (99.48%) post-chemotherapy and in all cases prior to chemotherapy. Conclusions: We determined a superiority of mammography and breast sonography over clinical palpation in predicting neoadjuvant tumor size. However, neither examination method can predict either pCR or tumor margins with high confidence.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13172811
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 2
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    Online Resource
    Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study
    Elsevier BV ; 2024
    In:  Heliyon Vol. 10, No. 5 ( 2024-03), p. e27515-
    Details
    In: Heliyon, Elsevier BV, Vol. 10, No. 5 ( 2024-03), p. e27515-
    Type of Medium: Online Resource
    ISSN: 2405-8440
    URL: Article
    DOI: 10.1016/j.heliyon.2024.e27515
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2835763-2
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  • 3
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    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)
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    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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  • 4
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    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
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    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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  • 5
    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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  • 6
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    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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  • 7
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    Online Resource
    Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Advances Vol. 3, No. 1 ( 2021-01-01)
    Details
    In: Neuro-Oncology Advances, Oxford University Press (OUP), Vol. 3, No. 1 ( 2021-01-01)
    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” that could be used to differentiate tissue heterogeneity or diagnostic entities. RS has 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 morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up an SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. Results Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on the spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal-appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal-appearing brain tissue can be detected. Conclusion These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
    Type of Medium: Online Resource
    ISSN: 2632-2498
    URL: Article
    DOI: 10.1093/noajnl/vdab077
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 3009682-0
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  • 8
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    Online Resource
    The Therapy of Vulvar Carcinoma—Evaluation of Surgical Options in a Retrospective Monocentric Study
    MDPI AG ; 2023
    In:  Life Vol. 13, No. 10 ( 2023-09-27), p. 1973-
    Details
    In: Life, MDPI AG, Vol. 13, No. 10 ( 2023-09-27), p. 1973-
    Abstract: (1) Background: Surgical-oncological treatment methods are continuously put to the test in times of evidence-based medicine—notably, a constant reevaluation remains key, especially for tumor entities with increasing incidence such as vulvar carcinoma. (2) Methods: In order to determine the postoperative clinical course of different methods of vulvar excision (vulvectomy, hemivulvectomy) as well as inguinal lymph node removal (lymphadenectomy, sentinel lymph node biopsy) with regard to postoperative wound-healingprocess, perioperative hemorrhage, and re-resection rates, we retrospectively analyzed surgical, morphological and laboratory data of 76 patients with a pathological diagnosed vulvar cancer. (3) Results: Analysis of our data from a single center revealed a comparable perioperative clinical course regardless of the chosen method of vulvar excision and inguinal lymph node removal. (4) Conclusions: Thus, our results emphasize the current multimodality in surgical therapy of vulvar carcinoma, in which consideration of known prognostic factors together with the individual patient’s clinical situation allow guideline-based therapy aimed at maximizing surgical safety.
    Type of Medium: Online Resource
    ISSN: 2075-1729
    URL: Article
    DOI: 10.3390/life13101973
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662250-6
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  • 9
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    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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