Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion.

Autor: Mirizzi G; National Department of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg.; Saarland University Medical Center and Faculty of Medicine, 66421 Homburg, Germany., Jelke F; National Department of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg.; Saarland University Medical Center and Faculty of Medicine, 66421 Homburg, Germany.; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1445 Strassen, Luxembourg., Pilot M; Department of Medicine IV, LMU University Hospital, LMU Munich, 80539 Munich, Germany., Klein K; Saarland University Medical Center and Faculty of Medicine, 66421 Homburg, Germany., Klamminger GG; Department of General and Special Pathology, Saarland University Medical Center (UKS), Saarland University (USAAR), 66424 Homburg, Germany.; National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg., Gérardy JJ; National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg.; Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg., Theodoropoulou M; Department of Medicine IV, LMU University Hospital, LMU Munich, 80539 Munich, Germany., Mombaerts L; National Department of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg.; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4365 Esch-sur-Alzette, Luxembourg., Husch A; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4365 Esch-sur-Alzette, Luxembourg., Mittelbronn M; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1445 Strassen, Luxembourg.; National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg.; Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg.; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4365 Esch-sur-Alzette, Luxembourg.; Department of Life Science and Medicine (DLSM), University of Luxembourg (UL), 4365 Esch-sur-Alzette, Luxembourg., Hertel F; National Department of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg.; Saarland University Medical Center and Faculty of Medicine, 66421 Homburg, Germany., Kleine Borgmann FB; Saarland University Medical Center and Faculty of Medicine, 66421 Homburg, Germany.; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1445 Strassen, Luxembourg.; Hôpitaux Robert Schuman, 2540 Luxembourg, Luxembourg.
Jazyk: angličtina
Zdroj: Molecules (Basel, Switzerland) [Molecules] 2024 Mar 06; Vol. 29 (5). Date of Electronic Publication: 2024 Mar 06.
DOI: 10.3390/molecules29051167
Abstrakt: Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples' classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.
Databáze: MEDLINE
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