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Titel: Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion
VerfasserIn: Mirizzi, Giulia
Jelke, Finn
Pilot, Michel
Klein, Karoline
Klamminger, Gilbert Georg
Gérardy, Jean-Jacques
Theodoropoulou, Marily
Mombaerts, Laurent
Husch, Andreas
Mittelbronn, Michel
Hertel, Frank
Kleine Borgmann, Felix Bruno
Sprache: Englisch
Titel: Molecules : a journal of synthetic chemistry and natural product chemistry
Bandnummer: 29
Heft: 5
Verlag/Plattform: MDPI
Erscheinungsjahr: 2024
Freie Schlagwörter: Raman spectroscopy
machine learning
tumor bank
formalin fixation
cryopreservation
DDC-Sachgruppe: 610 Medizin, Gesundheit
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: 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.
DOI der Erstveröffentlichung: 10.3390/molecules29051167
URL der Erstveröffentlichung: https://www.mdpi.com/1420-3049/29/5/1167
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-436622
hdl:20.500.11880/39123
http://dx.doi.org/10.22028/D291-43662
ISSN: 1420-3049
Datum des Eintrags: 5-Dez-2024
Fakultät: M - Medizinische Fakultät
Fachrichtung: M - Neurochirurgie
M - Pathologie
Professur: M - Keiner Professur zugeordnet
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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