Bitte benutzen Sie diese Referenz, um auf diese Ressource zu verweisen:
doi:10.22028/D291-43662
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 |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
molecules-29-01167.pdf | 2,58 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons