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Assessing Tissue Fixation Time and Quality with Label-free Mid Infrared Spectroscopy and Machine Learning.

Daniel R Bauer¹, David R Chafin¹

¹Roche Diagnostics Solutions, Pathology Research and Early Development (Ventana Medical Systems, Inc.), Tucson, Arizona, USA.

Biopreservation and Biobanking

|December 14, 2022

Summary

This summary is machine-generated.

Formaldehyde fixation alters biospecimen molecular composition, changing mid-infrared (MID-IR) spectra. This study developed a predictive model to determine tissue fixation time, enhancing diagnostic quality.

Keywords:

cancer diagnostics immunohistochemistry machine learning predictive modeling raman spectroscopy tissue preanalytics

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Area of Science:

Biomedical science
Spectroscopy
Computational pathology

Background:

Formaldehyde fixation is crucial for tissue preservation but can alter molecular composition.
Standardizing tissue quality is essential for accurate clinical diagnostics.

Purpose of the Study:

To investigate if formaldehyde fixation impacts mid-infrared (MID-IR) spectra.
To develop an analytical method for determining tissue fixation time and quality.

Main Methods:

Scanning unstained formalin-fixed paraffin-embedded tissues with MID-IR microscopy.
Identifying fixation-specific spectral patterns to build a predictive model.
Training and validating a machine-learning model on over 100 tissue samples.

Main Results:

Formaldehyde crosslinking significantly alters MID-IR spectra, particularly the Amide I band.
The developed machine-learning model accurately predicts tissue fixation time with an average accuracy of 1.4 hours.
Spectroscopic predictions correlated with histological stain quality (bcl-2, FOXP3, ki-67).

Conclusions:

Predicting tissue fixation status from spectral data is feasible, even for samples with unknown preanalytics.
This technique can standardize clinical tissue diagnostics by ensuring optimal tissue quality.
Improved tissue quality assessment leads to more accurate patient treatment decisions.