Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity
Date
2022
Authors
Ali-Adeeb, Ramie N.
Shreeves, Phil
Deng, Xinchen
Milligan, Kirsty
Brolo, Alexandre G.
Lum, Jullian J.
Haston, Christina
Andrews, Jeffrey L.
Jirasek, Andrew
Journal Title
Journal ISSN
Volume Title
Publisher
PLOS One
Abstract
Objective:
In this work, we explore and develop a method that uses Raman spectroscopy to measure
and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation
for a predictive disease model.
Methods:
Analysis of Raman tissue data is achieved through a combination of techniques. We first
distinguish between tissue measurements and air pockets in the lung by using group and
basis restricted non-negative matrix factorization. We then analyze the tissue spectra using
sparse multinomial logistic regression to discriminate between fibrotic gradings. Model validation
is achieved by splitting the data into a training set containing 70% of the data and a
test set with the remaining 30%; classification accuracy is used as the performance metric.
We also explore several other potential classification tasks wherein the response considered
is the grade of pneumonitis and fibrosis sickness.
Results:
A classification accuracy of 91.6% is achieved on the test set of fibrotic gradings, illustrating
the ability of Raman measurements to detect differing levels of fibrotic disease among the
murine lungs. It is also shown via further modeling that coarser consideration of fibrotic grading
via binning (ie. ‘Low’, ‘Medium’, ‘High’) does not degrade performance. Finally, we consider
preliminary models for pneumonitis discrimination using the same methodologies.
Description
Keywords
Mouse models, Pneumonitis, Fibrosis, Toxicity, Breast cancer, Lung and intrathoracic tumors, Radiation therapy, Raman spectroscopy
Citation
Ali-Adeeb, R. N., Shreeves, P., Deng, X., Milligan, K., Brolo, A. G., Lum, J. J., . . . Jirasek, A. (2022). “Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.” PLOS One, 17(12), e0279739. https://doi.org/10.1371/journal.pone.0279739