Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity
| dc.contributor.author | Ali-Adeeb, Ramie N. | |
| dc.contributor.author | Shreeves, Phil | |
| dc.contributor.author | Deng, Xinchen | |
| dc.contributor.author | Milligan, Kirsty | |
| dc.contributor.author | Brolo, Alexandre G. | |
| dc.contributor.author | Lum, Jullian J. | |
| dc.contributor.author | Haston, Christina | |
| dc.contributor.author | Andrews, Jeffrey L. | |
| dc.contributor.author | Jirasek, Andrew | |
| dc.date.accessioned | 2023-01-26T19:48:34Z | |
| dc.date.available | 2023-01-26T19:48:34Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022 | |
| dc.description.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. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | This research was supported by the Natural Sciences and Engineering Research Council of Canada through their Discovery Grants program (RGPIN-2014-06730 - Andrews, RGPIN- 2020-07232 - Jirasek) and the Cluster of Research Excellence in Medical Physics and Data Analytics (PI: Jirasek) enabled via the University of British Columbia’s Eminence Fund. Infrastructure support was provided by the Canada Foundation for Innovation through their John R.\ Evans Leaders Fund program (\#35578 - Andrews, \#32859 - Jirasek PI). | en_US |
| dc.identifier.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 | en_US |
| dc.identifier.uri | https://doi.org/10.1371/journal.pone.0279739 | |
| dc.identifier.uri | http://hdl.handle.net/1828/14699 | |
| dc.language.iso | en | en_US |
| dc.publisher | PLOS One | en_US |
| dc.subject | Mouse models | |
| dc.subject | Pneumonitis | |
| dc.subject | Fibrosis | |
| dc.subject | Toxicity | |
| dc.subject | Breast cancer | |
| dc.subject | Lung and intrathoracic tumors | |
| dc.subject | Radiation therapy | |
| dc.subject | Raman spectroscopy | |
| dc.subject.department | Department of Chemistry | |
| dc.subject.department | Department of Biochemistry and Microbiology | |
| dc.title | Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity | en_US |
| dc.type | Article | en_US |