{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:18:04Z","timestamp":1777443484250,"version":"3.51.4"},"reference-count":70,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2020-04646"],"award-info":[{"award-number":["RGPIN-2020-04646"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2020-07232"],"award-info":[{"award-number":["RGPIN-2020-07232"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT 162279"],"award-info":[{"award-number":["PJT 162279"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Appl Spectrosc"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>\n                    Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. There is growing interest in the clinical application of RS to characterize biomolecular signatures associated with radiotherapy response in tumor cells and tissues. A critical step before analyzing Raman data consists of performing spectral pre-processing to increase the quality of the measurements. Spectral pre-processing comprises baseline subtraction, signal smoothing, cosmic ray (CR) correction, and removal of poor-quality measurements. Herein, we present a convolutional autoencoder (AE) for single-step, automated pre-processing of Raman spectra obtained from tumor cells and tumor tissue. We trained two separate models using the same proposed architecture, one for eliminating spectral artifacts from preclinical single-cell line and xenografted tissue spectra exposed to single-fraction radiation, and the other for correcting clinical prostate tumor biopsy spectra collected from patients receiving high-dose-rate brachytherapy (HDR-BT). The autoencoder demonstrated fast, excellent performance in removing baseline, noise, and CRs. For the preclinical data, the model obtained a root mean squared error (RMSE), and a percentage root mean squared difference (PRD) of 7.1\u2009\u00d7\u200910\n                    <jats:sup>\u22125<\/jats:sup>\n                    and 3.1%, respectively, between the AE-corrected spectra and their corresponding target data (pre-processed by our current baseline-removal algorithm). Also, the autoencoder successfully removed 94.0% of CRs from the spectra. For the clinical biopsy data, the AE achieved an RMSE and a PRD of 8.1\u2009\u00d7\u200910\n                    <jats:sup>\u22125<\/jats:sup>\n                    and 3.7%, respectively, and a CR removal rate of 90.2%. Overall, the AE corrected approximately 11\u2009000 spectra within 2.4\u2005\u2005s without the need of a GPU. Furthermore, comparative supervised learning-based post-processing data analyses were performed separately on the spectra pre-processed by the autoencoder versus the target data, and we show consistency in the biochemical radiation response profiles extracted. Finally, the AE architecture was leveraged to train a reconstruction AE to facilitate semi-automated identification of poor-quality prostate biopsy spectra, and we demonstrate 96.4% agreement between AE and manually removed outliers. These results support the development of a deep learning framework for efficient, automated pre-processing of tumor cell and tissue Raman spectra collected for radiation response monitoring studies.\n                  <\/jats:p>","DOI":"10.1177\/00037028261422275","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T09:08:01Z","timestamp":1770023281000},"page":"359-376","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra"],"prefix":"10.1177","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5881-7530","authenticated-orcid":false,"given":"Alejandra M.","family":"Fuentes","sequence":"first","affiliation":[{"name":"The University of British Columbia Okanagan Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7089-9304","authenticated-orcid":false,"given":"Kirsty","family":"Milligan","sequence":"additional","affiliation":[{"name":"The University of British Columbia Okanagan Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitchell","family":"Wiebe","sequence":"additional","affiliation":[{"name":"The University of British Columbia Okanagan Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julian J.","family":"Lum","sequence":"additional","affiliation":[{"name":"The University of Victoria"},{"name":"Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3162-0881","authenticated-orcid":false,"given":"Alexandre G.","family":"Brolo","sequence":"additional","affiliation":[{"name":"The University of Victoria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey L.","family":"Andrews","sequence":"additional","affiliation":[{"name":"The University of British Columbia Okanagan Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0844-2092","authenticated-orcid":false,"given":"Andrew","family":"Jirasek","sequence":"additional","affiliation":[{"name":"The University of British Columbia Okanagan Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Brenner D. 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