Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts
Abstract Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perf...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28479-2 |
| _version_ | 1811175818457513984 |
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| author | Alejandra M. Fuentes Apurva Narayan Kirsty Milligan Julian J. Lum Alex G. Brolo Jeffrey L. Andrews Andrew Jirasek |
| author_facet | Alejandra M. Fuentes Apurva Narayan Kirsty Milligan Julian J. Lum Alex G. Brolo Jeffrey L. Andrews Andrew Jirasek |
| author_sort | Alejandra M. Fuentes |
| collection | DOAJ |
| description | Abstract Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring. |
| first_indexed | 2024-04-10T19:43:07Z |
| format | Article |
| id | doaj.art-735b32e284f743318c42f9cf976a8c6b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T19:43:07Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-735b32e284f743318c42f9cf976a8c6b2023-01-29T12:12:55ZengNature PortfolioScientific Reports2045-23222023-01-0113111210.1038/s41598-023-28479-2Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenograftsAlejandra M. Fuentes0Apurva Narayan1Kirsty Milligan2Julian J. Lum3Alex G. Brolo4Jeffrey L. Andrews5Andrew Jirasek6Department of Physics, The University of British Columbia Okanagan CampusDepartment of Computer Science, Western UniversityDepartment of Physics, The University of British Columbia Okanagan CampusDepartment of Biochemistry and Microbiology, The University of VictoriaDepartment of Chemistry, The University of VictoriaDepartment of Statistics, The University of British Columbia Okanagan CampusDepartment of Physics, The University of British Columbia Okanagan CampusAbstract Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.https://doi.org/10.1038/s41598-023-28479-2 |
| spellingShingle | Alejandra M. Fuentes Apurva Narayan Kirsty Milligan Julian J. Lum Alex G. Brolo Jeffrey L. Andrews Andrew Jirasek Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts Scientific Reports |
| title | Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts |
| title_full | Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts |
| title_fullStr | Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts |
| title_full_unstemmed | Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts |
| title_short | Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts |
| title_sort | raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts |
| url | https://doi.org/10.1038/s41598-023-28479-2 |
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