| Download | - View final version: Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images (PDF, 2.6 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1038/s41598-022-18393-4 |
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| Author | Search for: Majumdar, Anamitra; Search for: Allam, Nader; Search for: Zabel, W. Jeffrey; Search for: Demidov, Valentin; Search for: Flueraru, Costel1; Search for: Vitkin, I. Alex |
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| Affiliation | - National Research Council of Canada. Advanced Electronics and Photonics
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| Funder | Search for: Canadian Institutes of Health Research; Search for: Natural Sciences and Engineering Research Council of Canada; Search for: New Frontiers in Research Fund |
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| Format | Text, Article |
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| Abstract | The dominant consequence of irradiating biological systems is cellular damage, yet microvascular damage begins to assume an increasingly important role as the radiation dose levels increase. This is currently becoming more relevant in radiation medicine with its pivot towards higher-dose-per-fraction/fewer fractions treatment paradigm (e.g., stereotactic body radiotherapy (SBRT)). We have thus developed a 3D preclinical imaging platform based on speckle-variance optical coherence tomography (svOCT) for longitudinal monitoring of tumour microvascular radiation responses in vivo. Here we present an artificial intelligence (AI) approach to analyze the resultant microvascular data. In this initial study, we show that AI can successfully classify SBRT-relevant clinical radiation dose levels at multiple timepoints (t = 2–4 weeks) following irradiation (10 Gy and 30 Gy cohorts) based on induced changes in the detected microvascular networks. Practicality of the obtained results, challenges associated with modest number of animals, their successful mitigation via augmented data approaches, and advantages of using 3D deep learning methodologies, are discussed. Extension of this encouraging initial study to longitudinal AI-based time-series analysis for treatment outcome predictions at finer dose level gradations is envisioned. |
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| Publication date | 2022-08-17 |
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| Publisher | Nature Research |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | d2cd8521-4503-43f2-8d68-faeede6d5a19 |
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| Record created | 2023-10-05 |
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| Record modified | 2023-10-06 |
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