Chandy, Edward;
Szmul, Adam;
Stavropoulou, Alkisti;
Jacob, Joseph;
Veiga, Catarina;
Landau, David;
Wilson, James;
... McClelland, Jamie R; + view all
(2022)
Quantitative Analysis of Radiation-Associated Parenchymal Lung Change.
Cancers
, 14
(4)
, Article 946. 10.3390/cancers14040946.
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Abstract
We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.
| Type: | Article |
|---|---|
| Title: | Quantitative Analysis of Radiation-Associated Parenchymal Lung Change |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/cancers14040946 |
| Publisher version: | https://doi.org/10.3390/cancers14040946 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | radiotherapy-induced lung damage; lung cancer; deep learning |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10143755 |
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