Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics.

Autor: Brown KH; Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK. Electronic address: kathryn.brown@qub.ac.uk., Ghita-Pettigrew M; Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK., Kerr BN; Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK., Mohamed-Smith L; Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK., Walls GM; Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK; Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Northern Ireland, UK., McGarry CK; Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Northern Ireland, UK., Butterworth KT; Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK.
Jazyk: angličtina
Zdroj: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2024 Mar; Vol. 192, pp. 110106. Date of Electronic Publication: 2024 Jan 20.
DOI: 10.1016/j.radonc.2024.110106
Abstrakt: Background and Purpose: Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation.
Materials and Methods: Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model.
Results: Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96).
Conclusions: This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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