Cargando…

Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy

Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether sta...

Descripción completa

Detalles Bibliográficos
Autores principales: Davey, Angela, van Herk, Marcel, Faivre-Finn, Corinne, Brown, Sean, McWilliam, Alan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144744/
https://www.ncbi.nlm.nih.gov/pubmed/33882470
http://dx.doi.org/10.1088/1361-6560/abfa34
_version_ 1783697023929679872
author Davey, Angela
van Herk, Marcel
Faivre-Finn, Corinne
Brown, Sean
McWilliam, Alan
author_facet Davey, Angela
van Herk, Marcel
Faivre-Finn, Corinne
Brown, Sean
McWilliam, Alan
author_sort Davey, Angela
collection PubMed
description Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.
format Online
Article
Text
id pubmed-8144744
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher IOP Publishing
record_format MEDLINE/PubMed
spelling pubmed-81447442021-05-26 Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy Davey, Angela van Herk, Marcel Faivre-Finn, Corinne Brown, Sean McWilliam, Alan Phys Med Biol Paper Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure. IOP Publishing 2021-06-07 2021-05-24 /pmc/articles/PMC8144744/ /pubmed/33882470 http://dx.doi.org/10.1088/1361-6560/abfa34 Text en © 2021 Institute of Physics and Engineering in Medicine https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Davey, Angela
van Herk, Marcel
Faivre-Finn, Corinne
Brown, Sean
McWilliam, Alan
Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
title Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
title_full Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
title_fullStr Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
title_full_unstemmed Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
title_short Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
title_sort optimising use of 4d-ct phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144744/
https://www.ncbi.nlm.nih.gov/pubmed/33882470
http://dx.doi.org/10.1088/1361-6560/abfa34
work_keys_str_mv AT daveyangela optimisinguseof4dctphaseinformationforradiomicsanalysisinlungcancerpatientstreatedwithstereotacticbodyradiotherapy
AT vanherkmarcel optimisinguseof4dctphaseinformationforradiomicsanalysisinlungcancerpatientstreatedwithstereotacticbodyradiotherapy
AT faivrefinncorinne optimisinguseof4dctphaseinformationforradiomicsanalysisinlungcancerpatientstreatedwithstereotacticbodyradiotherapy
AT brownsean optimisinguseof4dctphaseinformationforradiomicsanalysisinlungcancerpatientstreatedwithstereotacticbodyradiotherapy
AT mcwilliamalan optimisinguseof4dctphaseinformationforradiomicsanalysisinlungcancerpatientstreatedwithstereotacticbodyradiotherapy