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Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy()
BACKGROUND AND PURPOSE: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomic...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536307/ https://www.ncbi.nlm.nih.gov/pubmed/33043157 http://dx.doi.org/10.1016/j.phro.2020.07.003 |
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author | Socarrás Fernández, Jairo A Mönnich, David Leibfarth, Sara Welz, Stefan Zwanenburg, Alex Leger, Stefan Löck, Steffen Pfannenberg, Christina La Fougère, Christian Reischl, Gerald Baumann, Michael Zips, Daniel Thorwarth, Daniela |
author_facet | Socarrás Fernández, Jairo A Mönnich, David Leibfarth, Sara Welz, Stefan Zwanenburg, Alex Leger, Stefan Löck, Steffen Pfannenberg, Christina La Fougère, Christian Reischl, Gerald Baumann, Michael Zips, Daniel Thorwarth, Daniela |
author_sort | Socarrás Fernández, Jairo A |
collection | PubMed |
description | BACKGROUND AND PURPOSE: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET. MATERIALS AND METHODS: Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [(18)F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures. Secondly, a pre-defined [(18)F]FMISO-PET tumour-to-muscle-ratio (TMR(peak) ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [(18)F]FMISO-PET was performed using area-under–the-curve (AUC) and Kaplan-Meier analysis including log-rank test. RESULTS: The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [(18)F]FMISO TMR(peak) reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [(18)F]FMISO-PET significant differences were observed (p = 0.02). CONCLUSIONS: No direct correlation of patient stratification using [(18)F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients. |
format | Online Article Text |
id | pubmed-7536307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75363072020-10-07 Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() Socarrás Fernández, Jairo A Mönnich, David Leibfarth, Sara Welz, Stefan Zwanenburg, Alex Leger, Stefan Löck, Steffen Pfannenberg, Christina La Fougère, Christian Reischl, Gerald Baumann, Michael Zips, Daniel Thorwarth, Daniela Phys Imaging Radiat Oncol Original Research Article BACKGROUND AND PURPOSE: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET. MATERIALS AND METHODS: Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [(18)F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures. Secondly, a pre-defined [(18)F]FMISO-PET tumour-to-muscle-ratio (TMR(peak) ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [(18)F]FMISO-PET was performed using area-under–the-curve (AUC) and Kaplan-Meier analysis including log-rank test. RESULTS: The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [(18)F]FMISO TMR(peak) reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [(18)F]FMISO-PET significant differences were observed (p = 0.02). CONCLUSIONS: No direct correlation of patient stratification using [(18)F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients. Elsevier 2020-08-04 /pmc/articles/PMC7536307/ /pubmed/33043157 http://dx.doi.org/10.1016/j.phro.2020.07.003 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Article Socarrás Fernández, Jairo A Mönnich, David Leibfarth, Sara Welz, Stefan Zwanenburg, Alex Leger, Stefan Löck, Steffen Pfannenberg, Christina La Fougère, Christian Reischl, Gerald Baumann, Michael Zips, Daniel Thorwarth, Daniela Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
title | Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
title_full | Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
title_fullStr | Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
title_full_unstemmed | Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
title_short | Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
title_sort | comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy() |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536307/ https://www.ncbi.nlm.nih.gov/pubmed/33043157 http://dx.doi.org/10.1016/j.phro.2020.07.003 |
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