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MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS

BACKGROUND: The aim of this study was to investigate the potential of combined radiomics textural feature analysis of contrast-enhanced MRI (CE MRI) and static O-(2-[(18)F]fluoroethyl)-L-tyrosine(FET) PET for the differentiation of recurrent brain metastasis from radiation injury. PATIENTS AND METHO...

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Autores principales: Lohmann, Philipp, Kocher, Martin, Ceccon, Garry, Bauer, Elena, Stoffels, Gabriele, Viswanathan, Shivakumar, Ruge, Maximilian, Neumaier, Bernd, Shah, Nadim, Fink, Gereon, Langen, Karl-Josef, Galldiks, Norbert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213311/
http://dx.doi.org/10.1093/noajnl/vdz014.076
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author Lohmann, Philipp
Kocher, Martin
Ceccon, Garry
Bauer, Elena
Stoffels, Gabriele
Viswanathan, Shivakumar
Ruge, Maximilian
Neumaier, Bernd
Shah, Nadim
Fink, Gereon
Langen, Karl-Josef
Galldiks, Norbert
author_facet Lohmann, Philipp
Kocher, Martin
Ceccon, Garry
Bauer, Elena
Stoffels, Gabriele
Viswanathan, Shivakumar
Ruge, Maximilian
Neumaier, Bernd
Shah, Nadim
Fink, Gereon
Langen, Karl-Josef
Galldiks, Norbert
author_sort Lohmann, Philipp
collection PubMed
description BACKGROUND: The aim of this study was to investigate the potential of combined radiomics textural feature analysis of contrast-enhanced MRI (CE MRI) and static O-(2-[(18)F]fluoroethyl)-L-tyrosine(FET) PET for the differentiation of recurrent brain metastasis from radiation injury. PATIENTS AND METHODS: Fifty-two patients with newly diagnosed or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly radiosurgery, 84% of patients) of brain metastases were additionally investigated using FET PET. Based on histology (n=19) or clinicoradiological follow-up (n=33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two features (shape-based, first and second order features) were calculated on both unfiltered and filtered CE MRI and summed FET PET images (20–40 min p.i). After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model. RESULTS: For differentiation between radiation injury and brain metastasis recurrence, textural features extracted from CE MRI had a diagnostic accuracy of 81%. FET PET textural features revealed a slightly higher diagnostic accuracy of 83%. However, the highest diagnostic accuracy was obtained when combining CE MRI and FET PET features (accuracy, 89%). CONCLUSION: Our findings suggest that combined FET PET/MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases. SUPPORT: This work was supported by the Wilhelm-Sander Stiftung, Germany
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spelling pubmed-72133112020-07-07 MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS Lohmann, Philipp Kocher, Martin Ceccon, Garry Bauer, Elena Stoffels, Gabriele Viswanathan, Shivakumar Ruge, Maximilian Neumaier, Bernd Shah, Nadim Fink, Gereon Langen, Karl-Josef Galldiks, Norbert Neurooncol Adv Abstracts BACKGROUND: The aim of this study was to investigate the potential of combined radiomics textural feature analysis of contrast-enhanced MRI (CE MRI) and static O-(2-[(18)F]fluoroethyl)-L-tyrosine(FET) PET for the differentiation of recurrent brain metastasis from radiation injury. PATIENTS AND METHODS: Fifty-two patients with newly diagnosed or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly radiosurgery, 84% of patients) of brain metastases were additionally investigated using FET PET. Based on histology (n=19) or clinicoradiological follow-up (n=33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two features (shape-based, first and second order features) were calculated on both unfiltered and filtered CE MRI and summed FET PET images (20–40 min p.i). After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model. RESULTS: For differentiation between radiation injury and brain metastasis recurrence, textural features extracted from CE MRI had a diagnostic accuracy of 81%. FET PET textural features revealed a slightly higher diagnostic accuracy of 83%. However, the highest diagnostic accuracy was obtained when combining CE MRI and FET PET features (accuracy, 89%). CONCLUSION: Our findings suggest that combined FET PET/MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases. SUPPORT: This work was supported by the Wilhelm-Sander Stiftung, Germany Oxford University Press 2019-08-12 /pmc/articles/PMC7213311/ http://dx.doi.org/10.1093/noajnl/vdz014.076 Text en © The Author(s) 2019. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Lohmann, Philipp
Kocher, Martin
Ceccon, Garry
Bauer, Elena
Stoffels, Gabriele
Viswanathan, Shivakumar
Ruge, Maximilian
Neumaier, Bernd
Shah, Nadim
Fink, Gereon
Langen, Karl-Josef
Galldiks, Norbert
MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS
title MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS
title_full MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS
title_fullStr MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS
title_full_unstemmed MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS
title_short MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS
title_sort mlti-17. differentiation of radiation injury from recurrent brain metastasis using combined fet pet/mri radiomics
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213311/
http://dx.doi.org/10.1093/noajnl/vdz014.076
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