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Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma

PURPOSE: The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [(18)F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. METHODS: A tota...

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Autores principales: Li, Zhicong, Holzgreve, Adrien, Unterrainer, Lena M., Ruf, Viktoria C., Quach, Stefanie, Bartos, Laura M., Suchorska, Bogdana, Niyazi, Maximilian, Wenter, Vera, Herms, Jochen, Bartenstein, Peter, Tonn, Joerg-Christian, Unterrainer, Marcus, Albert, Nathalie L., Kaiser, Lena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816231/
https://www.ncbi.nlm.nih.gov/pubmed/36227357
http://dx.doi.org/10.1007/s00259-022-05988-2
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author Li, Zhicong
Holzgreve, Adrien
Unterrainer, Lena M.
Ruf, Viktoria C.
Quach, Stefanie
Bartos, Laura M.
Suchorska, Bogdana
Niyazi, Maximilian
Wenter, Vera
Herms, Jochen
Bartenstein, Peter
Tonn, Joerg-Christian
Unterrainer, Marcus
Albert, Nathalie L.
Kaiser, Lena
author_facet Li, Zhicong
Holzgreve, Adrien
Unterrainer, Lena M.
Ruf, Viktoria C.
Quach, Stefanie
Bartos, Laura M.
Suchorska, Bogdana
Niyazi, Maximilian
Wenter, Vera
Herms, Jochen
Bartenstein, Peter
Tonn, Joerg-Christian
Unterrainer, Marcus
Albert, Nathalie L.
Kaiser, Lena
author_sort Li, Zhicong
collection PubMed
description PURPOSE: The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [(18)F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. METHODS: A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [(18)F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. RESULTS: A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60–0.88) in the independent testing cohort. CONCLUSIONS: This study successfully built and evaluated prediction models using [(18)F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [(18)F]FET PET–based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [(18)F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-022-05988-2.
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spelling pubmed-98162312023-01-07 Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma Li, Zhicong Holzgreve, Adrien Unterrainer, Lena M. Ruf, Viktoria C. Quach, Stefanie Bartos, Laura M. Suchorska, Bogdana Niyazi, Maximilian Wenter, Vera Herms, Jochen Bartenstein, Peter Tonn, Joerg-Christian Unterrainer, Marcus Albert, Nathalie L. Kaiser, Lena Eur J Nucl Med Mol Imaging Original Article PURPOSE: The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [(18)F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. METHODS: A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [(18)F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. RESULTS: A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60–0.88) in the independent testing cohort. CONCLUSIONS: This study successfully built and evaluated prediction models using [(18)F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [(18)F]FET PET–based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [(18)F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-022-05988-2. Springer Berlin Heidelberg 2022-10-13 2023 /pmc/articles/PMC9816231/ /pubmed/36227357 http://dx.doi.org/10.1007/s00259-022-05988-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Li, Zhicong
Holzgreve, Adrien
Unterrainer, Lena M.
Ruf, Viktoria C.
Quach, Stefanie
Bartos, Laura M.
Suchorska, Bogdana
Niyazi, Maximilian
Wenter, Vera
Herms, Jochen
Bartenstein, Peter
Tonn, Joerg-Christian
Unterrainer, Marcus
Albert, Nathalie L.
Kaiser, Lena
Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma
title Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma
title_full Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma
title_fullStr Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma
title_full_unstemmed Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma
title_short Combination of pre-treatment dynamic [(18)F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma
title_sort combination of pre-treatment dynamic [(18)f]fet pet radiomics and conventional clinical parameters for the survival stratification in patients with idh-wildtype glioblastoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816231/
https://www.ncbi.nlm.nih.gov/pubmed/36227357
http://dx.doi.org/10.1007/s00259-022-05988-2
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