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Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging

BACKGROUND: Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning diffe...

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Autores principales: Oliveira, Carol, Amstutz, Florian, Vuong, Diem, Bogowicz, Marta, Hüllner, Martin, Foerster, Robert, Basler, Lucas, Schröder, Christina, Eboulet, Eric I., Pless, Miklos, Thierstein, Sandra, Peters, Solange, Hillinger, Sven, Tanadini-Lang, Stephanie, Guckenberger, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380219/
https://www.ncbi.nlm.nih.gov/pubmed/34417899
http://dx.doi.org/10.1186/s13550-021-00809-3
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author Oliveira, Carol
Amstutz, Florian
Vuong, Diem
Bogowicz, Marta
Hüllner, Martin
Foerster, Robert
Basler, Lucas
Schröder, Christina
Eboulet, Eric I.
Pless, Miklos
Thierstein, Sandra
Peters, Solange
Hillinger, Sven
Tanadini-Lang, Stephanie
Guckenberger, Matthias
author_facet Oliveira, Carol
Amstutz, Florian
Vuong, Diem
Bogowicz, Marta
Hüllner, Martin
Foerster, Robert
Basler, Lucas
Schröder, Christina
Eboulet, Eric I.
Pless, Miklos
Thierstein, Sandra
Peters, Solange
Hillinger, Sven
Tanadini-Lang, Stephanie
Guckenberger, Matthias
author_sort Oliveira, Carol
collection PubMed
description BACKGROUND: Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). METHODS: A total of 1404 primary tumour radiomic features were extracted from pre-treatment [(18)F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). CONCLUSIONS: A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-021-00809-3.
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spelling pubmed-83802192021-09-08 Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging Oliveira, Carol Amstutz, Florian Vuong, Diem Bogowicz, Marta Hüllner, Martin Foerster, Robert Basler, Lucas Schröder, Christina Eboulet, Eric I. Pless, Miklos Thierstein, Sandra Peters, Solange Hillinger, Sven Tanadini-Lang, Stephanie Guckenberger, Matthias EJNMMI Res Original Research BACKGROUND: Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). METHODS: A total of 1404 primary tumour radiomic features were extracted from pre-treatment [(18)F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). CONCLUSIONS: A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-021-00809-3. Springer Berlin Heidelberg 2021-08-21 /pmc/articles/PMC8380219/ /pubmed/34417899 http://dx.doi.org/10.1186/s13550-021-00809-3 Text en © The Author(s) 2021 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 Research
Oliveira, Carol
Amstutz, Florian
Vuong, Diem
Bogowicz, Marta
Hüllner, Martin
Foerster, Robert
Basler, Lucas
Schröder, Christina
Eboulet, Eric I.
Pless, Miklos
Thierstein, Sandra
Peters, Solange
Hillinger, Sven
Tanadini-Lang, Stephanie
Guckenberger, Matthias
Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging
title Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging
title_full Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging
title_fullStr Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging
title_full_unstemmed Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging
title_short Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging
title_sort preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine fdg-pet imaging
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380219/
https://www.ncbi.nlm.nih.gov/pubmed/34417899
http://dx.doi.org/10.1186/s13550-021-00809-3
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