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Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell l...

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Autores principales: Bertolini, Marco, Trojani, Valeria, Botti, Andrea, Cucurachi, Noemi, Galaverni, Marco, Cozzi, Salvatore, Borghetti, Paolo, La Mattina, Salvatore, Pastorello, Edoardo, Avanzo, Michele, Revelant, Alberto, Sepulcri, Matteo, Paronetto, Chiara, Ursino, Stefano, Malfatti, Giulia, Giaj-Levra, Niccolò, Falcinelli, Lorenzo, Iotti, Cinzia, Iori, Mauro, Ciammella, Patrizia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332210/
https://www.ncbi.nlm.nih.gov/pubmed/35892979
http://dx.doi.org/10.3390/curroncol29080410
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author Bertolini, Marco
Trojani, Valeria
Botti, Andrea
Cucurachi, Noemi
Galaverni, Marco
Cozzi, Salvatore
Borghetti, Paolo
La Mattina, Salvatore
Pastorello, Edoardo
Avanzo, Michele
Revelant, Alberto
Sepulcri, Matteo
Paronetto, Chiara
Ursino, Stefano
Malfatti, Giulia
Giaj-Levra, Niccolò
Falcinelli, Lorenzo
Iotti, Cinzia
Iori, Mauro
Ciammella, Patrizia
author_facet Bertolini, Marco
Trojani, Valeria
Botti, Andrea
Cucurachi, Noemi
Galaverni, Marco
Cozzi, Salvatore
Borghetti, Paolo
La Mattina, Salvatore
Pastorello, Edoardo
Avanzo, Michele
Revelant, Alberto
Sepulcri, Matteo
Paronetto, Chiara
Ursino, Stefano
Malfatti, Giulia
Giaj-Levra, Niccolò
Falcinelli, Lorenzo
Iotti, Cinzia
Iori, Mauro
Ciammella, Patrizia
author_sort Bertolini, Marco
collection PubMed
description The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63–0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis.
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spelling pubmed-93322102022-07-29 Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer Bertolini, Marco Trojani, Valeria Botti, Andrea Cucurachi, Noemi Galaverni, Marco Cozzi, Salvatore Borghetti, Paolo La Mattina, Salvatore Pastorello, Edoardo Avanzo, Michele Revelant, Alberto Sepulcri, Matteo Paronetto, Chiara Ursino, Stefano Malfatti, Giulia Giaj-Levra, Niccolò Falcinelli, Lorenzo Iotti, Cinzia Iori, Mauro Ciammella, Patrizia Curr Oncol Article The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63–0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis. MDPI 2022-07-22 /pmc/articles/PMC9332210/ /pubmed/35892979 http://dx.doi.org/10.3390/curroncol29080410 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bertolini, Marco
Trojani, Valeria
Botti, Andrea
Cucurachi, Noemi
Galaverni, Marco
Cozzi, Salvatore
Borghetti, Paolo
La Mattina, Salvatore
Pastorello, Edoardo
Avanzo, Michele
Revelant, Alberto
Sepulcri, Matteo
Paronetto, Chiara
Ursino, Stefano
Malfatti, Giulia
Giaj-Levra, Niccolò
Falcinelli, Lorenzo
Iotti, Cinzia
Iori, Mauro
Ciammella, Patrizia
Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
title Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
title_full Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
title_fullStr Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
title_full_unstemmed Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
title_short Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
title_sort novel harmonization method for multi-centric radiomic studies in non-small cell lung cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332210/
https://www.ncbi.nlm.nih.gov/pubmed/35892979
http://dx.doi.org/10.3390/curroncol29080410
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