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Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial

The prognostic value of (18)F-Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) at baseline or the predictive value of minimal residual disease (MRD) detection appear as potential tools to improve mantle cell lymphoma (MCL) patients’ management. The LyMa-101, a phase 2...

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Autores principales: Bodet-Milin, Caroline, Morvant, Cyrille, Carlier, Thomas, Frecon, Gauthier, Tournilhac, Olivier, Safar, Violaine, Kraeber-Bodere, Françoise, Le Gouill, Steven, Macintyre, Elizabeth, Bailly, Clément
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598231/
https://www.ncbi.nlm.nih.gov/pubmed/37875524
http://dx.doi.org/10.1038/s41598-023-45215-y
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author Bodet-Milin, Caroline
Morvant, Cyrille
Carlier, Thomas
Frecon, Gauthier
Tournilhac, Olivier
Safar, Violaine
Kraeber-Bodere, Françoise
Le Gouill, Steven
Macintyre, Elizabeth
Bailly, Clément
author_facet Bodet-Milin, Caroline
Morvant, Cyrille
Carlier, Thomas
Frecon, Gauthier
Tournilhac, Olivier
Safar, Violaine
Kraeber-Bodere, Françoise
Le Gouill, Steven
Macintyre, Elizabeth
Bailly, Clément
author_sort Bodet-Milin, Caroline
collection PubMed
description The prognostic value of (18)F-Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) at baseline or the predictive value of minimal residual disease (MRD) detection appear as potential tools to improve mantle cell lymphoma (MCL) patients’ management. The LyMa-101, a phase 2 trial of the LYSA group (ClinicalTrials.gov:NCT02896582) reported induction therapy with obinutuzumab, a CD20 monoclonal antibody. Herein, we investigated the added prognostic value of radiomic features (RF) derived from FDG-PET/CT at diagnosis for MRD value prediction. FDG-PET/CT of 59 MCL patients included in the LyMa-101 trial have been independently, blindly and centrally reviewed. RF were extracted from the disease area with the highest uptake and from the total metabolic tumor volume (TMTV). Two models of machine learning were used to compare several combinations for prediction of MRD before autologous stem cell transplant consolidation (ASCT). Each algorithm was generated with or without constrained feature selections for clinical and laboratory parameters. Both algorithms showed better discrimination performances for negative vs positive MRD in the lesion with the highest uptake than in the TMTV. The constrained use of clinical and biological features showed a clear loss in sensitivity for the prediction of MRD status before ASCT, regardless of the machine learning model. These data plead for the importance of FDG-PET/CT RF compared to clinical and laboratory parameters and also reinforced the previously made hypothesis that the prognosis of the disease in MCL patients is linked to the most aggressive contingent, within the lesion with the highest uptake.
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spelling pubmed-105982312023-10-26 Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial Bodet-Milin, Caroline Morvant, Cyrille Carlier, Thomas Frecon, Gauthier Tournilhac, Olivier Safar, Violaine Kraeber-Bodere, Françoise Le Gouill, Steven Macintyre, Elizabeth Bailly, Clément Sci Rep Article The prognostic value of (18)F-Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) at baseline or the predictive value of minimal residual disease (MRD) detection appear as potential tools to improve mantle cell lymphoma (MCL) patients’ management. The LyMa-101, a phase 2 trial of the LYSA group (ClinicalTrials.gov:NCT02896582) reported induction therapy with obinutuzumab, a CD20 monoclonal antibody. Herein, we investigated the added prognostic value of radiomic features (RF) derived from FDG-PET/CT at diagnosis for MRD value prediction. FDG-PET/CT of 59 MCL patients included in the LyMa-101 trial have been independently, blindly and centrally reviewed. RF were extracted from the disease area with the highest uptake and from the total metabolic tumor volume (TMTV). Two models of machine learning were used to compare several combinations for prediction of MRD before autologous stem cell transplant consolidation (ASCT). Each algorithm was generated with or without constrained feature selections for clinical and laboratory parameters. Both algorithms showed better discrimination performances for negative vs positive MRD in the lesion with the highest uptake than in the TMTV. The constrained use of clinical and biological features showed a clear loss in sensitivity for the prediction of MRD status before ASCT, regardless of the machine learning model. These data plead for the importance of FDG-PET/CT RF compared to clinical and laboratory parameters and also reinforced the previously made hypothesis that the prognosis of the disease in MCL patients is linked to the most aggressive contingent, within the lesion with the highest uptake. Nature Publishing Group UK 2023-10-24 /pmc/articles/PMC10598231/ /pubmed/37875524 http://dx.doi.org/10.1038/s41598-023-45215-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Article
Bodet-Milin, Caroline
Morvant, Cyrille
Carlier, Thomas
Frecon, Gauthier
Tournilhac, Olivier
Safar, Violaine
Kraeber-Bodere, Françoise
Le Gouill, Steven
Macintyre, Elizabeth
Bailly, Clément
Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial
title Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial
title_full Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial
title_fullStr Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial
title_full_unstemmed Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial
title_short Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial
title_sort performance of baseline fdg-pet/ct radiomics for prediction of bone marrow minimal residual disease status in the lyma-101 trial
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598231/
https://www.ncbi.nlm.nih.gov/pubmed/37875524
http://dx.doi.org/10.1038/s41598-023-45215-y
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