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[18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients

Biopsy is the standard for assessment of bone marrow involvement in mantle cell lymphoma (MCL). We investigated whether [18F]FDG-PET radiomic texture features can improve prediction of bone marrow involvement in MCL, compared to standardized uptake values (SUV), and whether combination with laborato...

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Autores principales: Mayerhoefer, Marius E., Riedl, Christopher C., Kumar, Anita, Dogan, Ahmet, Gibbs, Peter, Weber, Michael, Staber, Philipp B., Huicochea Castellanos, Sandra, Schöder, Heiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281173/
https://www.ncbi.nlm.nih.gov/pubmed/32370121
http://dx.doi.org/10.3390/cancers12051138
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author Mayerhoefer, Marius E.
Riedl, Christopher C.
Kumar, Anita
Dogan, Ahmet
Gibbs, Peter
Weber, Michael
Staber, Philipp B.
Huicochea Castellanos, Sandra
Schöder, Heiko
author_facet Mayerhoefer, Marius E.
Riedl, Christopher C.
Kumar, Anita
Dogan, Ahmet
Gibbs, Peter
Weber, Michael
Staber, Philipp B.
Huicochea Castellanos, Sandra
Schöder, Heiko
author_sort Mayerhoefer, Marius E.
collection PubMed
description Biopsy is the standard for assessment of bone marrow involvement in mantle cell lymphoma (MCL). We investigated whether [18F]FDG-PET radiomic texture features can improve prediction of bone marrow involvement in MCL, compared to standardized uptake values (SUV), and whether combination with laboratory data improves results. Ninety-seven MCL patients were retrospectively included. SUVmax, SUVmean, SUVpeak and 16 co-occurrence matrix texture features were extracted from pelvic bones on [18F]FDG-PET/CT. A multi-layer perceptron neural network was used to compare three combinations for prediction of bone marrow involvement—the SUVs, a radiomic signature based on SUVs and texture features, and the radiomic signature combined with laboratory parameters. This step was repeated using two cut-off values for relative bone marrow involvement: REL > 5% (>5% of red/cellular bone marrow); and REL > 10%. Biopsy demonstrated bone marrow involvement in 67/97 patients (69.1%). SUVs, the radiomic signature, and the radiomic signature with laboratory data showed AUCs of up to 0.66, 0.73, and 0.81 for involved vs. uninvolved bone marrow; 0.68, 0.84, and 0.84 for REL ≤ 5% vs. REL > 5%; and 0.69, 0.85, and 0.87 for REL ≤ 10% vs. REL > 10%. In conclusion, [18F]FDG-PET texture features improve SUV-based prediction of bone marrow involvement in MCL. The results may be further improved by combination with laboratory parameters.
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spelling pubmed-72811732020-06-15 [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients Mayerhoefer, Marius E. Riedl, Christopher C. Kumar, Anita Dogan, Ahmet Gibbs, Peter Weber, Michael Staber, Philipp B. Huicochea Castellanos, Sandra Schöder, Heiko Cancers (Basel) Article Biopsy is the standard for assessment of bone marrow involvement in mantle cell lymphoma (MCL). We investigated whether [18F]FDG-PET radiomic texture features can improve prediction of bone marrow involvement in MCL, compared to standardized uptake values (SUV), and whether combination with laboratory data improves results. Ninety-seven MCL patients were retrospectively included. SUVmax, SUVmean, SUVpeak and 16 co-occurrence matrix texture features were extracted from pelvic bones on [18F]FDG-PET/CT. A multi-layer perceptron neural network was used to compare three combinations for prediction of bone marrow involvement—the SUVs, a radiomic signature based on SUVs and texture features, and the radiomic signature combined with laboratory parameters. This step was repeated using two cut-off values for relative bone marrow involvement: REL > 5% (>5% of red/cellular bone marrow); and REL > 10%. Biopsy demonstrated bone marrow involvement in 67/97 patients (69.1%). SUVs, the radiomic signature, and the radiomic signature with laboratory data showed AUCs of up to 0.66, 0.73, and 0.81 for involved vs. uninvolved bone marrow; 0.68, 0.84, and 0.84 for REL ≤ 5% vs. REL > 5%; and 0.69, 0.85, and 0.87 for REL ≤ 10% vs. REL > 10%. In conclusion, [18F]FDG-PET texture features improve SUV-based prediction of bone marrow involvement in MCL. The results may be further improved by combination with laboratory parameters. MDPI 2020-05-02 /pmc/articles/PMC7281173/ /pubmed/32370121 http://dx.doi.org/10.3390/cancers12051138 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mayerhoefer, Marius E.
Riedl, Christopher C.
Kumar, Anita
Dogan, Ahmet
Gibbs, Peter
Weber, Michael
Staber, Philipp B.
Huicochea Castellanos, Sandra
Schöder, Heiko
[18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients
title [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients
title_full [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients
title_fullStr [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients
title_full_unstemmed [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients
title_short [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients
title_sort [18f]fdg-pet/ct radiomics for prediction of bone marrow involvement in mantle cell lymphoma: a retrospective study in 97 patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281173/
https://www.ncbi.nlm.nih.gov/pubmed/32370121
http://dx.doi.org/10.3390/cancers12051138
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