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Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments
BACKGROUND: Current radiological assessments of (18)fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9373298/ https://www.ncbi.nlm.nih.gov/pubmed/35962459 http://dx.doi.org/10.1186/s40644-022-00476-0 |
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author | Jemaa, S. Paulson, J. N. Hutchings, M. Kostakoglu, L. Trotman, J. Tracy, S. de Crespigny, A. Carano, R. A. D. El-Galaly, T. C. Nielsen, T. G. Bengtsson, T. |
author_facet | Jemaa, S. Paulson, J. N. Hutchings, M. Kostakoglu, L. Trotman, J. Tracy, S. de Crespigny, A. Carano, R. A. D. El-Galaly, T. C. Nielsen, T. G. Bengtsson, T. |
author_sort | Jemaa, S. |
collection | PubMed |
description | BACKGROUND: Current radiological assessments of (18)fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment. METHODS: Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422). RESULTS: Our AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%. CONCLUSION: Our tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients. TRIAL REGISTRATION: Registered clinicaltrials.gov number: NCT01287741. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00476-0. |
format | Online Article Text |
id | pubmed-9373298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93732982022-08-13 Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments Jemaa, S. Paulson, J. N. Hutchings, M. Kostakoglu, L. Trotman, J. Tracy, S. de Crespigny, A. Carano, R. A. D. El-Galaly, T. C. Nielsen, T. G. Bengtsson, T. Cancer Imaging Research Article BACKGROUND: Current radiological assessments of (18)fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment. METHODS: Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422). RESULTS: Our AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%. CONCLUSION: Our tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients. TRIAL REGISTRATION: Registered clinicaltrials.gov number: NCT01287741. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00476-0. BioMed Central 2022-08-12 /pmc/articles/PMC9373298/ /pubmed/35962459 http://dx.doi.org/10.1186/s40644-022-00476-0 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Jemaa, S. Paulson, J. N. Hutchings, M. Kostakoglu, L. Trotman, J. Tracy, S. de Crespigny, A. Carano, R. A. D. El-Galaly, T. C. Nielsen, T. G. Bengtsson, T. Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments |
title | Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments |
title_full | Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments |
title_fullStr | Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments |
title_full_unstemmed | Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments |
title_short | Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments |
title_sort | full automation of total metabolic tumor volume from fdg-pet/ct in dlbcl for baseline risk assessments |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9373298/ https://www.ncbi.nlm.nih.gov/pubmed/35962459 http://dx.doi.org/10.1186/s40644-022-00476-0 |
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