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Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis

F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a robust imaging modality used for staging multiple myeloma (MM) and assessing treatment responses. Herein, we extracted features from the FDG PET/CT images of MM patients using an artificial intelligence autoen...

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Autores principales: Lee, Hyunjong, Hyun, Seung Hyup, Cho, Young Seok, Moon, Seung Hwan, Choi, Joon Young, Kim, Kihyun, Lee, Kyung-Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10185699/
https://www.ncbi.nlm.nih.gov/pubmed/37188831
http://dx.doi.org/10.1038/s41598-023-34653-3
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author Lee, Hyunjong
Hyun, Seung Hyup
Cho, Young Seok
Moon, Seung Hwan
Choi, Joon Young
Kim, Kihyun
Lee, Kyung-Han
author_facet Lee, Hyunjong
Hyun, Seung Hyup
Cho, Young Seok
Moon, Seung Hwan
Choi, Joon Young
Kim, Kihyun
Lee, Kyung-Han
author_sort Lee, Hyunjong
collection PubMed
description F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a robust imaging modality used for staging multiple myeloma (MM) and assessing treatment responses. Herein, we extracted features from the FDG PET/CT images of MM patients using an artificial intelligence autoencoder algorithm that constructs a compressed representation of input data. We then evaluated the prognostic value of the image-feature clusters thus extracted. Conventional image parameters including metabolic tumor volume (MTV) were measured on volumes-of-interests (VOIs) covering only the bones. Features were extracted with the autoencoder algorithm on bone-covering VOIs. Supervised and unsupervised clustering were performed on image features. Survival analyses for progression-free survival (PFS) were performed for conventional parameters and clusters. In result, supervised and unsupervised clustering of the image features grouped the subjects into three clusters (A, B, and C). In multivariable Cox regression analysis, unsupervised cluster C, supervised cluster C, and high MTV were significant independent predictors of worse PFS. Supervised and unsupervised cluster analyses of image features extracted from FDG PET/CT scans of MM patients by an autoencoder allowed significant and independent prediction of worse PFS. Therefore, artificial intelligence algorithm–based cluster analyses of FDG PET/CT images could be useful for MM risk stratification.
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spelling pubmed-101856992023-05-17 Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis Lee, Hyunjong Hyun, Seung Hyup Cho, Young Seok Moon, Seung Hwan Choi, Joon Young Kim, Kihyun Lee, Kyung-Han Sci Rep Article F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a robust imaging modality used for staging multiple myeloma (MM) and assessing treatment responses. Herein, we extracted features from the FDG PET/CT images of MM patients using an artificial intelligence autoencoder algorithm that constructs a compressed representation of input data. We then evaluated the prognostic value of the image-feature clusters thus extracted. Conventional image parameters including metabolic tumor volume (MTV) were measured on volumes-of-interests (VOIs) covering only the bones. Features were extracted with the autoencoder algorithm on bone-covering VOIs. Supervised and unsupervised clustering were performed on image features. Survival analyses for progression-free survival (PFS) were performed for conventional parameters and clusters. In result, supervised and unsupervised clustering of the image features grouped the subjects into three clusters (A, B, and C). In multivariable Cox regression analysis, unsupervised cluster C, supervised cluster C, and high MTV were significant independent predictors of worse PFS. Supervised and unsupervised cluster analyses of image features extracted from FDG PET/CT scans of MM patients by an autoencoder allowed significant and independent prediction of worse PFS. Therefore, artificial intelligence algorithm–based cluster analyses of FDG PET/CT images could be useful for MM risk stratification. Nature Publishing Group UK 2023-05-15 /pmc/articles/PMC10185699/ /pubmed/37188831 http://dx.doi.org/10.1038/s41598-023-34653-3 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
Lee, Hyunjong
Hyun, Seung Hyup
Cho, Young Seok
Moon, Seung Hwan
Choi, Joon Young
Kim, Kihyun
Lee, Kyung-Han
Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
title Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
title_full Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
title_fullStr Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
title_full_unstemmed Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
title_short Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
title_sort cluster analysis of autoencoder-extracted fdg pet/ct features identifies multiple myeloma patients with poor prognosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10185699/
https://www.ncbi.nlm.nih.gov/pubmed/37188831
http://dx.doi.org/10.1038/s41598-023-34653-3
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