ArticleScientific reports2023
Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The [European journal of nuclear medicine and molecular imaging · 2026Pooled it
- Review
- Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [EJNMMI research · 2024Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Semi-quantitative FDG parameters predict survival in multiple myeloma patients without autologous stem cell transplantation.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.Bioengineering (Basel, Switzerland) · 2023Review
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Authors and funding
7 authors.
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Abstract
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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