[18F]FDG-PET/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: A retrospective study in 97 patients Journal Article


Authors: Mayerhoefer, M. E.; Riedl, C. C.; Kumar, A.; Dogan, A.; Gibbs, P.; Weber, M.; Staber, P. B.; Huicochea Castellanos, S.; Schöder, H.
Article Title: [18F]FDG-PET/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: A retrospective study in 97 patients
Abstract: 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: bone marrow; lymphoma; fdg; pet/ct
Journal Title: Cancers
Volume: 12
Issue: 5
ISSN: 2072-6694
Publisher: MDPI  
Date Published: 2020-05-01
Start Page: 1138
Language: English
DOI: 10.3390/cancers12051138
PROVIDER: scopus
PUBMED: 32370121
DOI/URL:
Notes: Article -- Source: Scopus
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MSK Authors
  1. Christopher Riedl
    50 Riedl
  2. Heiko Schoder
    332 Schoder
  3. Anita Kumar
    67 Kumar
  4. Ahmet Dogan
    230 Dogan
  5. Peter Gibbs
    7 Gibbs