[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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