ArticleCancers2020
[18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 32 citations in OpenAlex.
- An interpretable PET/CT-based radiomic-clinical model for predicting bone marrow involvement in follicular lymphoma: comparison of pelvic and spine-pelvis VOI frameworks.European journal of nuclear medicine and molecular imaging · 2026Article
- In mantle cell lymphoma, whether 18 F-FDG PET/CT or flow cytometry provide superior diagnostic performance compared to biopsy in the determination of bone marrow involvement and enhance prognostic predictive value.Annals of hematology · 2026Review
- Radiogenomics in Lymphoma and Multiple Myeloma: A Systematic Review of Current Evidence and Future Directions.Journal of clinical medicine · 2026Review
- Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.BMC medicine · 2026Article
- Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based onAnnals of nuclear medicine · 2025Article
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Circulating tumor DNA in diffuse large B-cell lymphoma: analysis of response assessment, correlation with PET/CT and clone evolution.Hematology, transfusion and cell therapy · 2024Article
- Review
- Radiomics models to predict bone marrow metastasis of neuroblastoma using CT.Cancer innovation · 2024Article
- Long-Axial Field-of-View PET Imaging in Patients with Lymphoma: Challenges and Opportunities.PET clinics · 2024Review
- Semiquantitative 2-[Frontiers in medicine · 2024Review
- Machine-learning radiomics to predict bone marrow metastasis of neuroblastoma using magnetic resonance imaging.Cancer innovation · 2023Article
- Impact of ComBat Harmonization on PET Radiomics-Based Tissue Classification: A Dual-Center PET/MRI and PET/CT Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma.Cancers · 2022Article
- Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [European journal of nuclear medicine and molecular imaging · 2022Article
- Comparison of FDG PET/CT and Bone Marrow Biopsy Results in Patients with Diffuse Large B Cell Lymphoma with Subgroup Analysis of PET Radiomics.Diagnostics (Basel, Switzerland) · 2022Article
- Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy.Frontiers in oncology · 2022Article
- FDG-PET/CT in Lymphoma: Where Do We Go Now?Cancers · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
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.
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