ArticlePolish journal of radiology2020
Correlation of texture feature analysis with bone marrow infiltration in initial staging of patients with lymphoma using
Article in Polish journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.EJNMMI physics · 2026Article
- An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma viaBMC medical informatics and decision making · 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
- Review
- Semiquantitative 2-[Frontiers in medicine · 2024Review
- BaselineDiagnostics (Basel, Switzerland) · 2023Article
- Article
- 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
- Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases.Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, IndiaArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo explore whether radiomic features of fluorine-18-fluorodeoxyglucose ( MATERIAL AND
methodsForty-four patients (with pathologically proven lymphoma disease) underwent staging
resultsCorrelation between conventional PET metrics and features ranged between 0.50 and 0.97 for positive correlation (33 significant association features) and ranged from -0.52 to -0.97 for inverse correlation (three significant association features) for both strong and moderate correlations. Analysis of ROC curves showed that high-intensity long-run emphasis 4 bin, high-intensity large zone emphasis 64 bin, long-run emphasis (LRE) 64 bin, large-zone emphasis 64 bin, max spectrum 8 bin, busyness 64 bin, and code similarity 32 and 64 bin were significant discriminators of BMI among other features (area under curve > 0.682,
conclusions
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.