ArticleEJNMMI research2022
Combatting the effect of image reconstruction settings on lymphoma [
Article in EJNMMI research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 24 citations in OpenAlex.
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- Prognostic impact of metabolic tumor volume using the SUV4.0 segmentation threshold in 1,960 lymphoma patients from prospective LYSA trials.European journal of nuclear medicine and molecular imaging · 2025Article
- Prognostic Value of Multiple Manual Segmentation Methods for Diffuse Large B-Cell Lymphoma withCurrent oncology (Toronto, Ont.) · 2025Article
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- International Benchmark for Total Metabolic Tumor Volume Measurement in BaselineJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy.European journal of nuclear medicine and molecular imaging · 2024Article
- IsJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Quantitative and clinical implications of the EARL2 versus EARL1 [EJNMMI research · 2023Article
- Sensitivity of an AI method for [EJNMMI research · 2023Article
- Advances in positron emission tomography and radiomics.Hematological oncology · 2023Review
- Metabolic bulk volume predicts survival in a homogeneous cohort of stage II/III diffuse large B-cell lymphoma patients undergoing R-CHOP treatment.Frontiers in oncology · 2023Article
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- Semi-automatedComputational and structural biotechnology journal · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background[
methodsFifty-six lesions were segmented from baseline [
resultsMTVs from the SUV4.0 method were least sensitive to the use of different reconstructions (MTV ratio: median = 1.01, interquartile range = [0.96-1.10]). After ComBat harmonization, an improved agreement of MTVs among different reconstructions was found for most segmentation methods. The regular implementation of ComBat ('Regular ComBat') using non-transformed distributions resulted in less accurate and precise MTV alignments than a version using log-transformed datasets ('Log-transformed ComBat').
conclusionMTV depends on both segmentation method and reconstruction methods. ComBat reduces reconstruction dependent MTV variability, especially when log-transformation is used to account for the non-normal distribution of MTVs.
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