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Segmentation method comparison for baseline [<sup>18</sup>F]FDG PET-CT in follicular lymphoma patients.

February 8, 2026pubmed logopapers

Authors

Nijman ADM,Wiegers SE,Zwezerijnen GJC,Verweij L,Bes AL,Hüttmann A,Dührsen U,Kurch L,Kersten MJ,Heymans MW,Zijlstra JM,Boellaard R

Affiliations (11)

  • Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. [email protected].
  • Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. [email protected].
  • Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands. [email protected].
  • Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
  • Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
  • Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Department of Hematology and Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
  • Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany.
  • Department of Anesthesiology and Operative Intensive Care, University Hospital Halle (Saale), Halle (Saale), Germany.
  • Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
  • Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Abstract

Follicular lymphoma (FL) is the second most common subtype of non-Hodgkin lymphoma. Currently, [<sup>18</sup>F]FDG PET-CT is used for staging, response evaluation, and remission assessment. While advances in quantitative PET-CT are promising for prognostic assessment, they depend on reproducible tumor delineation. Various segmentation methods have been proposed, but their application to FL PET is less established, despite known differences in uptake patterns across lymphoma subtypes. This study aims to evaluate the performance of several single-threshold and multi-threshold methods for FL [<sup>18</sup>F]FDG PET-CT lesion segmentation on segmentation quality, interobserver variability, and ease-of-use. Baseline PET-CT data of 25 second-line FL patients from the HOVON110 trial and 12 first-line FL patients from the PETAL trial were selected. Two observers applied 13 different semi-automatic methods, of which six used a single threshold and seven combined thresholds (multi-threshold). Methods include, SUV threshold methods, an AI-based method, majority vote and lesion-based selection methods. The segmentation process comprises four steps: step 1 and 2 involved generating a preselection, while step 3 and 4 applied an automatic method followed by manual adjustments. To assess segmentation quality, both observers gave a score (1-3) ranging from undersegmentation to oversegmentation. For interobserver variability, the difference in total metabolic tumor volume between observers was determined. The ease-of-use was assessed based on manually added and removed volume in step 4. A total of 962 segmentations were made by two observers. Differences in results between the methods were limited across all characteristics, indicating an overall satisfactory performance of all methods. The multi-threshold method scored better for segmentation quality in comparison to single-threshold methods, indicating less under- or oversegmentation. The single-threshold method SUV4.0 demonstrated lower median (0.3 mL) and inter quartile range (2.0 mL) concerning interobserver variability in comparison to lesion-based methods. Among the single threshold methods, SUV4.0 is preferred regarding ease-of-use, observer variability and segmentation quality. While the multi-threshold lesion-based methods showed the a higher segmentation quality, SUV4.0 has the benefit of easy implementation, wide availability and is in-line with the currently set benchmark for lymphoma PET analysis. We identified SUV4.0 and a lesion-based method as the candidate methods preferred for further clinical performance evaluation.

Topics

Journal Article

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