Purpose High Stability of radiomic features is critical for developing reliable imaging biomarkers that can support risk stratification, treatment response assessment, and personalized therapy in lymphoma patients. To evaluate how partial volume correction (PVC) affects the Stability of 18F-FDG PET radiomic features in lymphoma lesions, with respect to lesion volume and tissue type. Methods This single-center retrospective study included 131 newly diagnosed lymphoma patients (2014–2024) who underwent baseline 18F-FDG PET/CT. In total, 1,603 lesions (1,302 lymph nodes, 117 spleen/liver, 150 bone, and 34 bone and soft-tissue) were semi-automatically segmented and grouped by volume (< 3, 3–10, 10–30, > 30 mL) and tissue type. Ninety-three radiomic features were extracted from non-PVC and PVC images processed with the Richardson–Lucy (RL) and Reblurred Van Cittert (RVC) algorithms after isotropic resampling (3 mm) and discretization (0.25 SUV bin size), following IBSI guidelines. Stability was quantified using the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC2, absolute agreement), with statistical comparisons performed via Mann–Whitney U tests and false discovery rate (FDR) correction. Results PVC significantly improved feature Stability, particularly for large lesions (> 30 mL), with median ICC2 > 0.90 across most feature categories (e.g., First-Order = 0.99, GLSZM = 0.97, NGTDM = 0.97). Small lesions (< 3 mL) showed lower stability (ICC2 = 0.84–0.94) and higher CoV (0.09–0.21), mainly in texture-based features. First-Order and GLCM features were the most robust overall (ICC2 = 0.92–0.99; CoV = 0.07–0.11). Bone and spleen lesions exhibited the highest Stability (median ICC2 ≈ 0.95), whereas lymph node and liver features were more variable. All volume- and tissue-dependent differences remained significant after FDR correction (p < 0.05). Conclusion PVC using RL and RVC markedly enhances FDG-PET radiomic Stability in lymphoma, particularly for larger and structurally uniform lesions. Robust features such as First-Order and GLCM can support standardized radiomics workflows and the development of reliable biomarkers for prognosis and personalized therapy. Additionally, PVC reduces variability in texture features, especially in small or heterogeneous lesions. Multicenter validation would further strengthen generalizability beyond this single-center setting.

Evaluating the impact of partial volume correction on FDG PET radiomics stability in lymphoma lesions

期刊论文
发表日期：

2026年3月9日

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Setareh Hasanabadi,
Mohammad Saber Azimi,
Mehrdad Bakhshayesh Karam,
Hossein Arabi

摘要

Purpose

High Stability of radiomic features is critical for developing reliable imaging biomarkers that can support risk stratification, treatment response assessment, and personalized therapy in lymphoma patients. To evaluate how partial volume correction (PVC) affects the Stability of ¹⁸F-FDG PET radiomic features in lymphoma lesions, with respect to lesion volume and tissue type.

Methods

This single-center retrospective study included 131 newly diagnosed lymphoma patients (2014–2024) who underwent baseline ¹⁸F-FDG PET/CT. In total, 1,603 lesions (1,302 lymph nodes, 117 spleen/liver, 150 bone, and 34 bone and soft-tissue) were semi-automatically segmented and grouped by volume (< 3, 3–10, 10–30, > 30 mL) and tissue type. Ninety-three radiomic features were extracted from non-PVC and PVC images processed with the Richardson–Lucy (RL) and Reblurred Van Cittert (RVC) algorithms after isotropic resampling (3 mm) and discretization (0.25 SUV bin size), following IBSI guidelines. Stability was quantified using the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC₂, absolute agreement), with statistical comparisons performed via Mann–Whitney U tests and false discovery rate (FDR) correction.

Results

PVC significantly improved feature Stability, particularly for large lesions (> 30 mL), with median ICC₂ > 0.90 across most feature categories (e.g., First-Order = 0.99, GLSZM = 0.97, NGTDM = 0.97). Small lesions (< 3 mL) showed lower stability (ICC₂ = 0.84–0.94) and higher CoV (0.09–0.21), mainly in texture-based features. First-Order and GLCM features were the most robust overall (ICC₂ = 0.92–0.99; CoV = 0.07–0.11). Bone and spleen lesions exhibited the highest Stability (median ICC₂ ≈ 0.95), whereas lymph node and liver features were more variable. All volume- and tissue-dependent differences remained significant after FDR correction (p < 0.05).

Conclusion

PVC using RL and RVC markedly enhances FDG-PET radiomic Stability in lymphoma, particularly for larger and structurally uniform lesions. Robust features such as First-Order and GLCM can support standardized radiomics workflows and the development of reliable biomarkers for prognosis and personalized therapy. Additionally, PVC reduces variability in texture features, especially in small or heterogeneous lesions. Multicenter validation would further strengthen generalizability beyond this single-center setting.