European journal of nuclear medicine and molecular imaging · 2026
Discriminating inflammation from malignancy with short-dynamic patlak parametric 18 F-FDG PET/CT.
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Jandric Jelena, Leonardi Lorenzo, Barisonzi Rossella, Rodari Marcello, Evangelista Laura, Artesani Alessia
Аннотация
PURPOSE: Differentiating malignant from inflammatory uptake on 18F-FDG PET/CT remains a major diagnostic challenge, as standardised uptake value (SUV) lacks specificity. This study evaluated whether parametric imaging from short-duration dynamic FDG PET/CT provides complementary information beyond SUV for distinguishing malignancy from inflammation.
METHODS: Twenty-eight patients undergoing oncologic PET/CT (breast, lung, lymphatic or gastrointestinal cancer) were included, yielding 68 lesions (43 malignant, 25 inflammatory). Short dynamic acquisitions (20 min) were motion-corrected and used to generate influx rate (Ki) and distribution volume (Vd) maps. Lesions were segmented on SUV images (40% SUVmax), and radiomic features were extracted from SUV, Ki, and Vd maps, including core and peritumoral regions. Classification performance was assessed both using logistic regression and Random Forest model.
RESULTS: Malignant lesions exhibited higher mean values than inflammatory lesions for both SUV (6.0 vs. 2.7 g/ml) and Ki (2.0 vs. 0.8 ml/min/100 ml), while Vd values largely overlapped between classes (50 vs. 47%). No single parameter provided a reliable discriminative threshold. Restricting the analysis to equivocal SUV values (< 5.2 g/ml), multivariate regression combining SUV Mean, Ki/Vd Variance, and Ki Entropy achieved an accuracy of 0.86 (pseudo-R² = 0.42), outperforming SUV Mean alone (accuracy 0.77, pseudo-R² = 0.26). Core-peritumoral analysis revealed as Ki/Vd Variance the most statistically significant features between the two classes (4.35 ± 3.36 malignant vs. 1.93 ± 1.34 inflammatory; p = 0.006). Random Forest classification confirmed superior performance of parametric features (ROC AUC 0.86 ± 0.10) compared with SUV-only models (0.83 ± 0.08).
CONCLUSION: Short dynamic Patlak FDG PET/CT improves differentiation of malignant from inflammatory uptake beyond SUV alone. Decomposing FDG uptake into metabolised (Ki) and unmetabolised (Vd) fractions, components provide physiologically meaningful insights, revealing steeper core-to-peritumoral metabolic gradients in malignancy and more homogeneous tracer distribution in inflammation. These findings support the added value of parametric imaging and motivate prospective validation in larger clinical cohorts.
METHODS: Twenty-eight patients undergoing oncologic PET/CT (breast, lung, lymphatic or gastrointestinal cancer) were included, yielding 68 lesions (43 malignant, 25 inflammatory). Short dynamic acquisitions (20 min) were motion-corrected and used to generate influx rate (Ki) and distribution volume (Vd) maps. Lesions were segmented on SUV images (40% SUVmax), and radiomic features were extracted from SUV, Ki, and Vd maps, including core and peritumoral regions. Classification performance was assessed both using logistic regression and Random Forest model.
RESULTS: Malignant lesions exhibited higher mean values than inflammatory lesions for both SUV (6.0 vs. 2.7 g/ml) and Ki (2.0 vs. 0.8 ml/min/100 ml), while Vd values largely overlapped between classes (50 vs. 47%). No single parameter provided a reliable discriminative threshold. Restricting the analysis to equivocal SUV values (< 5.2 g/ml), multivariate regression combining SUV Mean, Ki/Vd Variance, and Ki Entropy achieved an accuracy of 0.86 (pseudo-R² = 0.42), outperforming SUV Mean alone (accuracy 0.77, pseudo-R² = 0.26). Core-peritumoral analysis revealed as Ki/Vd Variance the most statistically significant features between the two classes (4.35 ± 3.36 malignant vs. 1.93 ± 1.34 inflammatory; p = 0.006). Random Forest classification confirmed superior performance of parametric features (ROC AUC 0.86 ± 0.10) compared with SUV-only models (0.83 ± 0.08).
CONCLUSION: Short dynamic Patlak FDG PET/CT improves differentiation of malignant from inflammatory uptake beyond SUV alone. Decomposing FDG uptake into metabolised (Ki) and unmetabolised (Vd) fractions, components provide physiologically meaningful insights, revealing steeper core-to-peritumoral metabolic gradients in malignancy and more homogeneous tracer distribution in inflammation. These findings support the added value of parametric imaging and motivate prospective validation in larger clinical cohorts.