Discriminating inflammation from malignancy with short-dynamic patlak parametric 18 F-FDG PET/CT.

Area of Science:

• Nuclear Medicine
• Radiology
• Oncology

Background:

• Differentiating malignant from inflammatory lesions using ¹⁸F-FDG PET/CT is challenging due to the limited specificity of standardized uptake value (SUV).
• Parametric imaging derived from dynamic PET scans offers potential for more specific quantitative analysis beyond static SUV measurements.

Purpose of the Study:

• To evaluate if parametric imaging from short-duration dynamic ¹⁸F-FDG PET/CT provides complementary information to SUV for distinguishing malignancy from inflammation.
• To assess the diagnostic performance of influx rate (Ki) and distribution volume (Vd) maps in classifying lesions.

Main Methods:

• Twenty-eight cancer patients with 68 lesions (43 malignant, 25 inflammatory) underwent short dynamic (20 min) ¹⁸F-FDG PET/CT acquisitions.
• Motion-corrected dynamic data were used to generate influx rate (Ki) and distribution volume (Vd) parametric maps.
• Radiomic features were extracted from SUV, Ki, and Vd maps, and classification was performed using logistic regression and Random Forest models.

Main Results:

• Malignant lesions showed higher SUV and Ki values than inflammatory lesions, but Vd values overlapped.
• Multivariate analysis combining SUV Mean, Ki/Vd Variance, and Ki Entropy achieved 0.86 accuracy for equivocal SUV values, outperforming SUV Mean alone.
• Core-peritumoral analysis highlighted Ki/Vd Variance as a significant differentiator (p=0.006), and Random Forest models showed superior performance with parametric features (ROC AUC 0.86).

Conclusions:

• Short dynamic Patlak ¹⁸F-FDG PET/CT parametric imaging enhances the differentiation of malignant from inflammatory uptake beyond SUV.
• Ki and Vd fractions provide physiological insights, revealing steeper metabolic gradients in malignancy and homogeneous distribution in inflammation.
• Parametric imaging holds added value and warrants prospective validation in larger clinical studies.