» Articles » PMID: 36143386

Can Dynamic Whole-Body FDG PET Imaging Differentiate Between Malignant and Inflammatory Lesions?

Abstract

Background: Investigation of the clinical feasibility of dynamic whole-body (WB) [18F]FDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB [18F]FDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images. Maximum parameter values (SUVmax, Kimax, and Vdmax) were measured in liver parenchyma and in malignant or inflammatory/infectious lesions. Lesion-to-background ratios (LBRs) were calculated by dividing the measurements by their respective mean in the liver tissue. Results: Seventy-seven clinical target lesions were identified, 60 malignant and 17 inflammatory/infectious. Kimax was significantly higher in cancer than in inflammatory/infections lesions (3.0 vs. 2.0, p = 0.002) while LBRs of SUVmax, Kimax, and Vdmax did not differ significantly between the etiologies: LBR (SUVmax) 3.3 vs. 2.9, p = 0.06; LBR (Kimax) 5.0 vs. 4.4, p = 0.05, LBR (Vdmax) 1.1 vs. 1.0, p = 0.18). LBR of inflammatory/infectious and cancer lesions was higher in Kimax than in SUVmax (4.5 vs. 3.2, p < 0.001). LBRs of Kimax and SUVmax showed a strong correlation (Spearman’s rho = 0.83, p < 0.001). Conclusions: Dynamic WB [18F]FDG PET/CT is feasible in a clinical setting. LBRs of Kimax were higher than SUVmax. Kimax was higher in malignant than in inflammatory/infectious lesions but demonstrated a large overlap between the etiologies.

Citing Articles

Fluorodeoxyglucose Positron Emission Tomography Evaluation of Chronic Recurrent Multifocal Osteomyelitis.

Hirota R, Emori M, Teramoto A Cureus. 2024; 16(9):e69735.

PMID: 39429331 PMC: 11490289. DOI: 10.7759/cureus.69735.


Case report: When infection lurks behind malignancy: a unique case of primary bone lymphoma mimicking infectious process in the spine.

Jaafari A, Rizzo O, Mansour S, Chbabou A, Trepant A, Attou R Front Nucl Med. 2024; 4:1402552.

PMID: 39355207 PMC: 11440879. DOI: 10.3389/fnume.2024.1402552.


Assessment of image-derived input functions from small vessels for patlak parametric imaging using total-body PET/CT.

Tang H, Wu Y, Cheng Z, Song S, Dong Q, Zhou Y Eur J Nucl Med Mol Imaging. 2024; 52(2):648-659.

PMID: 39325156 PMC: 11732897. DOI: 10.1007/s00259-024-06926-0.


Long-Axial Field-of-View PET Imaging in Patients with Lymphoma: Challenges and Opportunities.

Mingels C, Nalbant H, Sari H, Godinez F, Sen F, Spencer B PET Clin. 2024; 19(4):495-504.

PMID: 38969563 PMC: 11433941. DOI: 10.1016/j.cpet.2024.05.005.


Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions.

Du F, Wumener X, Zhang Y, Zhang M, Zhao J, Zhou J EJNMMI Phys. 2024; 11(1):23.

PMID: 38441830 PMC: 10914647. DOI: 10.1186/s40658-024-00625-3.


References
1.
Butof R, Hofheinz F, Zophel K, Schmollack J, Jentsch C, Zschaeck S . Prognostic value of SUR in patients with trimodality treatment of locally advanced esophageal carcinoma. J Nucl Med. 2018; . PMC: 8833854. DOI: 10.2967/jnumed.117.207670. View

2.
Naganawa M, Gallezot J, Shah V, Mulnix T, Young C, Dias M . Assessment of population-based input functions for Patlak imaging of whole body dynamic F-FDG PET. EJNMMI Phys. 2020; 7(1):67. PMC: 7683759. DOI: 10.1186/s40658-020-00330-x. View

3.
Dias A, Pedersen M, Danielsen H, Munk O, Gormsen L . Clinical feasibility and impact of fully automated multiparametric PET imaging using direct Patlak reconstruction: evaluation of 103 dynamic whole-body F-FDG PET/CT scans. Eur J Nucl Med Mol Imaging. 2020; 48(3):837-850. DOI: 10.1007/s00259-020-05007-2. View

4.
Zaker N, Kotasidis F, Garibotto V, Zaidi H . Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping. Clin Nucl Med. 2020; 45(5):e221-e231. DOI: 10.1097/RLU.0000000000002954. View

5.
Nikulin P, Hofheinz F, Maus J, Li Y, Butof R, Lange C . A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET. Eur J Nucl Med Mol Imaging. 2020; 48(4):995-1004. PMC: 8041711. DOI: 10.1007/s00259-020-04991-9. View