Dynamisches Ganzkörper-[18F]FDG-PET/CT bei Patienten/innen mit Bronchialkarzinom

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Zitierfähiger Link (URI): http://hdl.handle.net/10900/166577
http://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1665774
http://dx.doi.org/10.15496/publikation-107904
Dokumentart: Dissertation
Erscheinungsdatum: 2025-06-12
Originalveröffentlichung: Weissinger, M.; Atmanspacher, M.; Spengler, W.; Seith, F.; Von Beschwitz, S.; Dittmann, H.; Zender, L.; Smith, A.M.; Casey, M.E.; Nikolaou, K.; et al. Diagnostic Performance of Dynamic Whole-Body Patlak [18F]FDG-PET/CT in Patients with Indeterminate Lung Lesions and Lymph Nodes. J. Clin. Med. 2023, 12, 3942. https://doi.org/10.3390/jcm12123942
Sprache: Englisch
Fakultät: 4 Medizinische Fakultät
Fachbereich: Medizin
Gutachter: la Fougère, Chrstian (Prof. Dr.)
Tag der mündl. Prüfung: 2025-05-20
DDC-Klassifikation: 610 - Medizin, Gesundheit
Lizenz: http://tobias-lib.uni-tuebingen.de/doku/lic_ohne_pod.php?la=de http://tobias-lib.uni-tuebingen.de/doku/lic_ohne_pod.php?la=en
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Abstract:

Static [18F]FDG-PET/CT is the imaging method of choice for evaluation of indeterminate lung lesions and NSCLC staging, however, histological confirmation of PET-positive lesions is needed in most cases due to its limited specificity. Therefore, we aimed to evaluate the diagnostic performance of additional dynamic whole-body PET. Methods: 34 consecutive patients with indeterminate pulmonary lesions were enrolled in this prospective trial. All patients underwent a static (60 min p.i) and dynamic (0-60min p.i.) whole-body [18F]FDG-PET/CT (300 MBq) using multi-bed-multi-timepoint technique (Siemens mCT FlowMotion). Histology and follow-up served as ground truth. Kinetic modelling factors were calculated using a 2-compartment linear Patlak model (FDG influx rate constant =Ki, metabolic rate = MR-FDG, distribution volume = DV-FDG) and compared to SUV using ROC-analysis. Results: MR-FDGmean provided the best discriminatory power between benign and malignant lung lesions with AUC of 0.887. AUC of DV-FDGmean (0.818) and SUVmean (0.827) was non-significantly lower. For LNM, the AUCs for MR-FDGmean (0.987) and SUVmean (0.993) were comparable. Moreover, the DV-FDGmean in liver metastases was three times higher than in bone or lung metastases. Conclusions: Metabolic rate quantification was shown to be a reliable method to detect malign lung tumors, LNM and distant metastases at least as accurately as the established SUV or dual time-point PET scans.

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