Diagnostic Performance of Dynamic Whole-Body Patlak [18F]FDG-PET/CT in Patients with Indeterminate Lung Lesions and Lymph Nodes

被引:2
|
作者
Weissinger, Matthias [1 ,2 ]
Atmanspacher, Max [1 ]
Spengler, Werner [3 ]
Seith, Ferdinand [2 ]
Von Beschwitz, Sebastian [1 ]
Dittmann, Helmut [1 ]
Zender, Lars [3 ]
Smith, Anne M. [4 ]
Casey, Michael E. [4 ]
Nikolaou, Konstantin [2 ,5 ,6 ]
Castaneda-Vega, Salvador [1 ,7 ]
la Fougere, Christian [1 ,5 ,6 ]
机构
[1] Univ Hosp Tuebingen, Dept Nucl Med & Clin Mol Imaging, D-72076 Tubingen, Germany
[2] Univ Hosp Tuebingen, Dept Diagnost & Intervent Radiol, D-72076 Tubingen, Germany
[3] Univ Hosp Tuebingen, Dept Internal Med 8, D-72076 Tubingen, Germany
[4] Siemens Med Solut USA Inc, Mol Imaging, Knoxville, TN 37932 USA
[5] Eberhard Karls Univ Tuebingen, iFIT Cluster Excellence, D-72076 Tubingen, Germany
[6] German Canc Consortium DKTK, Partner Site Tuebingen, D-72076 Tubingen, Germany
[7] Eberhard Karls Univ Tuebingen, Werner Siemens Imaging Ctr, Dept Preclin Imaging & Radiopharm, D-72076 Tubingen, Germany
关键词
whole-body; dynamic PET; parametric FDG; Patlak; FDG; PET; CT; BRAIN TRANSFER CONSTANTS; GRAPHICAL EVALUATION; CANCER;
D O I
10.3390/jcm12123942
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Static [F-18]FDG-PET/CT is the imaging method of choice for the 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: A total of 34 consecutive patients with indeterminate pulmonary lesions were enrolled in this prospective trial. All patients underwent static (60 min p.i.) and dynamic (0-60 min p.i.) whole-body [F-18]FDG-PET/CT (300 MBq) using the multi-bed-multi-timepoint technique (Siemens mCT FlowMotion). Histology and follow-up served as ground truth. Kinetic modeling factors were calculated using a two-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-FDG(mean) provided the best discriminatory power between benign and malignant lung lesions with an AUC of 0.887. The AUC of DV-FDG(mean) (0.818) and SUVmean (0.827) was non-significantly lower. For LNM, the AUCs for MR-FDG(mean) (0.987) and SUVmean (0.993) were comparable. Moreover, the DV-FDG(mean) 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 malignant lung tumors, LNM, and distant metastases at least as accurately as the established SUV or dual-time-point PET scans.
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页数:16
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