Prospective comparison of 18 F-FDG PET/MRI and 18 F-FDG PET/CT for thoracic staging of non-small cell lung cancer.

Abstract

Objectives: To compare the diagnostic performance of 18F-FDG PET/MRI and 18F-FDG PET/CT for primary and locoregional lymph node staging in non-small cell lung cancer (NSCLC). Methods: In this prospective study, a total of 84 patients (51 men, 33 women, mean age 62.5 ± 9.1 years) with histopathologically confirmed NSCLC underwent 18F-FDG PET/CT followed by 18F-FDG PET/MRI in a single injection protocol. Two readers independently assessed T and N staging in separate sessions according to the seventh edition of the American Joint Committee on Cancer staging manual for 18F-FDG PET/CT and 18F-FDG PET/MRI, respectively. Histopathology as a reference standard was available for N staging in all 84 patients and for T staging in 39 patients. Differences in staging accuracy were assessed by McNemars chi2 test. The maximum standardized uptake value (SUVmax) and longitudinal diameters of primary tumors were correlated using Pearson’s coefficients. Results: T stage was categorized concordantly in 18F-FDG PET/MRI and 18F-FDG PET/CT in 38 of 39 (97.4%) patients. Herein, 18F-FDG PET/CT and 18F-FDG PET/MRI correctly determined the T stage in 92.3 and 89.7% of patients, respectively. N stage was categorized concordantly in 83 of 84 patients (98.8%). 18F-FDG PET/CT correctly determined the N stage in 78 of 84 patients (92.9%), while 18F-FDG PET/MRI correctly determined the N stage in 77 of 84 patients (91.7%). Differences between 18F-FDG PET/CT and 18F-FDG PET/MRI in T and N staging accuracy were not statistically significant (p > 0.5, each). Tumor size and SUVmax measurements derived from both imaging modalities exhibited excellent correlation (r = 0.963 and r = 0.901, respectively). Conclusion: 18F-FDG PET/MRI and 18F-FDG PET/CT show an equivalently high diagnostic performance for T and N staging in patients suffering from NSCLC.

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Felix Nensa
Felix Nensa
Lead

My research interests include medical digitalization, computer vision and radiology.