Performance of integrated FDG–PET/contrast-enhanced CT in the diagnosis of recurrent ovarian cancer: comparison with integrated FDG–PET/non-contrast-enhanced CT and enhanced CT
The aim of this study was to evaluate the accuracy of integrated positron emission tomography and computed tomography (PET/CT) using 18F-fluorodeoxyglucose with IV contrast for depiction of suspected recurrent ovarian cancer and to assess the impact of PET/contrast-enhanced CT findings on clinical management, compared with PET/non-contrast-enhanced CT and CT component.
One hundred thirty-two women previously treated for ovarian cancer underwent PET/CT consisting of non-enhanced and contrast-enhanced CT for suspected recurrence. PET/contrast enhanced CT, PET/non-contrast-enhanced CT, and enhanced CT were interpreted by two experienced radiologists by consensus for each investigation. Lesion status was determined on the basis of histopathology, radiological imaging, and clinical follow-up for longer than 6 months.
Patient-based analysis showed that the sensitivity, specificity, and accuracy of PET/contrast-enhanced CT were 78.8% (52 of 66), 90.9% (60 of 66), and 84.8% (112 of 132), respectively, whereas those of PET/non-contrast-enhanced CT were 74.2% (49 of 66), 90.9% (60 of 66), and 82.6% (109 of 132), respectively, and those of enhanced CT were 60.6% (40 of 66), 84.8% (56 of 66), and 72.7% (96 of 132), respectively. Sensitivity, specificity, and accuracy differed significantly among the three modalities (Cochran Q test: p = 0.0001, p = 0.018, and p < 0.0001, respectively). The findings of PET/contrast-enhanced CT resulted in a change of management for 51 of the 132 patients (39%) and had an effect on patient management in 16 patients (12%) diagnosed by enhanced CT alone and three patients (2%) diagnosed by PET/non-contrast-enhanced CT.
Integrated PET/contrast-enhanced CT is an accurate modality for assessing ovarian cancer recurrence and led to changes in the subsequent appropriate therapy.
KeywordsOvarian cancer Recurrence 18F-FDG PET/CT
We thank Kennichi Kobayashi, Kouichi Asano, Kazufumi Suzuki, Kaoru Ishida, and Tomoyuki Sakamoto for their excellent technical assistance and generous support.
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