Imaging of Peritoneal Metastases in Ovarian Cancer Using MDCT, MRI, and FDG PET/CT: A Systematic Review and Meta-Analysis.
Athina C TsiliGeorgios A AlexiouMartha TzoumpaTimoleon SiempisMaria I ArgyropoulouPublished in: Cancers (2024)
This review aims to compare the diagnostic performance of multidetector CT (MDCT), MRI, including diffusion-weighted imaging, and FDG PET/CT in the detection of peritoneal metastases (PMs) in ovarian cancer (OC). A comprehensive search was performed for articles published from 2000 to February 2023. The inclusion criteria were the following: diagnosis/suspicion of PMs in patients with ovarian/fallopian/primary peritoneal cancer; initial staging or suspicion of recurrence; MDCT, MRI and/or FDG PET/CT performed for the detection of PMs; population of at least 10 patients; surgical results, histopathologic analysis, and/or radiologic follow-up, used as reference standard; and per-patient and per-region data and data for calculating sensitivity and specificity reported. In total, 33 studies were assessed, including 487 women with OC and PMs. On a per-patient basis, MRI ( p = 0.03) and FDG PET/CT ( p < 0.01) had higher sensitivity compared to MDCT. MRI and PET/CT had comparable sensitivities ( p = 0.84). On a per-lesion analysis, no differences in sensitivity estimates were noted between MDCT and MRI ( p = 0.25), MDCT and FDG PET/CT ( p = 0.68), and MRI and FDG PET/CT ( p = 0.35). Based on our results, FDG PET/CT and MRI are the preferred imaging modalities for the detection of PMs in OC. However, the value of FDG PET/CT and MRI compared to MDCT needs to be determined. Future research to address the limitations of the existing studies and the need for standardization and to explore the cost-effectiveness of the three imaging modalities is required.
Keyphrases
- contrast enhanced
- diffusion weighted imaging
- magnetic resonance imaging
- computed tomography
- pet ct
- high resolution
- randomized controlled trial
- end stage renal disease
- newly diagnosed
- positron emission tomography
- lymph node
- chronic kidney disease
- case report
- ejection fraction
- machine learning
- young adults
- data analysis
- label free