Abstract
Background
The challenge of treating epithelial ovarian cancer (EOC) is significantly heightened by peritoneal metastasis. 18F-FDG PET/CT is employed as a preoperative assessment method for evaluating the extent of peritoneal spread in EOC, while peritoneal cancer index (PCI) serves as a vital tool in assessing peritoneal dissemination during surgery. We sought to investigate the value of a PCI derived from 18F-FDG PET/CT (PET-PCI) in predicting tumor pathological grade, tumor burden, and completeness of cytoreductive surgery (CRS) in patients with EOC.
Methods
We conducted a retrospective analysis of 64 patients with the International Federation of Gynecology and Obstetrics (FIGO) stages III–IV or recurrent EOC with peritoneal metastasis who underwent 18F-FDG PET/CT before therapy. PET-PCI was calculated by summing the 18F-FDG uptake scores across 13 abdominopelvic regions. Among them, 23 patients underwent CRS within 2 months after 18F-PET/CT. The relationship between PET-PCI, histological type (I or II), and surgical PCI was analyzed, as was the ability of PET-PCI to predict the completeness of CRS.
Results
Pathological analysis revealed 14 patients with type I and 50 patients with type II tumors. Compared to patients with type I tumors, those with type II tumors exhibited higher PET-PCI values (19.0 ± 11.1 vs. 12.4 ± 11.5 points, p = 0.022). Setting a cutoff of 15 points for PET-PCI to identify type II EOC resulted in a sensitivity of 56.0%, a specificity of 78.6%, and an AUC of 0.701 (p = 0.023). PET-PCI exhibited a positive correlation with surgical PCI (r = 0.885, p < 0.001). PET-PCI was a significant predictor of CRS completeness, with an AUC of 0.967 (p = 0.004). The cutoff value of 16 for PET-PCI facilitated the identification of CRS completeness in EOC patients, providing a sensitivity of 84.2% and a specificity of 100%.
Conclusions
This study demonstrated that PET-PCI is a valuable parameter in predicting tumor grade and burden in patients with advanced EOC. Moreover, PET-PCI may serve as a tool for predicting CRS completeness.