Optimal peritoneal cancer index cutoff point for predicting surgical resectability of pseudomyxoma peritonei in treatment-naive patients - Mingjian Bai; Yunxiang Li; Hairong Pu; Yueming Xu; Jingliang Chen; Hongbin Xu; Hongjiang Wei; Guowei Liang; Ruiqing Ma; Jing Feng (World Journal of Surgical Oncology, 2024)
Abstract Background The peritoneal cancer index (PCI) has been used to predict surgical outcomes for pseudomyxoma peritonei (PMP). The present study aimed to establish the optimal cutoff point for PCI to predict surgical resectability of PMP. Methods A total of 366 PMP patients were included. The patients were divided into low-grade and high-grade groups. Based on the completeness of the cytoreduction (CC) score, both low-grade and high-grade PMP patients were further divided into complete cytoreductive surgery (CRS) and maximal tumor debulking (MTD) subgroups. The ability to predict surgical resectability of total and selected PCI (regions 2 + 9 to 12) was analyzed through receiver operating characteristic (ROC) curves. Results Both total and selected PCI demonstrated excellent discriminative ability in predicting surgical resectability for low-grade PMP patients (n = 266), with the ROC-AUC of 0.940 (95% CI: 0.904–0.965) and 0.927 (95% CI: 0.889–0.955). The corresponding optimal cutoff point was 21 and 5, respectively. For high-grade PMP patients (n = 100), both total and selected PCI exhibited good performance in predicting surgical resectability, with the ROC-AUC of 0.894 (95% CI: 0.816–0.946) and 0.888 (95% CI: 0.810–0.943); correspondingly, the optimal cutoff point was 25 and 8, respectively. The discriminative ability between total and selected PCI in predicting surgical resectability did not show a statistical difference. Conclusions Both total and selected PCI exhibited good performance and similarity in predicting complete surgical resection for both low-grade and high-grade PMP patients. However, the selected PCI was simpler and time-saving in clinical practice. In the future, new imaging techniques or predictive models may be developed to better predict PCI preoperatively, which might assist in confirming whether complete surgical resection can be achieved.