Browsing by Author "Bilen, Erkan"

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    Clinical Utility of Pan-Immune Inflammation Value (PIV) in Predicting Prognosis of Endometrial Cancer
    (MDPI, 2025) Onal Kalkan, Nurhan; Urakci, Zuhat; Mermit Ercek, Berrak; Bilen, Erkan; Arvas, Hayati; Akkus, Mehmet Hadi
    Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries. While early-stage disease has favorable outcomes, advanced or recurrent EC remains associated with poor prognosis. Novel prognostic markers are needed to refine risk stratification. Systemic inflammation-based indices such as Pan-Immune Inflammation Value (PIV), Systemic Inflammation Response Index (SIRI), and Systemic Immune Inflammation Index (SII) have shown prognostic potential in solid tumors. Methods: We retrospectively evaluated 78 patients with endometrioid EC who had undergone hysterectomy with adnexectomy and lymphadenectomy. Demographic, clinicopathological, and laboratory data were extracted from electronic medical records. PIV, SII, and SIRI were calculated from the preoperative complete blood counts. Survival was assessed using Kaplan-Meier analysis, while prognostic factors were determined using univariate and multivariate Cox regression analyses. Results: The median age was 59 years, and 64.1% of the patients presented with early-stage disease. A high PIV (>= 802) was significantly associated with a shorter overall survival (64 vs. 111 months, p < 0.001). PIV demonstrated the highest discriminatory accuracy (AUC = 0.776), followed by the SII (0.747) and SIRI (0.718). Univariate analysis identified that age, grade, LVSI, PNI, stage, distant metastasis, and high PIV, SII, SIRI, and NLR were predictors of poor survival. Multivariate analysis confirmed grade, distant metastasis and SIRI >= 1.5 as independent prognostic factors. Conclusions: Inflammation-based indices, particularly PIV and SIRI, correlated with survival outcomes in patients with EC. The SIRI retained an independent prognostic value, whereas PIV showed a strong discriminatory capacity. Incorporating these indices into established risk models may improve prognostic precision and support individualized management.