We compared the preoperative values of mean platelet volume (MPV) and peripheral systemic inflammatory response (SIR) markers (neutrophil/lymphocyte ratio and platelet/lymphocyte ratio) between patients with advanced-stage (stage 3/4) endometriosis having endometrioma (OMA) and patients with a non-neoplastic adnexal mass other than endometrioma (non-OMA). Patients who underwent operations with the pre-diagnosis of infertility or adnexal mass and who underwent laparoscopic tubal ligation were included. Haemoglobin levels, leucocyte count, platelet count, neutrophil count and lymphocyte count were not significantly different between patients with advanced stage endometriosis having OMA, patients with non-OMA and patients in the control group (p=0.970, p=0.902, p=0.373, p=0.501 and p=0.463, respectively). Patients with stage 3/4 endometriosis having OMA, patients with non-OMA and control patients were also not significantly different in terms of MPV (p=0.836), neutrophil/lymphocyte ratio (NLR) (p=0.555) and platelet/lymphocyte ratio (PLR) (p=0.358). Preoperative cancer antigen 125 (Ca-125) levels were significantly higher in patients with OMA (p=0.006). Mean size of the OMAs was significantly lower than non-OMAs (p=0.000). It is very important to determine advanced stage endometriosis and OMAs during preoperative evaluation in order to inform patients and plan an appropriate surgical approach. We demonstrate that MPV, NLR and PLR values are not useful for this purpose in patients with advanced stage endometriosis that are proven to develop severe inflammation at either the cellular or molecular level.

The risk of malignancy index (RMI) for the evaluation of adnexal masses is a sensitive tool in certain populations. The best cut off value for RMI 1, 2 and 3 is 200. The cut off value of RMI-4 to differentiate benign from malignant lesions is 450. Our aim was to evaluate the efficiency of four different malignancy indexes (RMI1-4) in a homogeneous population. We evaluated a total of 153 non-pregnant women with adnexal masses who did not have a history of malignancy and who were above 18 years of age. A cut-off value of 250 for RMI-1 provided 95.9% inter-observer agreement, yielding 95.9% specificity, 93.5% negative predictive value, 75.0% sensitivity and 82.8% positive predictive value. A cut-off value of 250 for RMI-1 showed high performance in preoperative diagnosis of invasive malignant lesions than cut-off value of 200 in our population. A cut-off value of 350 for RMI-2 provided 94.5% inter-observed agreement, yielding 94.2% specificity, 93.4% negative predictive value, 75.0% sensitivity and 77.4% positive predictive value. RMI-2 showed the higher performance when the cut-off value was set at 350 in our population. A cut-off value of 250 provided 95.2% inter-observer agreement, yielding 95.0% specificity, 93.2% negative predictive value, 75.0% sensitivity, and 88.0% positive predictive value. RMI-3 showed the highest performance to diagnose malignant adnexal masses when the cut-off value was set at 250. In our study, RMI-4 showed similar statistical performance when the cut-off value was set at 400 [(Kappa: 0.684/p=0.000), yielding 93.8% inter-observer agreement, 93.4% specificity, 93.4% negative predictive value, 75.0% sensitivity, and 75.0% negative predictive value]. We showed successful utilization of RMIs in preoperative differentiation of benign from malignant masses. Many studies conducted in Asian and Pacific countries have reported different cut-off values as was the case in our study. We think that it is difficult to determine universally accepted cut-off values for RMIs for common use around the globe.

Background and Objectives: Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age. Women with PCOS often have metabolic disorders such as insulin resistance (IR), type 2 diabetes (T2DM), obesity, and metabolic syndrome (MetS). The assessment of visceral adiposity and dysfunctional adipose tissue is crucial for understanding the metabolic risks associated with PCOS. The visceral adiposity index (VAI) and the dysfunctional adiposity index (DAI) are two novel metabolic indices that more specifically assess adipose tissue dysfunction and visceral fat accumulation. This study aimed to evaluate the clinical utility of VAI and DAI as predictive markers for metabolic complications such as insulin resistance (IR) and metabolic syndrome (MetS) in women with PCOS. Materials and Methods: This cross-sectional study included 92 women diagnosed with PCOS based on the 2023 revised Rotterdam criteria, as well as 68 healthy controls. Anthropometric and biochemical parameters, including fasting glucose, insulin, lipid profile, and hormonal markers, were recorded. VAI and DAI were calculated using established formulas derived from previous validated studies. Results: The mean VAI in PCOS patients was 4.26 ± 3.23, compared to 2.61 ± 1.92 in controls (p = 0.003). The mean DAI in PCOS patients was 3.00 ± 1.86, while in controls it was 1.86 ± 1.22 (p = 0.003). Both VAI (Area Under the Curve [AUC] = 0.639) and DAI (AUC = 0.635) did not demonstrate statistically significant diagnostic performance for PCOS itself, but they were strongly associated with metabolic disturbances within the PCOS group. VAI and DAI values were significantly elevated in PCOS patients with IR (p < 0.001) and MetS (p < 0.001). For MetS in PCOS patients, VAI demonstrated the highest predictive ability, with an AUC of 0.87 and a cutoff of 4.73 (sensitivity 62%, specificity 92%), while DAI had an AUC of 0.86 with a cutoff of 2.44 (sensitivity 74%, specificity 80%). Regarding IR in PCOS patients, VAI had an AUC of 0.75 with a cutoff of 2.56 (sensitivity 82%, specificity 56%), while DAI had an AUC of 0.74 with a cutoff of 1.59, showing a sensitivity of 82% and a specificity of 55%. Conclusions: Although VAI and DAI are not suitable for diagnosing PCOS, they provide valuable insights into the metabolic risks associated with the condition. VAI and DAI can serve as promising biomarkers for identifying IR and MetS risk in women with PCOS. Their integration into clinical practice may facilitate the early detection of cardiometabolic complications, offering a more specific metabolic risk assessment compared to traditional anthropometric measures.