Placenta accreta spectrum (PAS) is an important problem with increasing cesarean section (CS) rates recently. There is still no serum marker for the diagnosis. We determined whether serum YKL-40 levels can be used in the diagnosis and prognosis of PAS. The study was conducted with 50 patients with a PAS diagnosis, 27 individuals without PAS, and 33 normal pregnant women. The operations (CS + placental bed suture, CS + excision of the lower segment, CS-hysterectomy) and for individuals who had the excision of the lower segment /CS-hysterectomy, the histopathological diagnoses (accreta, increta, percreta) were recorded. Serum YKL-40 levels were analyzed. The individuals with PAS possessed significantly greater serum YKL-40 grades (p=0.001). The surgical interventions included 4 CS + excision of the lower segment, 9 CS + placental bed sutures, and 37 CS-hysterectomy. The histopathological outcomes of the individuals who had the excision of the lower segment, CS-hysterectomy and diagnosed 6, 9, and 26 patients with accreta, increta, and percreta, respectively. The accreta, increta, and percreta groups showed statistically significant different serum YKL-40 grades (p=0.001). The receiver operating characteristic analysis was performed to discriminate the cut-off serum YKL-40 level as 32.81 ng/mL with a sensitivity of 66% and specificity of 70.37%. The positive and negative predictive values of YKL-40 in the indicator of PAS were 80.5% and 52.8%, respectively. Elevated serum YKL-40 grades were correlated with the diagnosis and severity of PAS. If our findings are corroborated and elaborated by larger patient series, the YKL-40 levels should be used along with ultrasonography to construct a model identical to that used in aneuploidy screening.

To predict lymphovascular space invasion (LVSI) positivity in early-stage (stage 1-2) endometrial cancer (EC) using a predictive model with prognostic factors of EC. We included 461 patients who underwent total hysterectomy and bilateral salpingo-oophorectomy with pelvic-paraaortic lymphadenectomy as the primary treatment for presumed early-stage EC at our clinic between 2010 and 2020. Moreover, all surgical specimens were examined histopathologically for the positivity or negativity of LVSI, and the patients were divided into two groups based on these pathologic outcomes. Age, menopausal status, histological type (type 1-2), histological grade (grades 1-2-3), depth of myometrial invasion, and peritoneal cytology results were recorded and analyzed as clinicopathological and demographic characteristics of the patients. The Loess algorithm determined the relationship between the observed and predicted outcomes. The distinction between the algorithms was evaluated by calculating the C-index. LVSI positivity was significantly associated with advanced age, menopause, type 2 EC, advanced histological grade, malignant peritoneal cytology, cervical involvement, and a tumor exceeding 50% of the myometrial depth (p<0.001, respectively). Remarkably, LVSI was most strongly associated with three explanatory variables: 1- More than 50% myometrial invasion [odds ratio (OR): 3.78; 95% confidence interval (CI): 1.80-7.60], 2- Advanced histological grade [OR=1.98 (1.20-3.20) 95% CI], 3- Malignant peritoneal cytology [OR= 3.06 (1.40-6.30) 95% CI]. The penalized maximum likelihood estimation model correctly classified 87% of the included patients (C-index: 0.876). Our predictive model may help predict LVSI based on different prognostic factors. The prognostic factors included in the nomogram were significantly associated with LVSI, particularly myometrial invasion depth of more than 50%, advanced histological grade, and malignant peritoneal cytology.

Artificial intelligence (AI) tools like ChatGPT-4o are increasingly utilized in prenatal care. However, their reliability and clinical applicability for healthcare providers in first-trimester screening remain unclear. This study aimed to evaluate the reliability, readability, and clinical utility of ChatGPT-4o's responses to support clinicians in counseling regarding combined first-trimester screening and non-invasive prenatal testing (NIPT). Fifteen risk-stratified clinical scenarios were used to prompt ChatGPT-4o. Fourteen perinatologists rated the responses using mDISCERN and Global Quality Scale (GQS). Readability was assessed via five indices. Inter-rater agreement and internal consistency were evaluated using ICC and Cronbach's alpha. AI responses showed high inter-rater reliability (ICC = 0.998) and internal consistency (α = 0.975). GQS and mDISCERN scores were highest in high-risk scenarios. Readability did not significantly differ across risk levels, nor correlate with quality scores. ChatGPT-4o demonstrates potential as a clinical decision-support and counseling tool for clinicians involved in prenatal screening, particularly in high-risk scenarios. Further refinement is needed for consistent performance across risk levels.