(1) The surgical method of choice for the treatment of endometrial cancer is minimally invasive surgery. In cases of high-risk endometrial cancer, completed paraaortic and pelvic lymphadenectomy are indicated. The aim of this study was to analyze the types of docking during robotic surgery assisted with the da Vinci X system while performing paraaortic and pelvic lymphadenectomy. (2) Methods: A total of 25 patients with high-risk endometrial cancer, with a mean age of 60.07 ± 10.67 (range 34.69-83.23) years, and with a mean body mass index (BMI) of 28.4 ± 5.62 (range 18-41.5) kg/m2, were included in this study. The analyzed population was divided into groups that underwent single or dual docking during surgery. (3) Results: No statistical significance was observed between single and dual docking during paraaortic and pelvic lymphadenectomy and between the type of docking and the duration of the operation. However, there was a statistically significant correlation between the duration of the operation and previous surgery (p < 0.005). The number of removed lymph nodes was statistically associated with BMI (p < 0.005): 15.87 ± 6.83 and 24.5 ± 8.7 for paraaortic and pelvic lymph nodes, respectively, in cases of single docking, and 18.05 ± 7.92 and 24.88 ± 11.75 for paraaortic and pelvic lymph nodes, respectively, in cases of dual docking. (4) Conclusions: The robot-assisted approach is a good surgical method for lymphadenectomy for obese patients, and, despite the type of docking, there are no differences in the quality of surgery.

• Klíčová slova
• dual docking, endometrial cancer, paraaortic lymphadenectomy, robotic surgery, single docking,
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Among industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages. METHODS: The ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures. RESULTS: The area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages. CONCLUSIONS: Our data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

• Klíčová slova
• Bayesian networks model, disease-specific survival, endometrial cancer, lymph node metastasis, prognosis, risk stratification, sentinel node biopsy,
• Publikační typ
• časopisecké články MeSH

Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low- and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment. Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method). Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER (p = 0.024), PR (0.048), and higher levels of L1CAM (p = 0.001) were observed; in cervical involvement a higher expression of L1CAM (p = 0.001), lower PR (p = 0.014); in tumors with positive LVSI, higher L1CAM (p = 0.014); in cases with positive LN, lower expression of ER/PR (p < 0.001), higher L1CAM (p = 0.002) and frequent mutation of p53 (p = 0.008). Cut-offs for determination of high-risk tumors were established: ER <78% (p = 0.001), PR <88% (p = 0.008), and L1CAM ≥4% (p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8-96.5%), 63% (52.1-72.8%), 83% (70.5-90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5-63.4%), 65% (55.6-74.0%), and 77% (67.3-84.2%). Mutation of p53 revealed PPV 94% (67.4-99.1%) and NPV 61% (56.1-66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001). Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.

• Klíčová slova
• ER, L1CAM, PR, endometrial cancer, imaging method, p53, preoperative biopsy, risk stratification,
• Publikační typ
• časopisecké články MeSH