Predictive factors of sentinel lymph node failed mapping in endometrial carcinoma patients: a systematic review and meta-analysis.

OBJECTIVE: In endometrial carcinoma patients, sentinel lymph node bilateral mapping fails in 20-25% of cases, with several factors affecting the likelihood of detection. However, pooled data about predictive factors of failure are lacking. The aim of this systematic review and meta-analysis was to assess the predictive factors of sentinel lymph node failed mapping in endometrial cancer patients undergoing sentinel lymph node biopsy. METHODS: A systematic review and a meta-analysis was performed searching all studies assessing predictive factors of sentinel lymph node failed mapping in apparent uterine-confined endometrial cancer patients undergoing sentinel lymph node biopsy through the cervical injection of indocyanine green. The associations between sentinel lymph node failed mapping and predictive factors of failure were assessed, calculating the odds ratio (OR) with 95% confidence intervals. RESULTS: Six studies with a total of 1345 patients were included. Compared with patients with sentinel lymph node bilateral successful mapping, patients with sentinel lymph node failed mapping showed: OR 1.39 (p=0.41) for body mass index >30 kg/m2; OR 1.72 (p=0.24) for menopausal status; OR 1.19 (p=0.74) for adenomyosis; OR 0.86 (p=0.55) for prior pelvic surgery; OR 2.38 (p=0.26) for prior cervical surgery; OR 0.96 (p=0.89) for prior Cesarean section; OR 1.39 (p=0.70) for lysis of adhesions during surgery before sentinel lymph node biopsy; OR 1.77 (p=0.02) for indocyanine green dose <3 mL; OR 1.28 (p=0.31) for deep myometrial invasion; OR 1.21 (p=0.42) for International Federation of Gynecology and Obstetrics (FIGO) grade 3; OR 1.89 (p=0.01) for FIGO stages III-IV; OR 1.62 (p=0.07) for non-endometrioid histotype; OR 1.29 (p=0.25) for lymph-vascular space invasion; OR 4.11 (p<0.0001) for enlarged lymph nodes; and OR 1.71 (p=0.022) for lymph node involvement. CONCLUSION: Indocyanine green dose <3 mL, FIGO stage III-IV, enlarged lymph nodes, and lymph node involvement are predictive factors of sentinel lymph node failed mapping in endometrial cancer patients.

Endometrial Cancer Arising in Adenomyosis (EC-AIA): A Systematic Review.

Endometrial cancer arising in adenomyosis (EC-AIA) is a rare uterine disease characterized by the malignant transformation of the ectopic endometrium within the adenomyotic foci. Clinicopathological and survival data are mostly limited to case reports and a few cohort studies. We aimed to assess the clinicopathological features and survival outcomes of women with EC-AIA through a systematic review of the literature. Six electronic databases were searched, from 2002 to 2022, for all peer-reviewed studies that reported EC-AIA cases. Thirty-seven EC-AIA patients from 27 case reports and four case series were included in our study. In our analysis, EC-AIA appeared as a rare disease that mainly occurs in menopausal women, shares symptoms with endometrial cancer, and is challenging to diagnose preoperatively. Differently from EC, it shows a higher prevalence of the non-endometrioid histotype, advanced FIGO stages, and p53-signature, which might be responsible for its worse prognosis. Future studies are necessary, to confirm our findings and further investigate this rare condition.

Laparoscopic entry techniques: Which should you prefer?

BACKGROUND: Despite a debate spanning two decades, no consensus has been achieved about the safest laparoscopic entry technique. OBJECTIVES: To update the evidence about the safety of the main different laparoscopic entry techniques. SEARCH STRATEGY: Six electronic databases were searched from inception to February 2021. SELECTION CRITERIA: All randomized controlled trials (RCTs) comparing different laparoscopic entry techniques were included. DATA COLLECTION AND ANALYSIS: Entry-related complications and total time for entry were compared among the different methods of entry calculating pooled odds ratios (ORs) and mean differences, with 95% confidence intervals (CIs); P < 0.05 was considered significant. MAIN RESULTS: In total, 25 RCTs (6950 patients) were included. Complications considered were vascular, visceral and omental injury, failed entry, extraperitoneal insufflation, bleeding and infection at the trocar site bleeding, and incisional hernia. Compared to direct trocar, the OR for Veress needle was significantly higher for omental injury (OR 3.65, P < 0.001), for failed entry (OR 4.19, P < 0.001), and for extraperitoneal insufflation (OR 5.29, P < 0.001). Compared to the open method, the OR for Veress needle was significantly higher for omental injury (OR 4.93, P = 0.001), for failed entry (OR 2.99, P < 0.001), for extraperitoneal insufflation (OR 4.77; P = 0.04), and for incisional hernia. Compared to the open method, the OR for direct trocar was significantly lower for visceral injury (OR 0.17, P = 0.002) and for trocar site infection (OR 0.27, P = 0.001). CONCLUSIONS: The direct trocar method may be preferred over Veress needle and open methods as a laparoscopic entry technique since it appears associated to a lower risk of complications.

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery.

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models' classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.