Purpose: To compare early postoperative outcomes after transversus abdominis release (TAR) for ventral hernia repair with open (oTAR) and robotic (rTAR) approach. Methods: A systematic search of PubMed/MEDLINE, EMBASE, SCOPUS and Web of Science databases was conducted to identify comparative studies until October 2020. A meta-analysis of postoperative short-term outcomes was performed including complications rate, operative time, length of stay, surgical site infection (SSI), surgical site occurrence (SSO), SSO requiring intervention (SSOPI), systemic complications, readmission, and reoperation rates as measure outcomes. Results: Six retrospective studies were included in the analysis with a total of 831 patients who underwent rTAR (n = 237) and oTAR (n = 594). Robotic TAR was associated with lower risk of complications rate (9.3 vs 20.7%, OR 0.358, 95% CI 0.218–0.589, p < 0.001), lower risk of developing SSO (5.3 vs 11.5%, OR 0.669, 95% CI 0.307–1.458, p = 0.02), lower risk of developing systemic complications (6.3 vs 26.5%, OR 0.208, 95% CI 0.100–0.433, p < 0.001), shorter hospital stay (SMD − 4.409, 95% CI − 6.000 to − 2.818, p < 0.001) but longer operative time (SMD 53.115, 95% CI 30.236–75.993, p < 0.01) compared with oTAR. There was no statistically significant difference in terms of SSI, SSOPI, readmission, and reoperation rates. Conclusion: Robotic TAR improves recovery by adding the benefits of minimally invasive procedures when compared to open surgery. Although postoperative complications appear to decrease with a robotic approach, further studies are needed to support the real long-term and cost-effective advantages.

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.

Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.

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.

The aim of this systematic review is to report the normal cortical development of different fetal cerebral fissures on ultrasound, describe associated anomalies in fetuses with cortical malformations, and evaluate the quality of published charts of cortical fissures. The inclusion criteria were studies reporting development, anomalies, and reference charts of fetal cortical structures on ultrasound. The outcomes observed were the timing of the appearance of different cortical fissures according to different gestational age windows, associated central nervous system (CNS) and extra-CNS anomalies detected at ultrasound in fetuses with cortical malformation, and rate of fetuses with isolated anomaly. Furthermore, we performed a critical evaluation of the published reference charts for cortical development on ultrasound. Random-effect meta-analyses of proportions were used to combine the data. Twenty-seven studies (6875 fetuses) were included. Sylvian fissure was visualized on ultrasound in 97.69% (95% CI 92.0–100) of cases at 18–19, 98.17% (95% CI 94.8–99.8) at 20–21, 98.94% (95% CI 97.0–99.9) at 22–23, and in all cases from 24 weeks of gestation. Parieto-occipital fissure was visualized in 81.56% (95% CI 48.4–99.3) of cases at 18–19, 96.59% (95% CI 83.2–99.8) at 20–21, 96.85% (95% CI 88.8–100) at 22–23, and in all cases from 24 weeks of gestation, while the corresponding figures for calcarine fissure were 37.27% (95% CI 0.5–89.6), 80.42% (95% CI 50.2–98.2), 89.18% (95% CI 74.0–98.2), and 96.02% (95% CI 96.9–100). Malformations of cortical development were diagnosed as an isolated finding at ultrasound in 6.21% (95% CI 2.9–10.9) of cases, while they were associated with additional CNS anomalies in 93.79% (95% CI 89.1–97.2) of cases. These findings highlight the need for large studies specifically looking at the timing of the appearance of the different brain sulci. Standardized algorithms for prenatal assessment of fetuses at high risk of malformations of cortical development are also warranted.