This systematic review (SR) and meta-analysis aims to compare the surgery-related results and oncological outcomes between SH and RH in patients with early-stage cervical cancer. We systematically searched databases including PubMed, Embase and Cochrane to collect studies that compared oncological and surgery-related outcomes between SH and RH groups in patients with stage IA2 and IB1 cervical cancer. A random-effect model calculated the weighted average difference of each primary outcome via Review Manager V.5.4. Seven studies comprising 6977 patients were included into our study. For oncological outcomes, we found no statistical difference in recurrence rate [OR = 0.88; 95% CI (0.50, 1.57); P = 0.68] and Overall Survival (OS) [OR = 1.23; 95% CI (0.69, 2.19), P = 0.48]. No difference was detected in the prevalence of positive LVSI and lymph nodes metastasis between the two groups. Concerning surgery-related outcomes, the comprehensive effects revealed that the bladder injury [OR = 0.28; 95% CI (0.08, 0.94), P = 0.04] and bladder disfunction [OR = 0.10; 95% CI (0.02, 0.53), P = 0.007] of the RH group were higher compared to the SH group. This meta-analysis suggested there are no significant differences in terms of both recurrence rate and overall survival among patients with stage IA2-IB1 cervical cancer undergoing SH or RH, while the SH group has better surgery-related outcomes. These data confirm the need to narrow the indication for RH in early-stage cervical cancer.

Introduction: This systematic review examines the application of multiple deep learning algorithms in the analysis of intraoperative videos to enable feature extraction and pattern recognition of surgical phases, anatomical structures, and surgical instruments. Material and Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and EBSCO, covering studies published until March 2024. This review includes studies that applied AI models in the operating room for surgical‐phase recognition and/or anatomical structures and instruments. Only studies utilizing machine learning or deep learning for surgical video analysis were considered. The primary outcome measures were accuracy, precision, recall, and F1 score. Results: A total of 21 studies were included. Multilayer architecture of interconnected neural networks was predominantly used. The deep learning models demonstrated promising results, with accuracy ranging from 81% to 93.2% for surgical‐phase recognition. Anatomical structure recognition models achieved accuracy between 71.4% and 98.1%. Conclusions: Artificial intelligence has the potential to significantly improve surgical precision and workflow, with demonstrated success in phase recognition and anatomical structure identification. However, further research is needed to address dataset limitations, standardize annotation protocols, and minimize biases.

The study aimed to validate the Betella algorithm, focusing on molecular analyses exclusively for endometrial cancer patients, where molecular classification alters risk assessment based on ESGO/ESTRO/ESP 2020 guidelines. Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution. Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %). In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.

Iatrogenic urinary tract injury is a known complication of pelvic surgery, most commonly occurring during gynecological procedures. The bladder and ureters are particularly vulnerable due to their close anatomical proximity to the uterus. Urinary tract damage can result from various mechanisms, including laceration, ligation, and thermal injury. Incidence rates vary according to the affected organ and surgical type; bladder injuries occur in 0.24% of benign and 0.4–3.7% of oncologic surgeries, whereas ureteral injuries are reported in 0.08% of benign and 0.39–1.1% of oncologic procedures. Timely diagnosis is essential for effective management. When detected intraoperatively, the injury can often be repaired immediately. Surgical treatment options vary depending on the specific nature and location of the bladder or ureteral damage. Delayed diagnosis can significantly impact the patient’s quality of life, increasing the risk of severe complications such as genitourinary fistulas. This narrative review aims to summarize current evidence on the diagnosis, prevention, and treatment of urinary tract injuries occurring during gynecological surgery. It evaluates risk factors, incidence, management, complications, and prevention strategies for iatrogenic bladder and ureteral injuries. Additionally, it highlights the innovative role of artificial intelligence in preventing urologic damage during gynecological procedures. The relevant literature was identified through a structured search of the PubMed database using predefined keywords related to gynecological surgery and urinary tract injury.

Epithelial ovarian cancer (EOC) remains the most lethal gynecological malignancy worldwide, primarily due to late-stage diagnosis, limited early screening tools, and the aggressive nature of the disease. Increasing global efforts have been directed toward harmonizing clinical guidelines and ensuring equitable access to optimal care. This narrative review provides a comprehensive overview and critical comparison of the most recent international guidelines for ovarian cancer management, emphasizing similarities and discrepancies. Each section examines diagnostic, surgical, and therapeutic recommendations across all disease stages—from prevention to recurrence. Emerging advances that are reshaping the management of EOC such as preclinical models and the application of artificial intelligence are also discussed. This work provides an updated and practical synthesis to support clinicians, researchers, and policymakers in optimizing ovarian cancer management and guiding future research priorities in precision oncology.