Cervical cancer continues to have a significant incidence, despite global efforts in HPV vaccination campaigns. Managing this condition involves a diverse team of healthcare professionals. Research in this field is undergoing a period of great revolution in multiple areas, and international guidelines will soon have to adapt to new scientific evidence. This could be true mainly in locally advanced stages, and it could also be true for minimal invasive surgery. This paper aims to summarize and compare the most recent recommendations published by international gynecological oncological societies for patients with cervical cancer. From their comparison, common aspects and disagreements emerged, especially in the diagnostic pathway and follow-up strategies. Several issues that remain to be debated in the literature were addressed and compared, highlighting similarities and differences, from the role of the sentinel lymph node in early stages to that of the adjuvant hysterectomy in locally advanced tumors. On the surgical side, for this last subset of patients, currently, a laparotomic approach is recommended. At the same time, the advent of immunotherapy has just opened up new and promising scenarios in systemic treatment for locally advanced cervical cancer, and international guidelines will soon introduce it into their algorithms.

Background: Vulvar carcinoma is an uncommon gynecological tumor primarily affecting older women. Its treatment significantly impacts the quality of life and, not least, aesthetics because of the mutilating surgery it requires. Objectives: The management requires a multidisciplinary team of specialists who know how to care for the patient in her entirety, not neglecting psychological aspects and reconstructive surgery. How do the guidelines address multidisciplinarity, team surgical management, passing through preoperative diagnosis, and follow-up in such a challenging rare tumor to treat? Methods: To answer these questions, we compared the main scientific recommendations to identify similarities and differences in diagnostic and therapeutic management to provide an overview of the gaps that there are currently in European and American international recommendations in providing management guidance in a cancer that is both among the rarest and most difficult to manage. In this way, we aim to encourage an update in practices based on the latest scientific evidence. Results: A review of various international guidelines, some dating back to 2014, shows significant variation in approaches, ranging from initial diagnostic procedures to managing relapses. The most recent guidelines also lacked references to the latest literature, indicating that more robust scientific evidence is needed before new treatments, such as electrochemotherapy for palliation and reconstructive surgery post exenteration, can be widely adopted. Conclusions: From the systematic comparison of the main international guidelines, a strong heterogeneity emerged in the diagnostic and therapeutic recommendations as well as for the multidisciplinary approach that today is essential. Our work certainly stimulated an update of the main guidelines.

Background: The field of medicine, both clinical and surgical, has recently been overwhelmed by artificial intelligence technology, which promises countless application scenarios and, above all, implementation in clinical practice and research. Novelties are riding the wave fast, but where do we stand? A small overview in gynecological oncology of future challenges, evidence already investigated, and possible scenarios to be derived was conducted. Methods: Both diagnostic and surgical work in the field of gynecological oncology was conducted, selecting the most interesting articles on the subject. Results: From the narrative review of the literature, it emerged how much further ahead the diagnostic field is at present compared to the surgical one, which appeared to be more limited to ovarian surgery. Most current evidence focuses on the role of different biomarkers in predicting diagnostic, prognostic, and treatment-integrated patterns. Conclusions: Everything we know to date is related to a dynamic photograph that is constantly and rapidly changing as much as AI is becoming inextricably linked to our medical field.

With a 5-year survival rate of fewer than 50%, epithelial ovarian carcinoma is the most fatal of the gynecologic cancers. Each year, an estimated 22,000 women are diagnosed with the condition, with 14,000 dying as a result, in the United States. Over the last decade, the advent of molecular and genetic data has enhanced our understanding of the heterogeneity of ovarian cancer. More than 80% of women diagnosed with advanced illness have an initial full response to rigorous therapy at diagnosis, including surgery and platinum-based chemotherapy. Unfortunately, these responses are infrequently lasting, and the majority of women with ovarian cancer suffer recurrent disease, which is often incurable, despite the possibility of future response and months of survival. And what therapeutic weapons do we have to counter it? For many years, radiation therapy for ovarian tumors was disregarded as an effective treatment option due to its toxicity and lack of survival benefits. Chemotherapy is widely used following surgery, and it has nearly completely supplanted radiation therapy. Even with the use of more modern and efficient chemotherapy regimens, ovarian cancer failures still happen. After receiving first-line ovarian cancer chemotherapy, over 70% of patients show evidence of recurrence in the abdomen or pelvis. It is necessary to reinterpret the function of radiation therapy in light of recent technological developments, the sophistication of radiation procedures, and the molecular and biological understanding of various histological subtypes. This review article focuses on the literature on the use of radiation in ovarian tumors as well as its rationale and current indications.

Objective Molecular features are essential for estimating the risk of recurrence and impacting overall survival in patients with endometrial cancer. Additionally, the surgical procedure itself could be personalized based on the molecular characteristics of the tumor. This study aims to assess the feasibility of obtaining reliable molecular classification status from biopsy specimens collected during hysteroscopy to better modulate the appropriate surgical treatment. Methods This monocentric, retrospective, observational study was conducted on 106 patients who underwent a biopsy procedure followed by radical surgery for endometrial cancer, with concurrent molecular investigation. The molecular classification was determined through immunohistochemical staining for p53 and mismatch repair proteins, along with gene sequencing for POLE. Results Overall, 106 patients underwent molecular investigation, which was finally achieved on 99 patients (93.4%). Among these, the molecular analysis was conducted in 71 patients (67%) on the pre-operative endometrial biopsy and on the final uterine specimen in 28 patients (26.4%). Most of the endometrial biopsies were performed using Bettocchi hysteroscopy (66%). Molecular analysis was not possible in seven patients (6.6%), with six cases due to sample inadequacy and one case attributed to intra-mucosal carcinoma. The molecular results showed that the copy number low sub-group was the most common, and five cases of ‘multiple classifiers’ were observed in the low-risk category. Conclusion Our experience in obtaining molecular information from biopsy samples underscores the feasibility and efficacy of this technique, even in small tissue samples. This capability helps define the prognostic group of patients, facilitates timely decision-making, and develops a personalized strategy for each patient.

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.