En Bloc Resection of Sentinel Lymph Nodes with the Hysterectomy Specimen in Endometrial Cancer.

OBJECTIVE: We demonstrate the surgical technique of removing the sentinel lymph nodes with its afferent lymphatic vessels attached to the hysterectomy specimen. DESIGN: Stepwise demonstration of the technique with narrated video footage. SETTING: Sentinel lymph node sampling has been established as an acceptable staging method in endometrial cancer cases.1 Lymphatic anatomy has been described according to three consistent channels for endometrial cancer dissemination: (1) an upper paracervical pathway draining external or obturator lymph nodes; (2) a lower pathway draining internal iliac lymph nodes; and (3) the infundibulo-pelvic pathway with a course along the broad ligament.2 A study in patients with cervical cancer identified tumor cells in the afferent lymphatic vessels of the upper pathway, even when the corresponding sentinel node was negative (3/20 patients).3 This could be an important prognostic factor in patients with cervical cancer. Since the typical position of sentinel nodes is the same in both endometrial and cervical cancers, we aimed to assess the feasibility of removing 'en bloc' the sentinel node with its afferent lymphatic vessels, and the uterus.4 INTERVENTIONS: The Da Vinci Xi surgical system was used. Indocyanine green was injected cervically, the pelvic surgical spaces were developed, and the sentinel lymph nodes, along with the afferent lymphatic vessels, were identified using the Firefly infrared camera. The lymphovascular tissue was mobilized and separated from the uterine artery, which was skeletonized and ligated. Colpotomy was performed and the specimen was retrieved vaginally. DISCUSSION: Emerging evidence regarding diagnosis, characterization, and treatment of endometrial cancer has introduced a new era, based on minimally invasive techniques for staging through sentinel lymph node biopsy, molecular classification, and personalized treatment algorithms that include immune checkpoint inhibitors and targeted therapies.5 Lymph node staging is one of the most significant prognostic factors in endometrial cancer patients and is a guide for adjuvant treatment. Sentinel lymph node biopsy is not inferior to conventional lymphadenectomy and is in fact a better way of identifying low-volume cancer through the use of ultrastaging, as part of the sentinel node algorithm.6 The dissection technique described in this video could offer an improvement in the staging of endometrial cancer, ensuring that the true sentinel lymph node is identified and that potential cancer cells inside the afferent lymphatic vessels are also excised. Therefore, it could be utilized as a more accurate way of planning adjuvant treatment and consequently improving recurrence and survival; however more studies are needed to further evaluate the feasibility and sensitivity of identifying disease in the afferent lymphatic vessels. CONCLUSION: This novel surgical technique emphasizes the importance of anatomical knowledge and offers inspiration for studies with potential clinical benefit that should follow.

Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis.

Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80-0.82), and specificity was 92% (95% CI, 0.92-0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.