Implementation of Surgical Safety and Intraoperative Metastasis Identification Through Deep Learning: Multicentric Video Collection for Minimally Invasive Sentinel Lymph Node Dissection in Uterine Malignancies

Brief summary (registry, in English)

The loco-regional metastatic or non-metastatic status of lymph nodes (LN) is critical for the therapeutic management of most cancer patients. Indeed, the presence or absence of lymphatic metastasis is essential for the accurate staging of the disease and strongly influence the prognosis and adjuvant treatment regimens. An important revolution in oncological surgery has been the introduction of the concept of sentinel lymph node (SLN) biopsy to reduce the complications of extensive loco-regional lymphadenectomies. SLN identification through ICG- based near-infrared fluorescence (NIR) cervical injection and its dissection is now recommended by European guidelines to stage uterine malignancies (endometrial and cervical cancers). However, SLN procedures have several limitations. In 11.2% of cases intra- or postoperative complications are reported due to anatomical structures injuries (vessels, nerves and lymphatic channels disruptions). Common mistakes, especially when the learning curve is not completed (at least 40 procedures), include mapping failure (25%) and removal of second/third-level nodes and/or empty nodes packets (8-14%). Additionally the intraoperative accuracy of frozen section is still far to be adequate with only the 65% of SLN metastasis detection. These limitations are a result of the lack of precision of current SLN localization and analysis as well as of the overall difficulty of visualizing lymph nodes and other critical structures in the retroperitoneum. Currently, studies on the safety of surgical procedures are based on perioperative clinical information and postoperative reports written by the surgeons themselves. Today, videos guiding minimally invasive surgical interventions allow for objective documentation of the procedure and provide opportunities to explore solutions for enhancing safety in the operating room. With an increasing use of endoscopic systems across different specialties, there is a need for standardization of training, assessment, testing and sign-off as a competent surgeon in order to improve patient safety. In laparoscopic lymph node dissection in endometrial and cervical cancer, a standardize stepwise approach to the procedure is highly recommended, by identifying key anatomic landmarks and structures, in various scenarios, that could prevent vascular, nervous and ureters injuries and enhance the mapping rate. Therefore, quantifying and studying intraoperative events such as the rate of achieving the right space dissections and anatomic structures visualization as a recommended step for safety and proficiency, would enable the examination of how best to implement guideline recommendations and seek new solutions to reduce operative risks. These videos could be utilized to train and validate artificial intelligence (AI) algorithms, with the potential to assist surgeons in the operating room and make the procedures safer. Additionally, the visual information (ICG intensity) could hide data that the AI can analyze and correlate with anatomopathological reports. By the integration of AI tool with laparoscopic/robotic platform it is possible to enhance MIS video streams in real time with surgical phases detection, events recognition, ICG signal intensity, anatomical structure identification and auto-targeting

What you commit to

• Study type: Observational
• Planned participants: 100
• From 17 Sept 2024 to the primary completion date, 30 Sept 2026
• 2 sites in 1 country
• Visits and tests: ask the trial team, the registry does not list them

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Who cannot take part

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Who the study is looking for

Age in the registry: 18 years to 99 years · Sex: Female · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (2)

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