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

NCT06619002 | Recruiting

• 1 other identifier: ID 6672
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 2

Brief Summary

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

Conditions

Endometrial Cancer; Cervical Cancer; Deep Learning; Artificial Intelligence

Keywords

endometrial cancer; cervical cancer; deep learning; computer vision; sentinel lymph node; artificial intelligence; indocyanin green

Outcome Measures

Primary Outcomes (1)

• Evaluate the rate of achieving critical view of safety (CVS) according to standard agreement recommendations in Laparoscopic SLN dissection procedures performed at centers involved in the study.

  The rate of Critical views of safety achievement will be evaluated in a video-based assessment by experts gyn surgeons and labeled according to the annotation protocol. The inter-rather agreement will be therefore also evaluated

  24 months

Secondary Outcomes (4)

• Develop an Artificial intelligence tool

  24 months

• Develop an Artificial intelligence tool

  24 months

• Develop an Artificial intelligence tool

  24 months

• Develop an Artificial intelligence tool

  24 months

Interventions

Sentinel lymph node sampling PROCEDURE

video analysis

Also known as: sentinel lymph node detection, sentinel lymph node dissection, sentinel lymph node biopsy

Eligibility Criteria

• Age: 18 Years - 99 Years
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Women undergoing laparoscopic or robotic sentinel lymph node dissection for endometrial or cervical cancer

You may qualify if:

• Women undergoing MIS sentinel lymph node dissection for endometrial or cervical cancers
• Availability of video
• Age \>18 years
• Willingness to participate in the study and to provide informed consent

You may not qualify if:

• Previous pelvic radiotherapy treatments
• Severe endometriosis or other conditions able to alter the pelvic anatomy

Sponsors & Collaborators

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS: lead
• IHU Strasbourg: collaborator
• University Hospital, Strasbourg: collaborator

Study Sites (2)

Fondazione Policlinico Universitario A. Gemelli IRCCS

Roma, Italy

RECRUITING

Fondazione Policlinico Universitario A. Gemelli IRCCS

Rome, Italy

RECRUITING

MeSH Terms

Conditions

Endometrial Neoplasms; Uterine Cervical Neoplasms

Interventions

Sentinel Lymph Node Biopsy

Condition Hierarchy (Ancestors)

Uterine Neoplasms; Genital Neoplasms, Female; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Uterine Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases; Uterine Cervical Diseases

Intervention Hierarchy (Ancestors)

Biopsy; Cytodiagnosis; Cytological Techniques; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Specimen Handling; Diagnostic Techniques, Surgical; Surgical Procedures, Operative; Lymph Node Excision; Investigative Techniques

Study Officials

• Matteo PAVONE, MD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy; IHU Strasbourg; IRCAD Strasbourg; Icube Strasbourg;

  PRINCIPAL INVESTIGATOR

• Nicolò BIZZARRI, MD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  PRINCIPAL INVESTIGATOR

• Lise LECOINTRE, MD, PhD

  University Hospitals of Starsbourg; Icube Strasbourg; IHU Strasbourg

  PRINCIPAL INVESTIGATOR

• Denis QUERLEU, MD, PhD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  STUDY CHAIR

• Nicolas PADOY, PhD

  IHU Strasbourg

  STUDY CHAIR

• Giovanni SCAMBIA, MD, PhD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  STUDY DIRECTOR

• Francesco FANFANI, MD, PhD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  STUDY CHAIR

• Cherif AKLADIOS, MD, PhD

  University Hospitals of Strasbourg

  STUDY CHAIR

• Pietro MASCAGNI, MD, PhD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  STUDY CHAIR

• Valentina IACOBELLI, MD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  STUDY CHAIR

• Andrea ROSATI, MD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome Italy

  STUDY CHAIR

Central Study Contacts

Matteo PAVONE, MD

CONTACT

00390630151; matteo.pavone@guest.policlinicogemelli.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: September 24, 2024
• First Posted: October 1, 2024
• Study Start: September 17, 2024
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): September 30, 2027
• Last Updated: October 2, 2024

Record last verified: 2024-10

Locations

IT (2)