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Study identifier: NCT06619002 Synced from ClinicalTrials.gov · July 28, 2026
● Recruiting

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

Condition: Endometrial Cancer · Cervical Cancer · Deep Learning  ·  Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

PhaseN/A
Planned participants100
Who can joinFemale, 18 Years to 99 Years
Healthy volunteersNo

About this study

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. C…

This description comes directly from the study's public registry record.

Talk to the study team

Matteo PAVONE, MD  ·  00390630151  ·  matteo.pavone@guest.policlinicogemelli.it

Always discuss trial participation with your own doctor first.

Locations (2)

Fondazione Policlinico Universitario A. Gemelli IRCCSRoma, ItalyRecruiting
Fondazione Policlinico Universitario A. Gemelli IRCCSRome, ItalyRecruiting

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Source record: clinicaltrials.gov/study/NCT06619002