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  11. A Prospective Study Evaluating An Optimized Sentinel Node Algorithm In Early Stage Cervical Cancer: The Prosacc-study.

A prospective study evaluating an optimized sentinel node algorithm in early stage cervical cancer: The PROSACC-study.

Jan Persson1, Oscar Lührs1, Barbara Geppert1

  • 1Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Skåne University Hospital, Lund University, Faculty of Medicine, Department of Clinical Sciences, Obstetrics and Gynecology, 22185 Lund, Sweden.

Gynecologic Oncology
|May 24, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study validates an anatomically-based sentinel lymph node (SLN) algorithm for early-stage cervical cancer, achieving 100% sensitivity and NPV in detecting metastatic disease. The algorithm accurately identifies pelvic nodal involvement, guiding surgical decisions.

Area of Science:

  • Gynecologic Oncology
  • Surgical Pathology
  • Medical Imaging

Background:

  • Sentinel lymph node (SLN) biopsy is crucial for staging cervical cancer.
  • Accurate identification of metastatic lymph nodes is essential for treatment planning.
  • Current SLN algorithms require refinement for optimal accuracy in early-stage disease.

Purpose of the Study:

  • To assess the efficacy of a systematically developed, anatomically-based SLN algorithm in identifying pelvic nodal metastases in early-stage cervical cancer.
  • To evaluate the diagnostic performance (sensitivity, negative predictive value) of this novel SLN approach.

Main Methods:

  • Prospective, non-randomized study involving 181 women with FIGO 2009 stage 1A2-2A1 cervical cancer.
  • Cervical injection of Indocyanine Green (ICG) for SLN detection during robotic radical hysterectomy/trachelectomy.
Keywords:
Cervical cancerProximal obturator fossaSLN-algorithmSentinel lymph node

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  • Adherence to an anatomically-based surgical algorithm, including assessment of parauterine lymphovascular tissue (PULT), followed by completion pelvic lymphadenectomy and ultrastaging.

    • Main Results:

    • High bilateral SLN mapping rate (98.3%).
    • Study stopped early due to interim analysis confirming efficacy, with 100% sensitivity and 100% negative predictive value (NPV) for detecting nodal metastases.
    • Metastases were frequently found in the obturator compartment (46.1%) and occasionally isolated in the PULT (10%).

    Conclusions:

    • The anatomically-based SLN algorithm, incorporating specific lymphatic pathways and PULT assessment, accurately identifies pelvic nodal metastatic disease in early-stage cervical cancer.
    • This refined approach allows for precise nodal staging and potentially limits the extent of lymphadenectomy.