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Predicting lymph node metastases in three different vulvar squamous cell carcinoma subgroups.

Anne F van Velzen1, Adam J Tulling2, Mariëtte I E van Poelgeest1

  • 1Department of Gynecology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, the Netherlands.

Gynecologic Oncology
|February 21, 2025
PubMed
Summary

Vulvar squamous cell carcinoma (VSCC) patients with HPV-negative and abnormal p53 (HPVneg/p53abn) have a higher risk of lymph node metastases (LNM). A new model predicts LNM risk in VSCC subgroups.

Keywords:
Human papillomavirusMolecular classificationPrediction modelTP53Vulvar neoplasmsVulvar squamous cell carcinomap16p53

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Area of Science:

  • Oncology
  • Molecular Biology
  • Cancer Research

Background:

  • Vulvar squamous cell carcinoma (VSCC) risk stratification is crucial for treatment planning.
  • Human papillomavirus (HPV) status and p53 gene mutations are key molecular markers in VSCC.
  • Understanding lymph node metastasis (LNM) risk across different VSCC molecular subgroups is essential.

Purpose of the Study:

  • To analyze LNM risk in HPV-positive (HPVpos), HPV-negative p53 wildtype (HPVneg/p53wt), and HPV-negative p53 abnormal (HPVneg/p53abn) VSCC subgroups.
  • To develop a predictive model for clinical LNM risk assessment in VSCC patients.

Main Methods:

  • Retrospective cohort study of surgically treated VSCC patients (2000-2022).
  • Identification of prognostic variables for groin LNM using univariate and multivariate analyses.
  • Development of a predictive model for LNM probability at diagnosis.

Main Results:

  • LNM rates were 17.0% (HPVpos), 26.5% (HPVneg/p53wt), and 35.1% (HPVneg/p53abn).
  • Molecular subgroup remained a significant predictor of LNM after adjusting for clinical factors (p=0.028).
  • A predictive model for LNM at diagnosis was successfully developed.

Conclusions:

  • HPVneg/p53abn VSCC exhibits a significantly higher LNM risk.
  • HPVneg/p53wt VSCC represents an intermediate-risk group.
  • The developed model accurately predicts LNM risk, aiding clinical decision-making.