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Web-based predictive tool for vaginal and vulvar melanomas: a machine learning study.

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|September 17, 2025
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Malignant melanoma of the vagina (VaM) and vulva (VuM) are rare and aggressive cancers. Machine learning models predict survival, revealing significantly lower survival rates for vaginal melanoma compared to vulvar melanoma.

Keywords:
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Area of Science:

  • Oncology
  • Genitourinary Cancers
  • Machine Learning in Medicine

Background:

  • Malignant melanoma is a significant health concern, with noncutaneous forms like genitourinary (GU) melanomas being particularly rare and aggressive.
  • Vaginal melanoma (VaM) and vulvar melanoma (VuM) represent rare subtypes of GU melanomas.
  • Limited data exists on prognostic factors and survival outcomes for VaM and VuM.

Purpose of the Study:

  • To develop machine learning (ML) based prognostic models for vaginal (VaM) and vulvar (VuM) melanomas.
  • To create the first web-based predictive tool for survival in VaM and VuM.
  • To identify key prognostic factors influencing survival in these rare cancers.

Main Methods:

  • Leveraged the Surveillance, Epidemiology, and End Results (SEER) database (2000-2020) for cohort assembly.
  • Utilized univariate and multivariate Cox regression for prognostic factor screening.
  • Developed and validated five ML classifiers to predict 5-year survival, assessing discrimination with AUC-ROC and calibration.

Main Results:

  • The study included 1575 patients: 372 with VaM and 1203 with VuM.
  • The 5-year survival rate for VuM (45.4%) was significantly higher than for VaM (15.2%) (P < 0.001).
  • Median patient age was 67 years, and median tumor size was 2.4 cm.

Conclusions:

  • Genitourinary melanomas, including VaM and VuM, exhibit an aggressive clinical behavior.
  • Surgical intervention is crucial for managing these rare cancers.
  • Caution is advised regarding the use of chemotherapy and radiotherapy in VaM and VuM treatment protocols.