This study aims to develop a machine learning-based prediction model for patients with
vulvar cancer. This model will utilize patient characteristics and disease features to
determine the disease's prognosis. The scoring system will also include management
information to facilitate prediction of clinical outcomes of different management
strategies and potential management that would yield the best prognosis.

Vulvar Cancer Individualized Scoring System (VCISS)

Vulvar Cancer

Vulvar cancer (VC) is a relatively rare gynecological cancer accounting for 5-8% of all
cases [1].

It comes the fourth among the commonest gynecological cancers and tends to affect women
after menopause with a median age of 68 years [2,3].

Risk factors include cervical intraepithelial neoplasia, prior history of cervical
cancer, smoking, lichen sclerosus, and immunodeficiency syndromes [4-5]. As squamous cell
carcinoma is considered the most common type of VC, there are two potential pathogenic
pathways for squamous cell carcinoma of the vulva include chronic inflammatory processes
and human papillomavirus (HPV) infection [6-7].

While VC may be asymptomatic, most cases are present with bleeding, discharge, vulvar
mass, ulcer and/or pruritis. Furthermore, it can be presented by a groin mass which
reflects inguinal lymph node involvement. VC may be confined to the primary site in 59%
of cases while 30% and 6% of cases spread to regional lymph nodes and distant areas,
respectively [8].

FIGO staging is considered the standard classification system that determines prognosis
and management of newly diagnosed VC. However, there are numerous gaps in the current
staging system that would limit full interpretation of prognosis and management guidance
[9]. Although staging system primarily determines disease prognosis, the staging system
does not consider all prognostic factors, such as disease stage and histopathology. In
fact, factors other than lymph node metastasis may have a stronger predictive influence
such as the severity of the disease, age, histologic type and adjuvant radiotherapy and
chemotherapy [10].

Development of a prognostic and decision-making system, based on comprehensive inclusion
of individual patient and disease characteristics, would facilitate accurate prediction
of disease prognosis and determination of individualized management strategy

A retrospective multicenter cohort study will be conducted among at least 6 European
gynecologic oncology centers.

Inclusion Criteria:

  1. Women diagnosed with Vulvar cancer and treated at collaborating centers between
     January 1st, 2008, and December 31st, 2017.

  2. Women aged 18 years old or older, complete follow-up on for at least 3 years, unless
     censored by mortality.

Exclusion criteria:

  1. Women will be excluded from the study if there were lost to follow-up before 3 years
     post-treatment.

  2. If the patient did not not receive their treatment in the receptive centers, and if
     they were diagnosed with synchronous cancers.

Machine learning-based prediction model

Vulvar Cancer Individualized Scoring System

Primary outcome of the study will be cancer-specific survival (CSS) rate at 3 and 5 years
after initiation of treatment.

Sherif Abdelkarim Mohammed Shazly, Assistant lecturer, Assiut University

Recurrence-free survival (RFS) rate at 3 and 5 years constitutes secondary outcomes

Vulvar Cancer Individualized Scoring System (VCISS)

About this trial

Study ID

Status

Type

Placebo

Accepting

Trial Timing

What is this trial about?

What are the participation requirements?

Locations

Location

Status