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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT06007625
Other study ID # MCOG-VC01
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date January 1, 2024
Est. completion date December 1, 2024

Study information

Verified date August 2023
Source Assiut University
Contact Sherif Shazly
Phone +4407554480388
Email sherif.shazly.mogge@gmail.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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.


Description:

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.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 1000
Est. completion date December 1, 2024
Est. primary completion date October 1, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender Female
Age group 18 Years to 80 Years
Eligibility Inclusion Criteria: - Women diagnosed with Vulvar cancer and treated at collaborating centers between January 1st, 2008, and December 31st, 2017 - women aged 18 years old or older, complete follow-up on for at least 3 years, unless censored by mortality. Exclusion Criteria: - Women will be excluded from the study if there were lost to follow-up before 3 years post-treatment - If the patient did not receive their treatment in the receptive centers - If the patient were diagnosed with synchronous cancers

Study Design


Related Conditions & MeSH terms


Intervention

Other:
Machine learning-based prediction model
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.

Locations

Country Name City State
Egypt Alexandria University Main Hospital Alexandria
Egypt Assiut Hospitals university Assiut

Sponsors (1)

Lead Sponsor Collaborator
Assiut University

Country where clinical trial is conducted

Egypt, 

References & Publications (10)

Brinton LA, Thistle JE, Liao LM, Trabert B. Epidemiology of vulvar neoplasia in the NIH-AARP Study. Gynecol Oncol. 2017 May;145(2):298-304. doi: 10.1016/j.ygyno.2017.02.030. Epub 2017 Feb 22. — View Citation

Chow L, Tsui BQ, Bahrami S, Masamed R, Memarzadeh S, Raman SS, Patel MK. Gynecologic tumor board: a radiologist's guide to vulvar and vaginal malignancies. Abdom Radiol (NY). 2021 Dec;46(12):5669-5686. doi: 10.1007/s00261-021-03209-2. Epub 2021 Aug 25. — View Citation

de Koning MN, Quint WG, Pirog EC. Prevalence of mucosal and cutaneous human papillomaviruses in different histologic subtypes of vulvar carcinoma. Mod Pathol. 2008 Mar;21(3):334-44. doi: 10.1038/modpathol.3801009. Epub 2008 Jan 11. — View Citation

Halec G, Alemany L, Quiros B, Clavero O, Hofler D, Alejo M, Quint W, Pawlita M, Bosch FX, de Sanjose S. Biological relevance of human papillomaviruses in vulvar cancer. Mod Pathol. 2017 Apr;30(4):549-562. doi: 10.1038/modpathol.2016.197. Epub 2017 Jan 6. — View Citation

Madsen BS, Jensen HL, van den Brule AJ, Wohlfahrt J, Frisch M. Risk factors for invasive squamous cell carcinoma of the vulva and vagina--population-based case-control study in Denmark. Int J Cancer. 2008 Jun 15;122(12):2827-34. doi: 10.1002/ijc.23446. — View Citation

Merlo S. Modern treatment of vulvar cancer. Radiol Oncol. 2020 Sep 22;54(4):371-376. doi: 10.2478/raon-2020-0053. — View Citation

Miljanovic-Spika I, Madunic MD, Topolovec Z, Kujadin Kenjeres D, Vidosavljevic D. PROGNOSTIC FACTORS FOR VULVAR CANCER. Acta Clin Croat. 2021 Mar;60(1):25-32. doi: 10.20471/acc.2021.60.01.04. — View Citation

Salvo G, Odetto D, Pareja R, Frumovitz M, Ramirez PT. Revised 2018 International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging: A review of gaps and questions that remain. Int J Gynecol Cancer. 2020 Jun;30(6):873-878. doi: 10.1136/ijgc-2020-001257. Epub 2020 Apr 1. — View Citation

Shetty AS, Menias CO. MR Imaging of Vulvar and Vaginal Cancer. Magn Reson Imaging Clin N Am. 2017 Aug;25(3):481-502. doi: 10.1016/j.mric.2017.03.013. Epub 2017 May 27. — View Citation

Virarkar M, Vulasala SS, Daoud T, Javadi S, Lall C, Bhosale P. Vulvar Cancer: 2021 Revised FIGO Staging System and the Role of Imaging. Cancers (Basel). 2022 Apr 30;14(9):2264. doi: 10.3390/cancers14092264. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary cancer-specific survival (CSS) rate at 3 and 5 years Primary outcome of the study will be cancer-specific survival (CSS) rate at 3 and 5 years after initiation of treatment. at 3 and 5 years
Secondary Recurrence-free survival (RFS) rate at 3 and 5 years Recurrence-free survival (RFS) rate at 3 and 5 years constitutes secondary outcomes at 3 and 5 years
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