Automated Cervical Cancer Screening Using a Smartphone-based Artificial Intelligence Classifier
Primary Purpose
Cervical Cancer, HPV
Status
Recruiting
Phase
Not Applicable
Locations
Cameroon
Study Type
Interventional
Intervention
AVC test
Sponsored by
About this trial
This is an interventional prevention trial for Cervical Cancer focused on measuring Cervical cancer screening, HPV-related cervical precancer and cancer, Artificial Intelligence, Image processing, Automated VIA Classifier
Eligibility Criteria
Inclusion Criteria:
- Free and informed consent to take part in the study on a voluntary basis
Exclusion Criteria:
- No initiation of sexual intercourse
- Pregnancy at the screening consultation
- Any condition altering the cervix visualization at the screening consultation (e.g. heavy vaginal bleeding)
- History of anogenital cancer or known anogenital cancer at the screening consultation
- Previous hysterectomy
- Not sufficiently healthy to participate in the study
Sites / Locations
- Dschang District HospitalRecruiting
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
AVC test
Arm Description
Outcomes
Primary Outcome Measures
Estimate accuracy of the AVC test
by including metrics such as sensitivity, specificity, positive predictive value and negative predictive value using histologic assessment as reference standard.
Secondary Outcome Measures
Compare accuracy of the AVC test and VIA to detect cervical precancer and cancer
using histopathology as gold standard.
Compare accuracy of the AVC test and cytology to detect cervical precancer and cancer
using histopathology as gold standard.
Estimate feasibility of the AVC test
by women and healthcare providers using qualitative and quantitative methods.
Estimate acceptability of the AVC test
by women and healthcare providers using qualitative and quantitative methods.
Full Information
1. Study Identification
Unique Protocol Identification Number
NCT04859530
Brief Title
Automated Cervical Cancer Screening Using a Smartphone-based Artificial Intelligence Classifier
Official Title
Study Protocol for a Two-site Clinical Trial to Validate a Smartphone-based Artificial Intelligence Classifier Identifying Cervical Precancer and Cancer in HPV-positive Women in Cameroon
Study Type
Interventional
2. Study Status
Record Verification Date
May 2023
Overall Recruitment Status
Recruiting
Study Start Date
August 1, 2018 (Actual)
Primary Completion Date
September 30, 2024 (Anticipated)
Study Completion Date
September 30, 2025 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor-Investigator
Name of the Sponsor
Prof. Patrick Petignat
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes
5. Study Description
Brief Summary
Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. The World Health Organization (WHO) recommendation for cervical cancer screening in LMICs includes Human Papillomavirus (HPV) testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider's experience. Therefore, an objective approach based on quantitative diagnostic algorithms is desirable to improve performance of VIA.
With this objective and in a collaboration between the Gynecology and Obstetrics Department of the Geneva University Hospital (HUG) and the Swiss Institute of Technology (EPFL), our group started the development of an automated smartphone-based image classification device called AVC (for Automatic VIA Classifier). Two-minute videos of the cervix are recorded during VIA and classified using an artificial neural network (ANN) and image processing techniques to differentiate precancer and cancer from non-neoplastic cervical tissue. The result is displayed on the smartphone screen with a delimitation map of the lesions when appropriate. The key feature used for classification is the dynamic of cervical acetowhitening during the 120 second following the application of acetic acid. Precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime.
Our aim is to assess the diagnostic performance of the AVC and to compare it with the performance of current triage tests (VIA and cytology). Histopathological examination will serve as reference standard. Participants' and providers' acceptability will also be considered as part of the study.
The study will be nested in an ongoing cervical cancer screening program called "3T-approach" (for Test, Triage and Treat) which includes HPV self-sampling for women aged 30 to 49 years, followed by VIA triage and treatment if needed. The AVC will be evaluated in this context.
The study's risk category is A according to swiss ethical guidelines. This decision is based on the fact that the planned measures for sampling biological material or collecting personal data entail only minimal risks and burdens.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Cervical Cancer, HPV
Keywords
Cervical cancer screening, HPV-related cervical precancer and cancer, Artificial Intelligence, Image processing, Automated VIA Classifier
7. Study Design
Primary Purpose
Prevention
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
5886 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
AVC test
Arm Type
Experimental
Intervention Type
Diagnostic Test
Intervention Name(s)
AVC test
Other Intervention Name(s)
HPV self-test, VIA/VILI (visual inspection with Lugol's iodine), Pap-smear, Cervical biopsy, Endocervical brushing
Intervention Description
The AVC test will be performed during VIA by local midwives: 120 second videos focused on the cervix will be taken right after the application of acetic acid on the cervix. The recording smartphone will be fixed on a tripod situated 15cm away from the cervix.
Primary Outcome Measure Information:
Title
Estimate accuracy of the AVC test
Description
by including metrics such as sensitivity, specificity, positive predictive value and negative predictive value using histologic assessment as reference standard.
Time Frame
2 years
Secondary Outcome Measure Information:
Title
Compare accuracy of the AVC test and VIA to detect cervical precancer and cancer
Description
using histopathology as gold standard.
Time Frame
2 years
Title
Compare accuracy of the AVC test and cytology to detect cervical precancer and cancer
Description
using histopathology as gold standard.
Time Frame
2 years
Title
Estimate feasibility of the AVC test
Description
by women and healthcare providers using qualitative and quantitative methods.
Time Frame
2 years
Title
Estimate acceptability of the AVC test
Description
by women and healthcare providers using qualitative and quantitative methods.
Time Frame
2 years
10. Eligibility
Sex
Female
Minimum Age & Unit of Time
30 Years
Maximum Age & Unit of Time
49 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria:
Free and informed consent to take part in the study on a voluntary basis
Exclusion Criteria:
No initiation of sexual intercourse
Pregnancy at the screening consultation
Any condition altering the cervix visualization at the screening consultation (e.g. heavy vaginal bleeding)
History of anogenital cancer or known anogenital cancer at the screening consultation
Previous hysterectomy
Not sufficiently healthy to participate in the study
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Patrick Petignat, Pr
Phone
+41796630546
Email
patrick.petignat@hcuge.ch
First Name & Middle Initial & Last Name or Official Title & Degree
Inès Baleydier
Phone
+41 77 460 61 20
Email
ines.baleydier@gmail.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Patrick Petignat
Organizational Affiliation
University Hospital, Geneva
Official's Role
Principal Investigator
Facility Information:
Facility Name
Dschang District Hospital
City
Dschang
State/Province
Menoua
Country
Cameroon
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Bruno Kenfack, Pr
Email
brunokenfack@gmail.com
12. IPD Sharing Statement
Citations:
PubMed Identifier
30207593
Citation
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12. Erratum In: CA Cancer J Clin. 2020 Jul;70(4):313.
Results Reference
background
PubMed Identifier
27340003
Citation
Bruni L, Diaz M, Barrionuevo-Rosas L, Herrero R, Bray F, Bosch FX, de Sanjose S, Castellsague X. Global estimates of human papillomavirus vaccination coverage by region and income level: a pooled analysis. Lancet Glob Health. 2016 Jul;4(7):e453-63. doi: 10.1016/S2214-109X(16)30099-7. Erratum In: Lancet Glob Health. 2017 Jul;5(7):e662.
Results Reference
background
PubMed Identifier
34914727
Citation
Baleydier I, Vassilakos P, Vinals R, Wisniak A, Kenfack B, Tsuala Fouogue J, Enownchong Enow Orock G, Lemoupa Makajio S, Foguem Tincho E, Undurraga M, Cattin M, Makohliso S, Schonenberger K, Gervaix A, Thiran JP, Petignat P. Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon. PLoS One. 2021 Dec 16;16(12):e0260776. doi: 10.1371/journal.pone.0260776. eCollection 2021.
Results Reference
derived
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Automated Cervical Cancer Screening Using a Smartphone-based Artificial Intelligence Classifier
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