Artificial inTelligence in eNdometriosis-related ovArian Cancer and Precision Surgery in eNdometriosis-related ovArian Cancer (ATENA)

December 4, 2021 updated by: Anna Myriam Perrone, IRCCS Azienda Ospedaliero-Universitaria di Bologna

Artificial inTelligence as Tool for Early Diagnosis and Precision Surgery in eNdometriosis-related ovArian Cancer

Endometriosis (EMS) is a chronic, invaliding, inflammatory gynaecological condition affecting 10-15% of women in reproductive age. EMS is characterized by lesions of endometrial-like tissue outside the uterus involving pelvic peritoneum and ovaries. In addition, distant foci are sometimes observed. Unfortunately, the aetiology of the EMS is little known. Although non-malignant, EMS shares similar features with cancer, such as development of local and distant foci, resistance to apoptosis and invasion of other tissues with subsequent damage to the target organs. Moreover, patients with EMS (particularly ovarian EMS) showed high risk (about 3 to 10 times) of developing epithelial ovarian cancer (EOC). Epidemiologic, morphological and molecular studies reported endometrioma as the precursor of EOC, including clear cell (CCC) endometrioid carcinoma which are both called "EMS-related ovarian carcinoma (EROC)". To date, it remains unclear why benign EMS causes malignant transformation. This multi-step process, unlike high-grade serous carcinomas, offers the possibility to identify the carcinoma precursors enabling an early diagnosis and in the early stages of the disease.

EOC is the most lethal female gynecological cancer with 25% 5-year overall survival (OS), due to the lack of effective screening tools, and rapidly spreads over the entire peritoneal surface (carcinosis) thus involving all abdominal organs. Diagnosis and clinical staging of EOC is currently performed by qualitative image evaluation although the sensitivity/specificity is suboptimal. To date, diagnostic, staging, and prognostic factors are strongly correlated with subjective assessment training and clinician experience.

Genomic analysis based on Next Generation Sequencing (NGS) has revealed the presence of cancer-associated gene mutations in EMS. Moreover, the chronic inflammatory process of EMS involves many factors, such as hormones, cytokines, glycoproteins, and angiogenic factors, which are expected to become early EMS biomarkers.

A promising new branch of cancer research is the use of artificial intelligence (AI) to recognize new image patterns and texture and/or detecting novel biomarkers to improve the early identification of EROC patients. AI has never been used for EROC and we want to investigate whether these methods/techniques can support and even improve current diagnostics and risk assessment. AI will be used to construct a new 3D risk assessment model based on images and volume of interest

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Anticipated)

240

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Italy
    • Bo
      • Bologna, Bo, Italy, 40138
        • Recruiting
        • IRCCS- Azienda Ospedaliera-Universitaria di Bologna
        • Principal Investigator:
          • Lidia Strigari

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 90 years (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

Female

Sampling Method

Non-Probability Sample

Study Population

Patients aged between 18 and 90 years, with clinical and radiological suspicion of ovarian cancer, eligible for surgery followed in the clinical care path at the U.O.C. Oncological Gynecology-IRCCS A.O.U of Bologna (study group) and patients aged 18 to 90 years with clinical and radiological suspicion of endometriosis or healthy (control group)

Description

Inclusion Criteria:

  • age>18
  • Suspected diagnosis of epithelial ovarian cancer
  • Patients eligible for surgery
  • radiological imaging available
  • informed consent

Exclusion Criteria:

  • Patients with previous different malignancies
  • Patients with previous chemotherapeutic treatment
  • Patients with previous pelvic radiotherapeutic treatment

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Control
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
group 1
200 patients with suspected ovarian cancer
group 2
40 non oncological patients of witch 20 with endometriosis

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
development of a diagnostic and prognostic model based on the use of artificial intelligence
Time Frame: 2 years
development of a diagnostic and prognostic model based on the use of artificial intelligence in patients suffering from ovarian cancer related to endometriosis through the collection of all available information (clinical, pathological, molecular, genetic, radiomic data)
2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correlation of specific features with clinical characteristic
Time Frame: 2 years

Correlation of the histopathological features, immuno-phenotypic and molecular alterations present in epithelial ovarian tumors, in particular in associated endometriosis related- ovarian tumors, using an immunohistochemical profile and an NGS panel

  • evaluation of the miRNA expression profile in endometriosis related- ovarian tumors
  • identification and validation of radiomic features indicative of endometriosis related- ovarian tumors
  • build a three-dimensional map of the lesions in order to distinguish the tumor areas to be removed during surgery while preserving the organs not affected by the tumor pathology
2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 29, 2021

Primary Completion (Anticipated)

July 30, 2023

Study Completion (Anticipated)

November 28, 2023

Study Registration Dates

First Submitted

December 4, 2021

First Submitted That Met QC Criteria

December 4, 2021

First Posted (Actual)

December 17, 2021

Study Record Updates

Last Update Posted (Actual)

December 17, 2021

Last Update Submitted That Met QC Criteria

December 4, 2021

Last Verified

December 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 923/2021/Oss/AOUBo

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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