Patients With Suspected Ovarian CarcinomaATENANCT05161949
Artificial inTelligence in eNdometriosis-related ovArian Cancer and Precision Surgery in eNdometriosis-related ovArian Cancer
An observational study of Patients With Suspected Ovarian Carcinoma and Non Oncological Patients or With Endometriosis, run by IRCCS Azienda Ospedaliero-Universitaria di Bologna at 1 site in Italy.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 90 years, women only
- Conditions the study is for
- Patients With Suspected Ovarian Carcinoma, Non Oncological Patients or With Endometriosis
- How many people
- 240 participants
- Where
- 1 siteItaly
- Study dates
- 29 Nov 2021to 28 Nov 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- Women only
- Adult, Older adult
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)
Who may be able to join
- age>18
- Suspected diagnosis of epithelial ovarian cancer
- Patients eligible for surgery
- radiological imaging available
- informed consent
Who cannot join
- Patients with previous different malignancies
- Patients with previous chemotherapeutic treatment
- Patients with previous pelvic radiotherapeutic treatment
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
IRCCS- Azienda Ospedaliera-Universitaria di Bologna
Bologna, Bo, 40138
Anna Myriam Perrone, MD, +39 051 2144392, myriam.perrone@unibo.it
Anna Myriam Perrone
About this study
Official title: 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.
Read more
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
- Keywords
- endometriosis, ovarian cancer, artificial intelligence
- Medical subject headings
- Endometriosis, Ovarian Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| group 1 | 200 patients with suspected ovarian cancer | |
| group 2 | 40 non oncological patients of witch 20 with endometriosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
development of a diagnostic and prognostic model based on the use of artificial intelligence
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)
Measured: 2 years
Secondary outcomes (1)
Correlation of specific features with clinical characteristic
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
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Dec 2021
- Study start29 Nov 2021
- Primary completion30 Jul 2023 (estimated)
- Study completion28 Nov 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Patients With Suspected Ovarian CarcinomaStudies of the same condition, open and under way.
- More trials run by IRCCS Azienda Ospedaliero-Universitaria di BolognaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 240 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Anna Myriam Perrone, IRCCS Azienda Ospedaliero-Universitaria di Bologna
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05161949
- Sponsor's protocol ID
- 923/2021/Oss/AOUBo
- Acronym
- ATENA
- Status last verified
- Dec 2021
- First submitted
- 4 Dec 2021
- Last updated
- 17 Dec 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial inTelligence as Tool for Early Diagnosis and Precision Surgery in eNdometriosis-related ovArian Cancer
- ClinicalTrials.gov ID
- NCT05161949
- Acronym / sponsor ID
- ATENA / 923/2021/Oss/AOUBo
- Keywords
- endometriosis, ovarian cancer, artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 240 participants (estimated)
- Lead sponsor
- IRCCS Azienda Ospedaliero-Universitaria di BolognaUniversity, hospital or other
- Study start
- 29 Nov 2021 (actual)
- Primary completion
- 30 Jul 2023 (estimated)
- Study completion
- 28 Nov 2023 (estimated)
- First posted
- 17 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| group 1 | – | –200 patients with suspected ovarian cancer |
| group 2 | – | –40 non oncological patients of witch 20 with endometriosis |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- Female only
- Age groups
- Adult, Older adult
- 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)
Key inclusion criteria (5 listed)
- age>18
- Suspected diagnosis of epithelial ovarian cancer
- Patients eligible for surgery
- radiological imaging available
- informed consent
Key exclusion criteria (3 listed)
- Patients with previous different malignancies
- Patients with previous chemotherapeutic treatment
- Patients with previous pelvic radiotherapeutic treatment
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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)
1 secondary outcome
Correlation of specific features with clinical characteristic
Time frame: 2 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Dec 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
IRCCS- Azienda Ospedaliera-Universitaria di Bologna
Bologna, Italy
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Patients With Suspected Ovarian Carcinoma and Non Oncological Patients or With Endometriosis, run by IRCCS Azienda Ospedaliero-Universitaria di Bologna at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 17 Dec 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
development of a diagnostic and prognostic model based on the use of artificial intelligence (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Dec 2021; AzTrials last refreshed its copy on 7 Nov 2026.