Endometrium CancerNCT06841653
A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer.
An observational study of Endometrium Cancer, run by Regina Elena Cancer Institute at 1 site in Italy.
Trial summary
Prediction of preoperative endometrial biopsy: the evolution from hyperplasia to cancer, the prognosis and the risk of recurrence. Intelligence methods artificial risk will be used to redefine the current risk classes including our profile immuno-mutational to provide a more precise characterization and closer to the… Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Endometrium Cancer
- How many people
- 40 participants
- Where
- 1 siteItaly
- Study dates
- 20 Jun 2024to 20 Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Patients suffering from endometrial cancer.
Who may be able to join
- Age > 18 years;
- Histological diagnosis of endometrial hyperplasia, endometrioid adenocarcinoma of the endometrium, healthy endometrium in patients undergoing total hysterectomy for benign extra-endometrial disease;
- Written informed consent (to the study and data processing), for the party's patients only prospective and/or in follow-up) For the retrospective cohort: availability of samples adequately stored at the biobank of the Institute and availability of data relating to follow-up (at least 2 years)
Who cannot join
- All exclusion criteria adopted in the surgical protocols will be applied to the study. In particular:
- Comorbidities not controlled with adequate medical therapy;
- Infections of the endometrial cavity (pyometra);
- Synchronous cancer;
- Neoadjuvant treatments;
- Previous radiotherapy treatments of the pelvic region;
- Hormone therapies.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site, 1 recruiting
- Recruiting
IRCCS National Cancer Institute "Regina Elena"
Rome, 00144
Enrico Vizza, Medical Doctor, +39 06-52666974 ext. +39, enrico.vizza@ifo.it
About this study
Official title: A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer: a Composite Approach Integrating the MultiOMics IMmune-IConographic Pattern (MOMIMIC Score) Towards Precision Oncology and Surgery.
Prediction of preoperative endometrial biopsy: the evolution from hyperplasia to cancer, the prognosis and the risk of recurrence. Intelligence methods artificial risk will be used to redefine the current risk classes including our profile immuno-mutational to provide a more precise characterization and closer to the real prognosis of the patient.
Read the detailed description
Identify new risk factors for endometrial cancer, using an integrated multi-omics approach linked to a specific immune pattern (called MOMIMIC score) useful for improving oncology and surgery precision. The aim is to evaluate the predictive value of the MOMIMIC score for early identification of progression from precancerous lesions to endometrial carcinoma, prognosis and relapses, to help the clinician in the decision to treatments. Through the identification during hysteroscopy of the most appropriate site for biopsies targeted endometrials, through an artificial intelligence algorithm applied to the video system hysteroscopic which, by comparing the information from the omics approach and the hysteroscopic image combined with radiogenomic information, it could help the gynecologist in the procedure and provide information on the prognosis through the omics-iconographic profile in order to calculate a preoperative predictive score. Furthermore by modulating the surgical radicality, according to the information obtained, there will be a tendency to preserve fertility in young patients with a low-risk profile (since currently the risk factors are not sufficient to discriminate for a non-treatment radical). This will help the surgeon through an artificial intelligence algorithm applied to the system robotic/laparoscopic video, will guide the operator in decision-making procedures regarding the resection margins tumor, metastasis localization, pathological lymph node detection, and imaging driven by biomolecular information.
- Conditions
- Endometrium Cancer
- Keywords
- MultiOMics IMmune-IConographic pattern
- Medical subject headings
- Endometrial 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 |
|---|---|---|
| Retrospective cohort | Fresh tissue samples stored at -80°C, collected at the Institute's IRE Biobank (a starting from 2019) and tissue preserved in paraffin at the biobank at 4°C at the UOC Pathological Anatomy archive, for carrying out WES, RNA-seq, scRNA-seq, spatial transcriptomics, metabolomics, proteomics, digital pathology, immune infiltrate characterization (e.g. FACS, immunohistochemistry) | |
| Prospective cohort | Collection of tissue samples obtained at the time of surgery and verified by the anatomical pathologist for the actual availability and adequacy, for the purpose of the creation of organoids (Patient-Derived Organoids, PDO), cell lines and co-cultures (created with the patient's own peripheral immune cells, collected and processed), in the context of which secretomics analyzes will be conducted using Olink and Luminex. |
What is being measured
The primary outcome is the main question the study is designed to answer.
OS (overall survival)
The study will evaluate the predictive value of the MultiOMics-IMmune-Iconographic model (global mutational profiling, RNA-seq of single cells coupled with the Spatial transcriptomics, proteomic and metabolomic profile) following the data obtained from the identification of new risk factors for endometrial carcinoma, in patients at high or low risk. They will be tested from Random Survival Forest to determine how capable a feature is discriminate between the 4 groups in terms of OS (overall survival). The selected features will be used in combination with the known prognostic clinical and histopathological risk factors described by ESMO-ESGO-ESTRO.
Measured: 24 months
DSF (disease-free survival)
The study will evaluate the predictive value of the MultiOMics-IMmune-Iconographic model (global mutational profiling, RNA-seq of single cells coupled with the Spatial transcriptomics, proteomic and metabolomic profile) following the data obtained from the identification of new risk factors for endometrial carcinoma, in patients at high or low risk.
They will be tested via Random Survival Forest to determine how capable a feature is discriminate between the 4 groups in terms of impact on progression to cancer, recurrence, DFS (disease-free survival).
Measured: 24 months
Secondary outcomes (2)
Area under the curve (AUC)
In order to obtain a more robust estimate of accuracy of the MultiOMics-IMmune predictive signature, for validation, we will use two groups of patients composed of a minimum of 200 cases (100 high risk and 100 low risk), at a reduction from 30% confidence interval to 95% when signature performance are kept constant. Considering the area under the curve (AUC).
Measured: 24 months
Accuracy (ACC)
In order to obtain a more robust estimate of accuracy of the MultiOMics-IMmune predictive signature, for validation, we will use two groups of patients composed of a minimum of 200 cases (100 high risk and 100 low risk), at a reduction from 30% confidence interval to 95% when signature performance are kept constant. Considering Accuracy (ACC).
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Feb 2025
- Study start20 Jun 2024
- Primary completion20 Jun 2026 (estimated)
- Study completion20 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endometrium CancerStudies of the same condition, open and under way.
- 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
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Regina Elena Cancer Institute
- Sponsor type
- University, hospital or other
- Collaborators
- University of Rome Tor Vergata, IRCCS Casa Sollievo della Sofferenza, Universita degli Studi di Palermo
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06841653
- Sponsor's protocol ID
- RS203/IRE/24
- Status last verified
- Feb 2025
- First submitted
- 18 Feb 2025
- Last updated
- 24 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 24 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer: a Composite Approach Integrating the MultiOMics IMmune-IConographic Pattern (MOMIMIC Score) Towards Precision Oncology and Surgery.
- ClinicalTrials.gov ID
- NCT06841653
- Acronym / sponsor ID
- RS203/IRE/24
- Conditions
- Endometrium Cancer
- Keywords
- MultiOMics IMmune-IConographic pattern
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Regina Elena Cancer InstituteUniversity, hospital or other
- Collaborators
- University of Rome Tor Vergata, IRCCS Casa Sollievo della Sofferenza, Universita degli Studi di Palermo
- Study start
- 20 Jun 2024 (actual)
- Primary completion
- 20 Jun 2026 (estimated)
- Study completion
- 20 Jun 2026 (estimated)
- First posted
- 24 Feb 2025 (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.
Prediction of preoperative endometrial biopsy: the evolution from hyperplasia to cancer, the prognosis and the risk of recurrence. Intelligence methods artificial risk will be used to redefine the current risk classes including our profile immuno-mutational to provide a more precise characterization and closer to the real prognosis of the patient.
Detailed description
Identify new risk factors for endometrial cancer, using an integrated multi-omics approach linked to a specific immune pattern (called MOMIMIC score) useful for improving oncology and surgery precision. The aim is to evaluate the predictive value of the MOMIMIC score for early identification of progression from precancerous lesions to endometrial carcinoma, prognosis and relapses, to help the clinician in the decision to treatments. Through the identification during hysteroscopy of the most appropriate site for biopsies targeted endometrials, through an artificial intelligence algorithm applied to the video system hysteroscopic which, by comparing the information from the omics approach and the hysteroscopic image combined with radiogenomic information, it could help the gynecologist in the procedure and provide information on the prognosis through the omics-iconographic profile in order to calculate a preoperative predictive score. Furthermore by modulating the surgical radicality, according to the information obtained, there will be a tendency to preserve fertility in young patients with a low-risk profile (since currently the risk factors are not sufficient to discriminate for a non-treatment radical). This will help the surgeon through an artificial intelligence algorithm applied to the system robotic/laparoscopic video, will guide the operator in decision-making procedures regarding the resection margins tumor, metastasis localization, pathological lymph node detection, and imaging driven by biomolecular information.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective cohort | – | –Fresh tissue samples stored at -80°C, collected at the Institute's IRE Biobank (a starting from 2019) and tissue preserved in paraffin at the biobank at 4°C at the UOC Pathological Anatomy archive, for carrying out WES, RNA-seq, scRNA-seq, spatial transcriptomics, metabolomics, proteomics, digital pathology, immune infiltrate characterization (e.g. FACS, immunohistochemistry) |
| Prospective cohort | – | –Collection of tissue samples obtained at the time of surgery and verified by the anatomical pathologist for the actual availability and adequacy, for the purpose of the creation of organoids (Patient-Derived Organoids, PDO), cell lines and co-cultures (created with the patient's own peripheral immune cells, collected and processed), in the context of which secretomics analyzes will be conducted using Olink and Luminex. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients suffering from endometrial cancer.
Key inclusion criteria (3 listed)
- Age > 18 years;
- Histological diagnosis of endometrial hyperplasia, endometrioid adenocarcinoma of the endometrium, healthy endometrium in patients undergoing total hysterectomy for benign extra-endometrial disease;
- Written informed consent (to the study and data processing), for the party's patients only prospective and/or in follow-up) For the retrospective cohort: availability of samples adequately stored at the biobank of the Institute and availability of data relating to follow-up (at least 2 years)
Key exclusion criteria (7 listed)
- All exclusion criteria adopted in the surgical protocols will be applied to the study. In particular:
- Comorbidities not controlled with adequate medical therapy;
- Infections of the endometrial cavity (pyometra);
- Synchronous cancer;
- Neoadjuvant treatments;
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
OS (overall survival)
Time frame: 24 months
The study will evaluate the predictive value of the MultiOMics-IMmune-Iconographic model (global mutational profiling, RNA-seq of single cells coupled with the Spatial transcriptomics, proteomic and metabolomic profile) following the data obtained from the identification of new risk factors for endometrial carcinoma, in patients at high or low risk. They will be tested from Random Survival Forest to determine how capable a feature is discriminate between the 4 groups in terms of OS (overall survival). The selected features will be used in combination with the known prognostic clinical and histopathological risk factors described by ESMO-ESGO-ESTRO.
DSF (disease-free survival)
Time frame: 24 months
The study will evaluate the predictive value of the MultiOMics-IMmune-Iconographic model (global mutational profiling, RNA-seq of single cells coupled with the Spatial transcriptomics, proteomic and metabolomic profile) following the data obtained from the identification of new risk factors for endometrial carcinoma, in patients at high or low risk. They will be tested via Random Survival Forest to determine how capable a feature is discriminate between the 4 groups in terms of impact on progression to cancer, recurrence, DFS (disease-free survival).
2 secondary outcomes
Area under the curve (AUC)
Time frame: 24 months
Accuracy (ACC)
Time frame: 24 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Feb 2025.
- Individual participant dataSharing planned: Undecided
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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.
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 Endometrium Cancer, run by Regina Elena Cancer Institute 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 3 inclusion and 7 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: looking for participants now, as of the sponsor's last update on 24 Feb 2025. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Enrico Vizza, Doctor (06 52666974 ext. +39, enrico.vizza@ifo.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
OS (overall survival) (time frame: 24 months). The study lists 2 primary outcomes in all. 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 24 Feb 2025.