A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer.
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Endometrium Cancer. Basic parameters: from 18 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Italy
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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.
Overview
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.
Primary outcome measures
- OS (overall survival) [Time frame: 24 months]
- DSF (disease-free survival) [Time frame: 24 months]
Secondary outcome measures (2)
- Area under the curve (AUC) [Time frame: 24 months]
- Accuracy (ACC) [Time frame: 24 months]
Eligibility criteria
Inclusion criteria
- 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)
Exclusion criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
Italy · 1 center
- IRCCS National Cancer Institute "Regina Elena" — Rome
Identifiers
NCT: NCT06841653 · RS203/IRE/24