A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer.

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

40 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-06-20

Study Completion Date

2026-06-20

Brief Summary

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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.

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Detailed Description

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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

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Endometrium Cancer

Study Design

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Observational Model Type

OTHER

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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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)

No interventions assigned to this group

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.

No interventions assigned to this group

Eligibility Criteria

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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

* 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.

Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No