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

Last updated: February 18, 2025
Sponsor: Regina Elena Cancer Institute
Overall Status: Active - Recruiting

Phase

N/A

Condition

Endometrial Cancer

Treatment

N/A

Clinical Study ID

NCT06841653
RS203/IRE/24
  • Ages > 18
  • Female

Study 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 real prognosis of the patient.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • Age > 18 years;

  • Histological diagnosis of endometrial hyperplasia, endometrioid adenocarcinoma ofthe endometrium, healthy endometrium in patients undergoing total hysterectomy forbenign extra-endometrial disease;

  • Written informed consent (to the study and data processing), for the party'spatients only prospective and/or in follow-up) For the retrospective cohort:availability of samples adequately stored at the biobank of the Institute andavailability of data relating to follow-up (at least 2 years)

Exclusion

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.

Study Design

Total Participants: 40
Study Start date:
June 20, 2024
Estimated Completion Date:
June 20, 2026

Study 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.

Connect with a study center

  • IRCCS National Cancer Institute "Regina Elena"

    Rome, 00144
    Italy

    Active - Recruiting

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