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

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.

Study Overview

• Status: Recruiting
• Conditions: Endometrium Cancer

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 Type: Observational
• Enrollment (Estimated): 40

Contacts and Locations

• Study Contact: Name: Enrico Vizza, Doctor; Phone Number: +39 06 52666974; Email: enrico.vizza@ifo.it

Study Locations

• Italy
  • Rome, Italy, 00144
    • Recruiting
    • IRCCS National Cancer Institute "regina Elena"
    • Contact: Enrico Vizza, Medical Doctor; Phone Number: +39 +39 06-52666974; Email: enrico.vizza@ifo.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients suffering from endometrial cancer.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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).	24 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	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).	24 months

Collaborators and Investigators

• Sponsor: Regina Elena Cancer Institute
• Collaborators: University of Rome Tor Vergata; Universita degli Studi di Palermo; Casa Sollievo della Sofferenza-IRCCS, San Giovanni Rotondo

Study record dates

Study Major Dates

• Study Start (Actual): June 20, 2024
• Primary Completion (Estimated): June 20, 2026
• Study Completion (Estimated): June 20, 2026

Study Registration Dates

• First Submitted: February 18, 2025
• First Submitted That Met QC Criteria: February 18, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 18, 2025
• Last Verified: February 1, 2025

More Information

Terms related to this study

• Keywords: MultiOMics IMmune-IConographic pattern
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Uterine Diseases; Genital Diseases, Female; Genital Neoplasms, Female; Uterine Neoplasms; Endometrial Neoplasms
• Other Study ID Numbers: RS203/IRE/24

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No