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

NCT06841653 | Recruiting

• 1 other identifier: RS203/IRE/24
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Endometrium Cancer

Keywords

MultiOMics IMmune-IConographic pattern

Outcome Measures

Primary Outcomes (2)

• 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 Outcomes (2)

• Area under the curve (AUC)

  24 months

• Accuracy (ACC)

  24 months

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: * patients undergoing hysterectomy for benign extra-endometrial pathology; * patients diagnosed with endometrial hyperplasia without atypia; * patients diagnosed with endometrial hyperplasia without atypia; * patients diagnosed with endometrial carcinoma of the endometrioid histology at any stage disease
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients suffering from endometrial cancer.

You may qualify if:

• 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)

You may not qualify if:

• 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.

Sponsors & Collaborators

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

Study Sites (1)

IRCCS National Cancer Institute "Regina Elena"

Rome, 00144, Italy

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

Tissue samples and peripheral venous blood samples

MeSH Terms

Conditions

Endometrial Neoplasms

Condition Hierarchy (Ancestors)

Uterine Neoplasms; Genital Neoplasms, Female; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Uterine Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases

Central Study Contacts

Enrico Vizza, Doctor

CONTACT

06 52666974; enrico.vizza@ifo.it

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 18, 2025
• First Posted: February 24, 2025
• Study Start: June 20, 2024
• Primary Completion (Estimated): June 20, 2026
• Study Completion (Estimated): June 20, 2026
• Last Updated: February 24, 2025

Record last verified: 2025-02

Locations

IT (1)