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

Launched by REGINA ELENA CANCER INSTITUTE · Feb 18, 2025

Keywords

ClinConnect Summary

This clinical trial is exploring how artificial intelligence (AI) and machine learning can help predict the risk of endometrial cancer, which affects the lining of the uterus. The goal is to improve the way doctors manage patients with this condition by creating a more accurate risk prediction model. This model will consider different factors, including a patient’s unique genetic information, to better understand the chances of cancer developing or returning after treatment.

To participate in this trial, women aged 18 and older who have been diagnosed with certain types of endometrial conditions, such as hyperplasia or endometrioid adenocarcinoma, may be eligible. Participants will need to provide written consent and have their medical data and samples available for analysis. Throughout the study, participants can expect to contribute to important research that could lead to better treatment options for future patients with endometrial cancer. It's important to note that individuals with certain health issues or treatments, like uncontrolled medical conditions or previous cancer therapies, may not be able to join the trial.

Gender

FEMALE

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.