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.
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)
Comorbidities not controlled with adequate medical therapy;
Infections of the endometrial cavity (pyometra);
Synchronous cancer;
Neoadjuvant treatments;
Patients suffering from endometrial cancer.
Other
Cross-sectional
Not listed
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.
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).
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).
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).
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Regina Elena Cancer Institute
Lead sponsor
University of Rome Tor Vergata
Collaborator
Casa Sollievo della Sofferenza-IRCCS, San Giovanni Rotondo
Collaborator
Universita degli Studi di Palermo
Collaborator
