Skip to content

Radiomics and Radiogenomics Models to Predict Molecular Integrated Risk Classes and Prognostic Factors in Endometrial Cancer.

Radiomics and Radiogenomics Models to Predict Molecular Integrated Risk Classes and Prognostic Factors in Endometrial Cancer ID: ROMANTIC STUDY

Status
Recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06279832
Acronym
Romantic
Enrollment
1000
Registered
2024-02-28
Start date
2023-11-01
Completion date
2025-10-31
Last updated
2024-02-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Endometrial Cancer

Keywords

Radiomic, Ultrasound, MRI, Molecular sequencing, Radiogenomic, Omic analyses, molecular classification

Brief summary

The aim is to develop radiogenomics models to stratify patients into three main risk categories (Favorable, Intermediate, and Unfavorable) according to the ProMisE model (9) and use these models to predict the most prognostically relevant EC histopathological features (i.e. FIGO stage, degree of tumor differentiation, histotype, LVSI status, myometrial and cervical invasion, lymph node metastases). These models would support clinicians in personalizing surgical and adjuvant treatment choice among the options considered by the international guidelines.

Interventions

OTHERtrascriptomic profiling

The mutational and copy number analyses will be complemented by transcriptomic profiling. RNA will be extracted from FFPE samples using miRNAeasy FFPE kit (Qiagen) and checked for quality and quantify by 2100 Bioanalyzer instrument (Agilent) and Qubit Fluorometer (ThermoFisher), respectively. Transcriptome analyses will be performed by RNA-seq. We will apply total RNAseq using the Illumina® TruSeq Stranded Total RNA workflow that provides a solution allowing the detection of whole transcriptome, splicing variants, and transcript fusions of human RNA isolated from FFPE samples. Libraries will be run using the Illumina's Novaseq6000 system, with a least 50 millions of reads/sample, the minimum read depth for the correct evaluation of low expressed transcripts.

Sponsors

Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE

Intervention model description

As far as the sample size concerns, the number of images required for the analysis are a figure of at least 100 positive cases (15). We considered as positive patients with a favorable prognostic profile according to PORTEC-4a and also low and intermediate risk class according to ESGO/ESTRO/ESP 2020 recommendations.Therefore, we estimate to enroll at least 1000 patients in order to reach the 100 positive cases.

Eligibility

Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* Pathologically confirmed diagnosis of primary endometrial cancer (endometrioid, clear cell, serous, mixed, any grade) * FIGO stage IA-IB * Formalin-fixed, paraffin-embedded (FFPE) tissue at the diagnosis available - Availability of preoperative MRI scans in dicom (.dcm) format * Availability of preoperative US images in dicom (.dcm) format * Availability of preoperative CT-scan images in dicom (.dcm) format (optional) * Available clinical information (e.g. baseline information, surgery, adjuvant therapy, median follow up period 24 months)

Exclusion criteria

* Metastatic cancer to the uterus (not primary EC) * Uterine sarcoma * Conservative surgery * FIGO stage \> II * Formalin-fixed, paraffin-embedded (FFPE) tissue at the diagnosis not available * Patients without available MRI, US or CT-scan images on digital media * Clinical information not available or incomplete * Any other malignancy in the previous 5 years or synchronous * Patients aged under 18 years

Design outcomes

Primary

MeasureTime frameDescription
Predictive value of the modelup to one yearReceiver operating characteristic (ROC) curve and 95% confidence interval (CI) will be performed to determine cut-off values for the studied quantitative variables.

Secondary

MeasureTime frameDescription
Validity of the modelup to one yearTo test the validity of different clinical and ultrasound variables Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) will be determined

Countries

Italy

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026