MRI Radiomics Combined With Pathomics on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer

November 13, 2023 updated by: Fujian Cancer Hospital

Study on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer Using a Model Constructed by Magnetic Resonance Imaging Radiomics Combined With Pathomics

Molecular typing provides accurate information for the diagnosis, treatment and prognosis prediction of endometrial cancer, which has important clinical significance. However, due to its high cost and complicated process, it is difficult to be widely used in clinical practice. Based on the artificial intelligence method, this study fused the characteristics of MRI radiomics and pathomics, combined with the clinical pathological information, built a model to predict the molecular typing and prognosis, analyzed the biological characteristics of endometrial cancer from the multi-scale level, guided the personalized and precise diagnosis and treatment, in order to improve the prognosis of patients.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

In this project, 150 cases of endometrial cancer were retrospectively collected, and 200 cases of endometrial cancer will be prospectively collected. All patients were pathologically confirmed and underwent Promise molecular typing. Before treatment, all patients completed abdominal MRI. Based on artificial intelligence technology, image features were extracted from magnetic resonance imaging, pathological features were extracted from pathological data, and clinical pathological data were collected at the same time. The treatment effect, recurrence and metastasis of patients were followed up, and the five-year survival rate and five-year progression free survival rate were calculated. It is proposed to focus on the following research:

  1. Construction of molecular typing and prognosis prediction model of endometrial cancer based on magnetic resonance imaging Radiomics
  2. Construction of molecular typing and prognosis prediction model of endometrial cancer based on pathomics.
  3. Construction of a prediction model for molecular typing of endometrial cancer by integrating pathomics and radiomics.

Study Type

Observational

Enrollment (Estimated)

350

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Jian Chen, Master
  • Phone Number: 15806030009
  • Email: marsz3@126.com

Study Locations

  • China
    • Fujian
      • Fuzhou, Fujian, China, 350014
        • Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

  1. All patients were pathologically confirmed as endometrial malignant tumors, and molecular typing was performed.
  2. Patients with endometrial cancer who were admitted to Fujian cancer hospital from January 2020 to December 2023 were retrospectively collected. Meanwhile, from January 1, 2024, all consecutive patients with newly diagnosed endometrial cancer were enrolled and signed the informed consent.

Description

Inclusion Criteria:

  • •Pathologically confirmed as endometrial malignant tumor with complete pathological H＆E stained sections;

    • Age ≥ 18 years and ≤ 80 years;
    • No other malignant cancers was found;
    • The complete immunohistochemical and second-generation sequencing results can be used for the molecular typing of ProMisE;
    • Magnetic resonance examination was performed within 2 weeks before treatment, and there was at least one measurable lesion according to RECIST 1.1 Criteria.

Exclusion Criteria:

  • • The image quality is poor or the tumor is too small due to serious graphic artifact and degeneration, and the ROI cannot be accurately delineated;

    • Patients who received any antitumor therapy before surgery;
    • Diagnostic endometrial biopsy before MRI

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
POLE Mut
The POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation.
Diagnostic test: next generation sequencing AND Immunohistochemical examination
First, the mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype; Then the POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation; Finally, p53 was detected by immunohistochemistry, and p53 mutant (p53 abn) and p53 wild-type (p53wt) were distinguished.
Other Names:
  • Magnetic resonance examination
dMMR
The mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype
Diagnostic test: next generation sequencing AND Immunohistochemical examination
First, the mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype; Then the POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation; Finally, p53 was detected by immunohistochemistry, and p53 mutant (p53 abn) and p53 wild-type (p53wt) were distinguished.
Other Names:
  • Magnetic resonance examination
P53abn
The expression of p53 was detected by immunohistochemistry. The abnormality of p53 protein expression (completely negative or diffusely strong positive in the nucleus) or expression location (cytoplasmic expression) was judged as p53abn, otherwise it was p53wt.
Diagnostic test: next generation sequencing AND Immunohistochemical examination
First, the mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype; Then the POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation; Finally, p53 was detected by immunohistochemistry, and p53 mutant (p53 abn) and p53 wild-type (p53wt) were distinguished.
Other Names:
  • Magnetic resonance examination
P53wt
The expression of p53 was detected by immunohistochemistry. The abnormality of p53 protein expression (completely negative or diffusely strong positive in the nucleus) or expression location (cytoplasmic expression) was judged as p53abn, otherwise it was p53wt.
Diagnostic test: next generation sequencing AND Immunohistochemical examination
First, the mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype; Then the POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation; Finally, p53 was detected by immunohistochemistry, and p53 mutant (p53 abn) and p53 wild-type (p53wt) were distinguished.
Other Names:
  • Magnetic resonance examination

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Application of magnetic resonance imaging radiomics and pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer
Time Frame: 2026-12-21
The imaging and pathological features of endometrial cancer patients were extracted by artificial intelligence method. Combined with clinicopathological risk factors and survival time, an imaging nomogram was constructed by lasso regression method to predict the molecular classification and prognosis of endometrial cancer. ROC curve was used to evaluate the test efficiency of the model.
2026-12-21

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Application of magnetic resonance imaging radiomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer
Time Frame: 2026-12-21
The imaging features of endometrial cancer patients were extracted by artificial intelligence method. Combined with clinicopathological risk factors and survival time, an imaging nomogram was constructed by lasso regression method to predict the molecular classification and prognosis of endometrial cancer. ROC curve was used to evaluate the test efficiency of the model.
2026-12-21

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Application of pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer
Time Frame: 2026-12-21
The pathomics features of endometrial cancer patients were extracted by artificial intelligence method. Combined with clinicopathological risk factors and survival time, an imaging nomogram was constructed by lasso regression method to predict the molecular classification and prognosis of endometrial cancer. ROC curve was used to evaluate the test efficiency of the model.
2026-12-21

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fujian Cancer Hospital

Collaborators

First Affiliated Hospital of Fujian Medical University
Fujian Provincial Hospital
Gutian Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

January 1, 2024

Primary Completion (Estimated)

March 31, 2027

Study Completion (Estimated)

June 30, 2027

Study Registration Dates

First Submitted

November 6, 2023

First Submitted That Met QC Criteria

November 6, 2023

First Posted (Actual)

November 13, 2023

Study Record Updates

Last Update Posted (Actual)

November 15, 2023

Last Update Submitted That Met QC Criteria

November 13, 2023

Last Verified

November 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CHENJIAN1

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

All relevant patient personal information and follow-up results of this study were saved by the principal investigator, and there was no plan to share them with other investigators

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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