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

Sponsor

Fujian Cancer Hospital (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT06126393

Collaborator

Fujian Provincial Hospital (Other), First Affiliated Hospital of Fujian Medical University (Other), Gutian Hospital (Other)

350

Enrollment

Location

41.9

Anticipated Duration (Months)

8.3

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Endometrial Neoplasms	Diagnostic Test: next generation sequencing AND Immunohistochemical examination

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:

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

Study Design

Study Type:

Observational

Anticipated Enrollment :

350 participants

Observational Model:

Cohort

Time Perspective:

Other

Official Title:

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

Anticipated Study Start Date :

Jan 1, 2024

Anticipated Primary Completion Date :

Mar 31, 2027

Anticipated Study Completion Date :

Jun 30, 2027

Arms and Interventions

Arm	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

Outcome Measures

Primary Outcome Measures

Application of magnetic resonance imaging radiomics and pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer [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.

Secondary Outcome Measures

Application of magnetic resonance imaging radiomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer [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.

Other Outcome Measures

Application of pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer [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.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 80 Years

Sexes Eligible for Study:

Female

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