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Trial Title:
MRI Radiomics Combined With Pathomics on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer
NCT ID:
NCT06126393
Condition:
Endometrial Neoplasms
Conditions: Official terms:
Endometrial Neoplasms
Conditions: Keywords:
Endometrial Neoplasms
machine learning
Radiomics
Pathomics
TCGA classification
Study type:
Observational
Overall status:
Not yet recruiting
Study design:
Time perspective:
Other
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
next generation sequencing AND Immunohistochemical examination
Description:
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.
Arm group label:
P53abn
Arm group label:
P53wt
Arm group label:
POLE Mut
Arm group label:
dMMR
Other name:
Magnetic resonance examination
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.
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.
Criteria for eligibility:
Study pop:
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.
Sampling method:
Non-Probability Sample
Criteria:
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
Gender:
Female
Minimum age:
18 Years
Maximum age:
80 Years
Locations:
Facility:
Name:
Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
Address:
City:
Fuzhou
Zip:
350014
Country:
China
Contact:
Last name:
Jian Chen, Master
Phone:
15806030009
Email:
marsz3@126.com
Start date:
January 1, 2024
Completion date:
June 30, 2027
Lead sponsor:
Agency:
Fujian Cancer Hospital
Agency class:
Other
Collaborator:
Agency:
Fujian Provincial Hospital
Agency class:
Other
Collaborator:
Agency:
First Affiliated Hospital of Fujian Medical University
Agency class:
Other
Collaborator:
Agency:
Gutian Hospital
Agency class:
Other
Source:
Fujian Cancer Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06126393
