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

NCT06126393 | Not Yet Recruiting

• 1 other identifier: CHENJIAN1
• Study Type: observational
• Target: 350
• Locations: 1 country
• Sites: 1

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.

Conditions

Endometrial Neoplasms

Keywords

Endometrial Neoplasms; machine learning; Radiomics; Pathomics; TCGA classification

Outcome Measures

Primary Outcomes (1)

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

  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 Outcomes (1)

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

  2026-12-21

Other Outcomes (1)

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

  2026-12-21

Study Arms (4)

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

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

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

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

Interventions

next generation sequencing AND Immunohistochemical examination DIAGNOSTIC_TEST

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.

Also known as: Magnetic resonance examination

P53abn; P53wt; POLE Mut; dMMR

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: female
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Fujian Cancer Hospital: lead
• Fujian Provincial Hospital: collaborator
• First Affiliated Hospital of Fujian Medical University: collaborator
• Gutian Hospital: collaborator

Study Sites (1)

Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital

Fuzhou, Fujian, 350014, China

Location

Related Publications (6)

• Song XL, Luo HJ, Ren JL, Yin P, Liu Y, Niu J, Hong N. Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer. Radiol Med. 2023 Feb;128(2):242-251. doi: 10.1007/s11547-023-01590-0. Epub 2023 Jan 19.

  PMID: 36656410 RESULT

• Jamieson A, McAlpine JN. Molecular Profiling of Endometrial Cancer From TCGA to Clinical Practice. J Natl Compr Canc Netw. 2023 Feb;21(2):210-216. doi: 10.6004/jnccn.2022.7096.

  PMID: 36791751 RESULT

• Talhouk A, McConechy MK, Leung S, Li-Chang HH, Kwon JS, Melnyk N, Yang W, Senz J, Boyd N, Karnezis AN, Huntsman DG, Gilks CB, McAlpine JN. A clinically applicable molecular-based classification for endometrial cancers. Br J Cancer. 2015 Jul 14;113(2):299-310. doi: 10.1038/bjc.2015.190. Epub 2015 Jun 30.

  PMID: 26172027 RESULT

• Hou L, Zhou W, Ren J, Du X, Xin L, Zhao X, Cui Y, Zhang R. Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer. Front Oncol. 2020 Aug 20;10:1393. doi: 10.3389/fonc.2020.01393. eCollection 2020.

  PMID: 32974143 RESULT

• Lefebvre TL, Ueno Y, Dohan A, Chatterjee A, Vallieres M, Winter-Reinhold E, Saif S, Levesque IR, Zeng XZ, Forghani R, Seuntjens J, Soyer P, Savadjiev P, Reinhold C. Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer. Radiology. 2022 Nov;305(2):375-386. doi: 10.1148/radiol.212873. Epub 2022 Jul 12.

  PMID: 35819326 RESULT

• Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022 Apr 9;399(10333):1412-1428. doi: 10.1016/S0140-6736(22)00323-3.

  PMID: 35397864 RESULT

MeSH Terms

Conditions

Endometrial Neoplasms

Condition Hierarchy (Ancestors)

Uterine Neoplasms; Genital Neoplasms, Female; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Uterine Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases

Central Study Contacts

Jian Chen, Master

CONTACT

15806030009; marsz3@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 6, 2023
• First Posted: November 13, 2023
• Study Start: January 1, 2024
• Primary Completion (Estimated): March 31, 2027
• Study Completion (Estimated): June 30, 2027
• Last Updated: November 15, 2023

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)