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

Endometrial Neoplasms Clinical Trial

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06126393
• Other study ID #: CHENJIAN1
• Status: Not yet recruiting
• Start date: January 1, 2024
• Est. completion date: June 30, 2027

Study information

• Verified date: November 2023
• Source: Fujian Cancer Hospital
• Contact: Jian Chen, Master
• Phone: 15806030009
• Email: marsz3[@]126.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 350
• Est. completion date: June 30, 2027
• Est. primary completion date: March 31, 2027
• Gender: Female
• Age group: 18 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

• Endometrial Neoplasms

Intervention

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.

Locations

Country	Name	City	State
China	Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital	Fuzhou	Fujian

Sponsors (4)

Lead Sponsor	Collaborator
Fujian Cancer Hospital	First Affiliated Hospital of Fujian Medical University, Fujian Provincial Hospital, Gutian Hospital

Country where clinical trial is conducted

China, 

References & Publications (6)

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. — View Citation

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 2 — View Citation

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. — View Citation

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 — View Citation

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/s11547-023-0159 — View Citation

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- — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Application of pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer	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
Primary	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	Application of magnetic resonance imaging radiomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer	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