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

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

Overview

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.

Interventions

• 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.

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 [Time frame: 2026-12-21]

Secondary outcome measures (1)

• 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]

Eligibility 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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Study design

Observational model

Cohort

Study locations

China · 1 center

• Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital — Fuzhou

Publications

• 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
• 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
• 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
• 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
• 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
• 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

Identifiers

NCT: NCT06126393 · CHENJIAN1

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗