Clinical trial NCT06126393: 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
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Endometrial Neoplasms
Keywords: Endometrial Neoplasms
machine learning
Radiomics
Pathomics
TCGA classification
Lead sponsor: Fujian Cancer Hospital
Sponsor class: OTHER_GOV
Collaborators: name: Fujian Provincial Hospital
name: First Affiliated Hospital of Fujian Medical University
name: Gutian Hospital
First posted: 2023-11-13
Enrollment: 350
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: OTHER
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.

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.

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.

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

Primary outcomes:
measure: Application of magnetic resonance imaging radiomics and pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer
timeFrame: 2026-12-21
Secondary outcomes:
measure: Application of magnetic resonance imaging radiomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer
timeFrame: 2026-12-21
Other outcomes:
measure: Application of pathomics to construct a model for predicting the molecular classification and prognosis of endometrial cancer
timeFrame: 2026-12-21
Locations:
city: Fuzhou
country: China
facility: Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
state: Fujian
Study officials:
References:
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/s11547-023-01590-0. Epub 2023 Jan 19.
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citation: 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.