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.
•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;
• 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
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.
Cohort
Other
next generation sequencing AND Immunohistochemical examination
Diagnostic testFirst, 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.
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.
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.
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.
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Fujian Cancer Hospital
Lead sponsor
Fujian Provincial Hospital
Collaborator
First Affiliated Hospital of Fujian Medical University
Collaborator
Gutian Hospital
Collaborator
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