Jin-yuan Liao

Comparative analysis of imaging and pathological features in diagnosis of endometrial carcinosarcoma based on multimodal MRI

This case report aims to present a rare case of endometrial carcinosarcoma, a highly malignant tumor with a poor prognosis. The primary objective is to describe this unique case's clinical presentation, multimodal magnetic resonance imaging (MRI) features, typical histopathological characteristics and surgical treatment. A detailed analysis of the patient's medical history, preoperative imaging evaluation, and treatment approach was conducted. This case report includes high-resolution images and figures, showcasing MRI scans, surgical treatment, and histopathology slides related to the case. The case report outlines imaging findings of a rare case of endometrial carcinosarcoma. Multimodal imaging such as T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) scanning could accurately identify the histopathological features of the case. Surgical resection is the best treatment, and preoperative imaging evaluation should be particularly important. This case report highlights endometrial carcinosarcoma's rarity and diagnostic challenges. Multimodal MRI has significant value in diagnosing endometrial carcinosarcoma. This technology not only improves the sensitivity, specificity, and accuracy of diagnosis, but also helps to more accurately evaluate the staging and grading of tumors. By comparing imaging features and pathological results, studies have found that multimodal MRI can clearly show the anatomical structure, pathological nature, and extent of the tumor, with a high degree of consistency with the pathological diagnosis. In particular, when differentiating endometrial carcinosarcoma from low-risk endometrial cancer, multimodal MRI combined with serum carbohydrate antigen 125 (CA125) and E-box binding zinc finger protein 1 (ZEB1) detection can further improve the sensitivity and specificity of differential diagnosis. In addition, research has found that the ADC value of the tumor tissue in different pathological grades is related to the multimodal MRI, which helps to better understand the biological behavior and prognosis of the tumor. In summary, multimodal MRI is an effective diagnostic tool that can provide important evidence for the precise diagnosis and treatment of endometrial carcinosarcoma.

Predicting Microsatellite Instability in Endometrial Cancer by Multimodal Magnetic Resonance Radiomics Combined with Clinical Factors

To develop a nomogram integrating clinical and multimodal MRI features for non-invasive prediction of microsatellite instability (MSI) in endometrial cancer (EC), and to evaluate its diagnostic performance. This retrospective multicenter study included 216 EC patients (mean age, 54.68 ± 8.72 years) from two institutions (2017-2023). Patients were classified as MSI (n=59) or microsatellite stable (MSS, n=157) based on immunohistochemistry. Institution A data were randomly split into training (n=132) and testing (n=33) sets (8:2 ratio), while Institution B data (n=51) served as external validation. Eight machine learning algorithms were used to construct models. A nomogram combining radiomics score and clinical predictors was developed. Performance was evaluated via receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). The T2-weighted imaging (T2WI) radiomics model showed the highest area under the receiver operating characteristic curve (AUC) among single sequences (training set:0.908; test set:0.838). The combined-sequence radiomics model achieved superior performance (AUC: training set=0.983, test set=0.862). The support vector machine (SVM) outperformed other algorithms. The nomogram integrating rad-score and clinical features demonstrated higher predictive efficacy than the clinical model (test set: AUC=0.904 vs. 0.654; p < 0.05) and comparable to the multimodal radiomics model. DCA indicated significant clinical utility for both nomogram and radiomics models. The clinical-radiomics nomogram effectively predicts MSI status in EC, offering a non-invasive tool for guiding immunotherapy decisions.