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• Clinical Article •
Application research of imaging genomics in preoperative prediction of microsatellite stability of endometrial cancer using mp-MRI
ZHAO Jifu  TIAN Yan  MA Mimi  CAO Xinshan 

Cite this article as: ZHAO J F, TIAN Y, MA M M, et al. Application research of imaging genomics in preoperative prediction of microsatellite stability of endometrial cancer using mp-MRI[J]. Chin J Magn Reson Imaging, 2024, 15(11): 110-116, 122. DOI:10.12015/issn.1674-8034.2024.11.017.


[Abstract] Objective To explore the predictive value of multi-parametric magnetic resonance imaging (mp-MRI) radiomics models for preoperative microsatellite instability (MSI) status in endometrial carcinoma (EC).Materials and Methods A retrospective analysis was conducted on clinical, pathological, and imaging data of 171 patients with pathologically confirmed EC. The patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Using the 3D Slicer software, regions of interest (ROIs) were delineated on axial T2WI, diffusion-weighted imaging (DWI), and sagittal contrast-enhanced T1WI (CE-T1WI) delayed phase sequences, and radiomic features were extracted. Feature selection and calculation of radiomics scores (Rad-scores) were performed using intra-class correlation coefficient (ICC), least absolute shrinkage and selection operator (LASSO) algorithm, ten-fold cross-validation, and Pearson correlation test. Models were constructed using Rad-scores for individual sequences (T2WI model, DWI model, CE-T1WI model) and a combined model. Receiver operating characteristic (ROC) curves were plotted for each model, and model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and other metrics. The models were validated on the test set. The DeLong test was used to compare the differences in AUC values among the models.Results Among the 171 EC patients, 35 had MSI and 136 had microsatellite stability (MSS). From the T2WI, DWI, and CE-T1WI sequences, 6, 3, and 3 features were retained, respectively. In the training set, the area under the curve (AUC) values for the T2WI model, DWI model, CE-T1WI model, and combine model were 0.869 [95% confidence interval, (CI): 0.772-0.938], 0.768 (95% CI: 0.645-0.865), 0.912 (95% CI: 0.830-0.966), and 0.927 (95% CI: 0.865-0.966), respectively. In the validation set, the AUC values were 0.736 (95% CI: 0.573-0.896), 0.714 (95% CI: 0.560-0.872), 0.856 (95% CI: 0.675-0.990), and 0.907 (95% CI: 0.813-0.977) for the T2WI model, DWI model, CE-T1WI model, and combine model, respectively. The DeLong test indicated that there were statistically significant differences in AUC values between the DWI model and both the combine model and the CE-T1WI model (P<0.05). No statistically significant differences were found between the AUC values of the other model pairs (P>0.05).Conclusions The radiomic model based on mp-MRI can effectively predict the MSI status of EC preoperatively. The combined model shows higher predictive performance compared to individual sequences, which helps in formulating personalized treatment plans and improving patient outcomes.
[Keywords] endometrial carcinoma；microsatellite stability；magnetic resonance imaging；multi-parametric；radiomics；prediction

ZHAO Jifu   TIAN Yan   MA Mimi   CAO Xinshan*  

Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou256603, China

Corresponding author: CAO X S, E-mail: byfycxs@126.com

Conflicts of interest   None.

Received  2024-08-07
Accepted  2024-11-10
DOI: 10.12015/issn.1674-8034.2024.11.017
Cite this article as: ZHAO J F, TIAN Y, MA M M, et al. Application research of imaging genomics in preoperative prediction of microsatellite stability of endometrial cancer using mp-MRI[J]. Chin J Magn Reson Imaging, 2024, 15(11): 110-116, 122. DOI:10.12015/issn.1674-8034.2024.11.017.

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