Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study

Abstract

Objective: To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC).

Methods: A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models.

Results: The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility.

Conclusion: We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.

Keywords: adjuvant therapy (post-operative); endometrial neoplasms; magnetic resonance imaging; microsatellite instability; radiomics.

Figure 1

Flowchart of patients’ recruitment in Two Centers. EC, endometrial cancer; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; MSS, microsatellite stability.

Figure 2

Typical multiparametric MR images of two EC patients with MSI-H (A–C) and MSI-L/MSS (D–F). From left to right: axial T₂WI, CE-T₁WI, and DWI MR images and the corresponding region of interest (ROI).

Figure 3

Plots (A–F) present the boxplots of the six radiomics features with a significant difference between the MSI-H and MSI-L/MSS groups in the training cohort. * = p < 0.5; ** = p < 0.01; *** = p < 0.001.

Figure 4

Receiver operating characteristic (ROC) curves of the radiomics models derived from five classifiers in the training (A), internal test (B), and external validation (C) cohorts, respectively.

Figure 5

Plots (A–C) show the Rad-score for each patient, and boxplots (D–F) show the Rad-score in the training, internal test and external validation cohorts, respectively.

Figure 6

Decision curve analysis (DCA) of the radiomics models derived from the five different classifiers in the whole cohorts.