Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer

Abstract

Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups. The hybrid radiomics model (HMRadSum) was developed by extracting quantitative imaging features and deep learning features from multiparametric MRI using emerging attention mechanism. Tumor markers were subsequently predicted utilizing an XGBoost classifier. Model performance and interpretability were evaluated using standard classification metrics, Gradient-weighted Class Activation Mapping (Grad-CAM), and SHapley Additive exPlanations (SHAP) techniques. For the MSI prediction task, the HMRadSum model achieved area-under-curve (AUC) value of 0.945 (95% CI 0.862-1.000) and accuracy of 0.889. For the Ki-67 prediction task, the AUC and accuracy of HMRadSum model was 0.888 (95% CI 0.743-1.000) and 0.810. This hybrid radiomics model effectively extracted features associated with EC gene expression, providing potential clinical implications for personalized diagnosis, treatment, and treatment strategy optimization.

Keywords: Attention mechanism; Endometrial cancer; Machine learning; Radiomics; SHAP analysis.

Fig. 1

Flowchart of inclusion and exclusion criteria for eligible patients.

Fig. 2

Flowchart of this research study.

Fig. 3

ROC curves of machine learning models in classic radiomics scheme.

Fig. 4

Visualization of features extracted by the CrossFormer model.

Fig. 5

SHAP summary plot of the HMRadSum model for predicting MSI.

Fig. 6

SHAP force plots explained how the HMRadSum model differentiated MMR gene expression in EC patients. The labels of patients A and B were MSI and MSS respectively. The feature value of T2-DL63 of patient A (−0.4046) was lower than that of patient B (0.0727). Combined with Fig. 5, it can be inferred that the probability of classification as MSI was increased.