Predicting recurrence risk in endometrial cancer: a multisequence MRI intratumoral and peritumoral radiomics nomogram approach

Abstract

Objective: To assess the predictive value of a nomogram model incorporating clinical factors and multisequence MRI intratumoral and peritumoral radiomics features for estimating recurrence risk in endometrial cancer (EC) patients.

Materials and methods: This retrospective study included 184 patients with EC. The samples were randomly divided into a training set and a test set according to a 7:3 ratio, and intratumoral and peritumoral radiomics features were extracted from diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) sequences. Optimal radiomics features were selected using the f-classification function, minimum redundancy maximum relevance (mRMR) method, and least absolute shrinkage and selection operator (Lasso). Nine machine learning classifiers were employed to construct the intratumoral model (RM1). The best-performing classifiers were then used to develop the intratumoral and peritumoral 2 mm radiomics model (RM2) and the intratumoral and peritumoral 4 mm radiomics model (RM3). The radiomics scores (Rad-score) from the top-performing radiomics model were combined with clinical factors to create the nomogram model (FM). The predictive performance of the FM model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve assessment, clinical decision curve analysis (DCA), clinical impact curve (CIC), and the DeLong test. Feature importance analysis using the SHapley Additive exPlanations (SHAP) methodology.

Results: The logistic regression classifier (LR) showed optimal predictive efficacy, and RM2 demonstrated the best diagnostic performance. The clinical decision curve and DeLong test results indicated that the FM model was the optimal recurrence model in EC patients.

Conclusion: A nomogram model integrating MRI radiomics features from intratumoral and peritumoral regions and clinical factors effectively predicts recurrence in EC patients.

Keywords: endometrial cancer; machine learning; magnetic resonance imaging; peritumoral radiomics; recurrence.

Figure 1

Patient screening flowchart.

Figure 2

Experiment flowchart.

Figure 3

(A) ROC curves of nine machine learning classifiers in training and validation sets, (B) Calibration curves of nine machine learning classifiers in validation sets, and (C) DCA of nine machine learning classifiers in validation sets. Treat None: no action for all patients. Treat All: all patients were treated.

Figure 4

ROC curves for the three models in the training, test, and validation sets. (A) RM1, (B) RM2, (C) RM3.

Figure 5

The nomogram for predicting the risk of recurrence in EC patients.

Figure 6

(A) ROC curves of the FM in the training and test sets, and (B) Calibration curves of the FM in the training and test sets. Apparent: empirical calibration curves. Bias-corrected: bias-corrected calibration curves. Ideal: perfect calibration curve.

Figure 7

(A) Three models of DCA. Model 1: CM. Model 2: RM2. Model 3: FM. None: no action for all patients. All: all patients were treated. (B) CIC for CM, RM2 and FM.

Figure 8

Overall visualization of the model through SHAP. (A) The SHAP beeswarm plot shows the positive or negative effects of each feature on the prediction probability through red and blue colors. (B) The SHAP bar chart shows the weight of the four most important characteristics in the model.