Multiparametric MRI-based radiomics and deep learning for differentiating uterine serous carcinoma from endometrioid carcinoma: a multicenter retrospective study

Abstract

Background: Uterine serous carcinoma (USC) and endometrioid endometrial carcinoma (EEC) are distinct subtypes of endometrial cancer with markedly different prognoses and management strategies. Accurate preoperative differentiation between USC and EEC is of great significance for tailoring surgical planning and adjuvant therapy.

Purpose: To develop and validate a multiparametric MRI-based radiomics and deep learning (DL) model for preoperative distinguishing USC from EEC.

Methods: A total of 210 patients (68 USCs and 142 EECs) from four hospitals who underwent preoperative MRI were enrolled in this retrospective study. Features from radiomics and deep learning were extracted using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhanced MRI (CE-MRI). The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Clinical-radiological characteristics, radiomics and DL features were constructed using a support vector machine (SVM) algorithm. The models were evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results: The all-combined model of clinical-radiological characteristics, radiomics and DL features showed better discrimination ability than either alone. The all-combined model demonstrated superior classification performance, achieving an AUC of 0.957 (95% CI: 0.904-1.000) on the internal-testing set and an AUC of 0.880 (95% CI: 0.800-0.961) on the external-testing set. The DLR model demonstrated superior predictive performance compared to the clinical-radiological model, although the differences were not statistically significant in both the internal-testing set (AUC = 0.908 vs. 0.861, p = 0.504) and the external-testing set (AUC = 0.767 vs. 0.700, p = 0.499). The DCA revealed that the all-combined model illustrated the best overall net benefit in clinical application.

Conclusion: The integrated model, combining multiparametric MRI-based radiomics, deep learning features, and clinical-radiological characteristics, may be utilized for the preoperative differentiation of USC from EEC.

Keywords: deep learning; endometrial cancer; magnetic resonance imaging; radiomics; uterine serous carcinoma.

Figure 1

Flowchart of patient recruitment.

Figure 2

Workflow of model development. CA125, carbohydrate antigen 125; HE4, Human Epididymis Protein 4; ET/AP ratio, ratios of endometrial thickness to the largest longitudinal and anteroposterior dimensions; LASSO, least absolute shrinkage and selection operator.

Figure 3

Patient predict scores output by the combined model in the training and testing sets (A–C). Receiver operation characteristic (ROC) curves of different models in the internal-testing set and external-testing set (D, E). The all-combined model had the best discriminating ability among seven models, with an area under the curve (AUC) of 0.957 in the internal-testing set and 0.880 in the external-testing set. Decision curve analysis (DCA) of the different models in the internal-testing set and external-testing set (F, G). The x-axis means the high-risk threshold, and the y-axis means clinic net benefit.

Figure 4

Visualization of the attention regions by the deep convolutional neural network of a 55-year-old patient who was confirmed EEC (A, B) and a 67-year-old patient who was confirmed USC (C, D). The red and yellow regions represent the areas with higher activation, whereas the blue and green regions represent the areas with lower activation.