Cancer medicine · 2026
MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.
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Ding Sixuan, Meng Huan, Cui Xin, Yang Yunze, Xing Lihong, Zuo Ziwei, Liu Chong, Xue Linyan, Yin Xiaoping
Аннотация
OBJECTIVE: Various machine learning and deep learning methods were used to develop and validate radiomics models based on magnetic resonance imaging (MRI) to predict eligibility for conservative management of endometrial cancer.
METHODS: This retrospective study included 184 patients with early endometrial cancer confirmed by histopathology from two medical centers, who were divided into a training set (n = 123), an internal validation set (n = 31), and an external test set (n = 30). T2 weighted imaging (T2WI), dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) maps were used to extract radiomics features. A random forest model was used to select the most important features. Radiomics models were constructed using four machine learning methods including support vector machine (SVM), random forest, decision tree, logistic regression, and one deep learning method that is multilayer perceptron (MLP). They were validated using five-fold cross validation and tested on the external test set. The performance of the models was evaluated by receiver operating characteristic curves, calibration curves, and clinical decision curves.
RESULTS: The radiomics model established using MLP showed a certain performance in predicting eligibility for conservative management in early endometrial cancer patients. In the validation set, the area under the receiver operating characteristic curve (AUC) was 0.946 (95% CI 0.915-0.973). In the external test set, the area under the receiver operating characteristic curve (AUC) was 0.792 (95% CI 0.571-0.965). The calibration curve demonstrated good fit of the MLP model. The results of the DCA indicated that the MLP model had high net benefit in predicting eligibility for conservative management when the threshold probability ranged from 0.1 to 0.9.
CONCLUSION: The radiomics model based on MRI can be used to predict eligibility for conservative management in early endometrial cancer patients, providing objective imaging evidence for clinical decision-making.
METHODS: This retrospective study included 184 patients with early endometrial cancer confirmed by histopathology from two medical centers, who were divided into a training set (n = 123), an internal validation set (n = 31), and an external test set (n = 30). T2 weighted imaging (T2WI), dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) maps were used to extract radiomics features. A random forest model was used to select the most important features. Radiomics models were constructed using four machine learning methods including support vector machine (SVM), random forest, decision tree, logistic regression, and one deep learning method that is multilayer perceptron (MLP). They were validated using five-fold cross validation and tested on the external test set. The performance of the models was evaluated by receiver operating characteristic curves, calibration curves, and clinical decision curves.
RESULTS: The radiomics model established using MLP showed a certain performance in predicting eligibility for conservative management in early endometrial cancer patients. In the validation set, the area under the receiver operating characteristic curve (AUC) was 0.946 (95% CI 0.915-0.973). In the external test set, the area under the receiver operating characteristic curve (AUC) was 0.792 (95% CI 0.571-0.965). The calibration curve demonstrated good fit of the MLP model. The results of the DCA indicated that the MLP model had high net benefit in predicting eligibility for conservative management when the threshold probability ranged from 0.1 to 0.9.
CONCLUSION: The radiomics model based on MRI can be used to predict eligibility for conservative management in early endometrial cancer patients, providing objective imaging evidence for clinical decision-making.