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• Clinical Article •
The value of machine-learning-based radiomics models for predicting disease-free survival and immune levels in endometrial cancer patients
CHEN Shuqing  ZHANG Yu  CHEN Dong  CHANG Xibao  LIU Jingjing  CHEN Lei  QIAN Yinfeng 

Cite this article as: CHEN S Q, ZHANG Y, CHEN D, et al. The value of machine-learning-based radiomics models for predicting disease-free survival and immune levels in endometrial cancer patients[J]. Chin J Magn Reson Imaging, 2024, 15(9): 107-113. DOI:10.12015/issn.1674-8034.2024.09.018.


[Abstract] Objective To investigate the predictive value of machine learning-based radiomics model for disease-free survival (DFS) in endometrial cancer patients. Matirials andMethods Data from 212 endometrial cancer patients who had undergone radical surgery in a dual-center were retrospectively analyzed. Radiomics features of tumor and peri-tumor 5 mm region in T2WI sequences were extracted for all patients. Five machine learning methods (gradient boosting machines, the least absolute shrinkage and selection operator, random survival forest, support vector machine, and extreme gradient boosting) were used to construct the radiomics model and calculate the best radiomics score (Radscore). The incremental value of Radscore to existing clinical predictors was analysed and a combined model was constructed. Finally, bioinformatics analysis was used to reveal the biological mechanisms of the radiomics models.Results The combined radiomics model based on gradient boosting machines showed the best predictive efficacy, with AUC of 0.977, 0.986, 0.995 and 0.745, 0.764, 0.802 for predicting 1-, 3-, and 5-year DFS in the training and validation sets, respectively. Multifactorial Cox regression analyses showed that clinical stage, carbohydrate antigen 125 (CA125), and Radscore were the independent predictors of DFS. The area under the curve (AUC) of the combined model in the training and validation sets were 0.926, 0.894, 0.864 and 0.828, 0.839, 0.873 for predicting 1-, 3-, and 5-year DFS. Meanwhile, bioinformatics analysis suggested that Radscore was significantly correlated with the immune level of endometrial cancer patients.Conclusions Machine learning-based radiomics model is helpful for the prediction of DFS and immune levels in endometrial cancer patients. The combination of radiomics and clinical indicators can further improve the accuracy of prediction and provide a reference basis for prognostic prediction and individualized treatment of endometrial cancer patients.
[Keywords] endometrial cancer；magnetic resonance imaging；radiomics；machine learning；disease-free survival

CHEN Shuqing1   ZHANG Yu2   CHEN Dong2   CHANG Xibao1   LIU Jingjing1   CHEN Lei1   QIAN Yinfeng3*  

1 Department of Imaging, Funan Hospital Affiliated Fuyang Normal University Medical College, Fuyang 236300, China

2 Department of Imaging, the First Affiliated Hospital, University of Science and Technology of China, Hefei 230001, China

3 Department of magnetic resonance, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China

Corresponding author: QIAN Y F, E-mail: 894206876@qq.com

Conflicts of interest   None.

Received  2024-04-28
Accepted  2024-08-26
DOI: 10.12015/issn.1674-8034.2024.09.018
Cite this article as: CHEN S Q, ZHANG Y, CHEN D, et al. The value of machine-learning-based radiomics models for predicting disease-free survival and immune levels in endometrial cancer patients[J]. Chin J Magn Reson Imaging, 2024, 15(9): 107-113. DOI:10.12015/issn.1674-8034.2024.09.018.

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