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Prediction of distant metastasis in nasopharyngeal carcinoma by interpretable machine learning model based on multiparametric MRI radiomics and clinical factors
JIN Zhe  ZHANG Bin  ZHANG Lu  ZHANG Shuixing 

Cite this article as: Jin Z, Zhang B, Zhang L, et al. Prediction of distant metastasis in nasopharyngeal carcinoma by interpretable machine learning model based on multiparametric MRI radiomics and clinical factors[J]. Chin J Magn Reson Imaging, 2022, 13(11): 22-29. DOI:10.12015/issn.1674-8034.2022.11.005.

[Abstract] Objective To establish a machine learning prediction model based on multi-parametric MRI features and clinical variables, and evaluate its efficacy in predicting distant metastasis in nasopharyngeal carcinoma (NPC) before treatment.Materials and Methods MRI images of 1393 patients with pathologically confirmed NPC from three hospitals (1049 in the training cohort and 344 in the external validation cohort) from June 2010 to September 2017 were retrospectively analyzed. We used ITK-SNAP and Pyradiomics to delineate regions of interest and extract radiomic features, respectively. Features were selected using correlation analysis, univariate analysis and recursive feature elimination (RFE) method. The gradient boosting machine (GBM) algorithm was utilized to construct models. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to compare the predictive efficacy of the models, and decision curve analysis (DCA) was used to assess the clinical utility. The SHapley Additive exPlanation (SHAP) algorithm was used to attribute interpretability to the optimal prediction model.Results Ten radiomic features were finally selected. GBM_R, GBM_C and GBM_RC models were constructed based on the three features combination: radiomic features, clinical variables, and radiomic features + clinical variables. The AUC values of the them on the training set were 0.938, 0.724, and 0.938, respectively. GBM_RC (hereafter, NPC-Wise) achieved the highest AUC value of 0.775 in the external validation set. The SHAP force plot provided a visualization of the direction and degree of influence of each feature on the predicting results of the model.Conclusions The interpretable machine learning prediction model NPC-Wise, based on multi-parametric MRI radiomic features and clinical variables, showed good performance in predicting the risk of distant metastasis in NPC, as well as providing individual-level interpretability with the SHAP algorithm, which can provide a valuable decision basis for personalized treatment.
[Keywords] nasopharyngeal carcinoma;distant metastasis;radiomics;Gradient Boosting Machine;interpretability;magnetic resonance imaging

JIN Zhe   ZHANG Bin   ZHANG Lu   ZHANG Shuixing*  

Department of Radiology, the First Affiliated Hospital of Jinan University, Guangzhou 510627, China

Zhang SX, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81871323).
Received  2022-08-02
Accepted  2022-11-14
DOI: 10.12015/issn.1674-8034.2022.11.005
Cite this article as: Jin Z, Zhang B, Zhang L, et al. Prediction of distant metastasis in nasopharyngeal carcinoma by interpretable machine learning model based on multiparametric MRI radiomics and clinical factors[J]. Chin J Magn Reson Imaging, 2022, 13(11): 22-29. DOI:10.12015/issn.1674-8034.2022.11.005.

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