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• Clinical Article •
The value of machine learning model for predicting prostate cancer bone metastases based on MRI radiomics
LI Kejian  ZHANG Juntao  REN Kaixuan  FANG Caiyun  SHANG Hui  JIAO Tianyu  ZENG Qingshi 

Cite this article as: LI K J, ZHANG J T, REN K X, et al. The value of machine learning model for predicting prostate cancer bone metastases based on MRI radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(1): 100-104, 115. DOI:10.12015/issn.1674-8034.2023.01.018.


[Abstract] Objective To develop and validate MRI-based machine learning models for predicting bone metastases in patients with prostate cancer (PCa).Materials and Methods The clinical and MRI data of 150 patients with pathologically confirmed PCa in the First Affiliated Hospital and Affiliated Provincial Hospital of Shandong First Medical University were retrospectively obtained from January 2018 to January 2022. According to the ratio of 7∶3, the samples were randomly divided into training set (n=105) and testing set (n=45). The apparent diffusion coefficient (ADC) and fat saturated T2 weighted imaging (FS-T2WI) of each patient were manually outlined for the tumor's region of interest and extracted for imaging histological features, respectively. Dimension reduction and feature selection were performed using intra-class correlation coefficients (ICC), feature importance and minimal-redundancy-maximal-relevance (mRMR). The filtered features were used to establish radiomics models using generalized linear model (GLM), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) methods. The models were evaluated using the following metrics: area under the curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis and Delong test.Results Seventeen features were selected and models were constructed using GLM, XGB, SVM and RF. In the training set, the mean AUC were 0.714, 0.845, 0.768 and 0.858, respectively. In the testing set, the AUC were 0.796, 0.729, 0.755 and 0.765, respectively. Calibration curve and Delong test indicated no significant differences between the four models. Decision curve analysis showed that the four models had similar clinical applications.Conclusions The MRI-based radiomics features allowed GLM, SVM, XGB and RF classifiers to be used as a promising tool for predicting bone metastases in PCa patients, providing potentially valid information for clinical management.
[Keywords] prostate cancer；bone metastasis；magnetic resonance imaging；radiomics；prediction

LI Kejian1, 2   ZHANG Juntao3   REN Kaixuan4   FANG Caiyun1, 2   SHANG Hui1, 2   JIAO Tianyu1, 2   ZENG Qingshi1*  

1 Department of Radiology, Shandong Provincial Qianfoshan Hospital, the First Hospital Affiliated Hospital of Shandong First Medical University, Jinan 250014, China

2 Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 271016, China

3 GE Healthcare Shanghai Co., Ltd., Shanghai 210000, China

4 Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250022, China

Corresponding author: Zeng QS, E-mail: zengqingshi@sina.com

Conflicts of interest   None.

Received  2022-08-02
Accepted  2022-12-05
DOI: 10.12015/issn.1674-8034.2023.01.018
Cite this article as: LI K J, ZHANG J T, REN K X, et al. The value of machine learning model for predicting prostate cancer bone metastases based on MRI radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(1): 100-104, 115. DOI:10.12015/issn.1674-8034.2023.01.018.

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