Volume 21 Issue 1
Jan.  2023
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Article Navigation >  Chinese Journal of General Practice  > 2023  >  21(1): 108-111
XU Zu-liang, SHENG Liang, HE Xia-xia, WANG Yan-na, WANG Guo-yu. Multi-sequence MRI image data-based machine learning model in the diagnosis of benign and malignant parotid gland tumours[J]. Chinese Journal of General Practice, 2023, 21(1): 108-111. doi: 10.16766/j.cnki.issn.1674-4152.002824
Citation: XU Zu-liang, SHENG Liang, HE Xia-xia, WANG Yan-na, WANG Guo-yu. Multi-sequence MRI image data-based machine learning model in the diagnosis of benign and malignant parotid gland tumours[J]. Chinese Journal of General Practice, 2023, 21(1): 108-111. doi: 10.16766/j.cnki.issn.1674-4152.002824
shu

Multi-sequence MRI image data-based machine learning model in the diagnosis of benign and malignant parotid gland tumours

doi: 10.16766/j.cnki.issn.1674-4152.002824
  • 1.

    Department of Radiology, Taizhou Central Hospital, Taizhou, Zhejiang 318000, China

Funds:

 2021KY396

  • Received Date: 2022-07-12
    Available Online: 2023-04-07
    Abstract
  •   Objective  The clinical value of multi-sequence MRI image data-based radiomics in the diagnosed of benign and malignant parotid gland tumors.  Methods  A total of 97 patients with parotid gland tumours diagnosed by pathological examination in Taizhou Central Hospital from January 1, 2021 to May 30, 2022 were selected, including 64 benign tumours and 33 malignant tumours. Meantime, patients' clinical data and MRI images were extracted. ITK-SNAP was used to segment regions of interest. In addition, PyRadiomics plugin of 3D-Slicer was used to extract 120 image features from T1-weighted contrast enhancement images (T1Wce), T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) images that were constructed on the basis of diffusion-weighted imaging (DWI) sequence. Then, Lasso regression was used for the reduction of image features. Finally, a support vector machine model (SVM) was constructed using the selected image features. Then, the ROC curve was drawn to evaluate the diagnostic efficiency of each model.  Results  No significant differences in age and gender ratio was observed between the two groups. Four imaging models were constructed, including the models based on T1Wce, T2WI, ADC images and the combination of the three sequences. The AUC was as follows: T1Wce model 0.752, T2WI model 0.776, ADC model 0.810, T1Wce+T2WI+ADC model 0.897. The AUC of the three-sequence combination model was significantly higher than that of the single-sequence model.  Conclusion  The radiomics model based on MRI image data can be effectively used for assisting the diagnosis of benign and malignant parotid gland tumours. Moreover, the model based on the combination of T1Wce, T2WI and ADC images has the best diagnostic efficiency.

     

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