Journal of Shandong University (Health Sciences) ›› 2021, Vol. 59 ›› Issue (11): 53-60.doi: 10.6040/j.issn.1671-7554.0.2021.0997

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Value of MRI radiomics of glioma and peritumoral edema in evaluating tumor recurrence

SONG Zhenzhen1,2, SUN Xiaoling3, LI Haiou1, WANG Fang1, ZHANG Ran4, YU Dexin1   

  1. 1. Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Department of Radiology, The Third Hospital of Jinan, Jinan 250132, Shandong, China;
    3. Department of Ultrasound, Wendeng District Peoples Hospital, Weihai 264400, Shandong, China;
    4. Huiying Medical Technology(Beijing)Co., Ltd., Beijing 100192, China
  • Published:2021-11-11

Abstract: Objective To explore the value of MRI radiomics of glioma and peritumoral edema(PTE)in evaluating the postoperative recurrence. Methods A total of 120 patients with glioma confirmed by postoperative pathology during Jan. 2013 and Dec. 2020 were retrospectively selected, including 55 cases with recurrence and 65 cases without recurrence. The tumor and PTE were delineated by three-dimensional volumetric regions of interest based on the preoperative T2WI and contrast enhanced T1WI images, which were divided into training group and validation group according to the ratio 8∶2. The relationship between tumor recurrence and the radiomic characteristics was analyzed. The receiver operating characteristic(ROC)curve was drawn, and the area under curve(AUC)and accuracy matrix were used to compare and evaluate the results of different radiomic models. Results For PTE, KNN classifier had the best prediction performance(AUC=0.910, sensitivity=0.84, specificity=0.88), while in the validation group, the AUC, sensitivity, and specificity were 0.916, 0.82 and 0.93, respectively. For tumor, LR classifier had the best prediction performance: the AUC, sensitivity and specificity of the training group were 0.777, 0.69 and 0.67, respectively, while in the validation group, they were 0.758, 0.82 and 0.92, respectively. In the model of tumor connected with PTE, LR classifier had the best prediction performance: the AUC, sensitivity and specificity of the training group were 0.977, 0.88 and 0.89, respectively, while in the validation group, they were 0.841, 0.73 and 0.83, respectively. Conclusion The MRI radiomic features of PTE and glioma are valuable to predict postoperative recurrence, and the KNN model of PTE has the best diagnostic efficacy.

Key words: Radiomics, Magnetic resonance imaging, Glioma, Peripheral edema, Postoperative recurrence

CLC Number: 

  • R455.2
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