Predictive value of multi-sequence MRI radiomics fusion model for glioma grading(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第5期
- Page:
- 545-549
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Predictive value of multi-sequence MRI radiomics fusion model for glioma grading
- Author(s):
- YANG Rihui1; 2; FAN Weixiong2; DONG Ting3; JIANG Guihua1; 3
- 1. Guangdong Medical University, Zhanjiang 524023, China 2. Meizhou Peoples Hospital (Meizhou Academy of Medical Sciences), Meizhou 514031, China 3. Department of Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, China
- Keywords:
- glioma pathological grade radiomics fusion model
- PACS:
- R742；R816.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.05.003
- Abstract:
- glioma grading. Methods The data of glioma patients who were pathologically confirmed and underwent MR examination in Meizhou Peoples Hospital from January 2016 to June 2021 were retrospectively analyzed. The original multi-sequence MR images in DICOM format were imported into ITK-SNAP software for VOI delineation. After extracting radiomics signatures with GE A.K analysis software, ANOVA+Mann Whitney, Spearman correlation analysis and LASSO model were used for feature screening. Logistic regression (LR) algorithm was selected to build a single sequence model, while LR, LDA and SVM were adopted to establish fusion sequence models. The prediction performances of different models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. Results A total of 150 patients were enrolled in the study, including 41 in low-grade glioma group and 109 in high-grade glioma group. There was no significant difference in gender and age between LGG group and HGG group (P>0.05). Five optimal feature sets were obtained, and there were 15, 31, 25, 12, 4 and 44 features in T1WI feature set, T2WI feature set, Flair feature set, DWI feature set, T1C, and fusion sequence set. The AUC of MRI radiomics-based prediction models of T1WI, T2WI, Flair, DWI and T1C was 0.719 0, 0.769 5, 0.741 0, 0.721 9 and 0.815 7 on the training set, and 0.651 4, 0.711 4, 0.610 2, 0.747 0 and 0.754 5 on the test set, respectively. The AUC of the fusion models established with LR, LDA and SVM was 0.952 4, 0.894 8 and 0.928 6 on the training set, and 0.767 0, 0.688 1 and 0.704 5 on the test set, respectively. Conclusion Among the single sequence models based on multi-sequence MRI radiomics, T1C single sequence prediction model has the highest efficiency in glioma grading. Compared with single sequence prediction model, multi-sequence fusion prediction model has higher diagnostic efficiency, and the fusion model established with LR shows higher prediction efficiency than the fusion models established with LDA and SVM.
Last Update: 2023-05-26