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The MRI-based 3D-ResNet101 deep learning model for predicting preoperative grading of gliomas: A multicenter study
LI Darui  HU Wanjun  LIU Guangyao  GAN Tiejun  MA Laiyang  ZHANG Jing 

Cite this article as: LI D R, HU W J, LIU G Y, et al. The MRI-based 3D-ResNet101 deep learning model for predicting preoperative grading of gliomas: A multicenter study[J]. Chin J Magn Reson Imaging, 2023, 14(5): 25-30. DOI:10.12015/issn.1674-8034.2023.05.006.


[Abstract] Objective The preoperative accurate and non-invasive prediction of glioma grading remains challenging. Developing a robust Residual Networks (ResNet) deep learning model based on conventional T2WI images to predict preoperative pathological grading of gliomas.Materials and Methods A retrospective analysis of preoperative T2WI images of 919 patients with pathologically confirmed glioma, of which 708 were data from patients enrolled at the Second Hospital of Lanzhou University from June 2014 to April 2021 and 211 were derived from The Cancer Imaging Archive (TCIA) database. The TCIA dataset was subdivided into a development set (n=135) and an independent test set (n=76). The data from the Second Hospital of Lanzhou University dataset and the TCIA development set were randomly split 7∶3 into a training set (n=590) and a test set (n=253) to construct a 3D-ResNet101 deep learning model based on T2WI images. After the training, the models were validated on the test set and independent test set, where the model efficacy was assessed by macro F1 scores, accuracy (ACC), and receiver operating characteristic (ROC) curves.Results The 3D-ResNet101 deep learning model constructed based on T2WI had ACCs of 99% and 95% in the training and test sets, respectively; The F1 scores were 99% and 95%, respectively; the area under the ROC curve (AUC) were 0.98 and 0.97, respectively; the ACC of the independent test set was 83%, the F1 score was 83%, and the AUC was 0.89.Conclusions The 3D-ResNet101 deep learning model based on T2WI images predicts high- and low-grade gliomas with high accuracy and robustness. The method can be used for the non-invasive prediction of preoperative glioma grading as well as helping to improve the effectiveness of clinical management of patients.
[Keywords] glioma；3D-Residual Networks；deep learning；magnetic resonance imaging；T2 weighted imaging

LI Darui1, 2, 3   HU Wanjun1, 2   LIU Guangyao1, 2   GAN Tiejun1, 2   MA Laiyang1, 2, 3   ZHANG Jing1, 2*  

1 Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China

3 Second Clinical School, Lanzhou University, Lanzhou 730030, China

Corresponding author: Zhang J, E-mail: lztong2001@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Gansu Health Industry Scientific Research Project (No. GSWSKY2020-68); Lanzhou University Second Hospital "Cuiying Science and Technology Innovation" Program (No. CY2021-BJ-A05).
Received  2023-01-04
Accepted  2023-05-06
DOI: 10.12015/issn.1674-8034.2023.05.006
Cite this article as: LI D R, HU W J, LIU G Y, et al. The MRI-based 3D-ResNet101 deep learning model for predicting preoperative grading of gliomas: A multicenter study[J]. Chin J Magn Reson Imaging, 2023, 14(5): 25-30. DOI:10.12015/issn.1674-8034.2023.05.006.

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