Abstract
This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.
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Data Availability
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to patient confidentiality.
Abbreviations
- DL:
-
Deep learning
- GBM:
-
Glioblastoma
- BM:
-
Brain metastasis
- MRI:
-
Magnetic resonance imaging
- DWI:
-
Diffusion-weighted imaging
- ResNet:
-
Residual network
- T model:
-
Deep learning model in tumoral regions
- T&P model:
-
Deep learning model in tumoral + peritumoral regions
- AUC:
-
Area under the receiver operating characteristic curve
- T2WI:
-
T2-weighted imaging
- CE-T1WI:
-
Contrast-enhanced T1-weighted imaging
- T2-FLAIR:
-
T2-fluid-attenuated inversion recovery
- cMRI:
-
Conventional MRI
- CNN:
-
Convolutional neural network
- ROI:
-
Region of interest
- Grad-CAM:
-
Gradient-weighted class activation mapping
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Qingqing Yan and Qingshi Zeng contributed to the study’s conception and design. Material preparation and data collection were performed by Wenjing Jia, Yuting Li, Huan Chang, Fuyan Li, Yi Cui, Xiao Wang, and Yong Wang, and data analysis was performed by Feng Shi, Yuwei Xia, Qing Zhou, and Qingqing Yan. The first draft of the manuscript was written by Qingqing Yan and Qingshi Zeng, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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This retrospective study was approved by the local Institutional Review Board, and a waiver of informed consent was made.
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Yan, Q., Li, F., Cui, Y. et al. Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm. J Digit Imaging 36, 1480–1488 (2023). https://doi.org/10.1007/s10278-023-00838-5
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DOI: https://doi.org/10.1007/s10278-023-00838-5