Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images.
In: Frontiers in Neuroscience, 2024-03-28, S. 1-8
Online
academicJournal
Zugriff:
Background and objectives: Glioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging. Therefore, this study explores an improved deep learning algorithm to assist in the differentiation of GBM and MET. Materials and methods: For this study, axial contrast-enhanced T1 weight (ceT1W) MRI images from 321 cases of high-grade gliomas and solitary brain metastasis were collected. Among these, 251 out of 270 cases were selected for the experimental dataset (127 glioblastomas and 124 metastases), 207 cases were chosen as the training dataset, and 44 cases as the testing dataset. We designed a new deep learning algorithm called SCAT-inception (Spatial Convolutional Attention inception) and used five-fold cross-validation to verify the results. Results: By employing the newly designed SCAT-inception model to predict glioblastomas and brain metastasis, the prediction accuracy reached 92.3%, and the sensitivity and specificity reached 93.5 and 91.1%, respectively. On the external testing dataset, our model achieved an accuracy of 91.5%, which surpasses other model performances such as VGG, UNet, and GoogLeNet. Conclusion: This study demonstrated that the SCAT-inception architecture could extract more subtle features from ceT1W images, provide state-of-the-art performance in the differentiation of GBM and MET, and surpass most existing approaches. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images.
|
---|---|
Autor/in / Beteiligte Person: | Lv, Cheng ; Shu, Xu-Jun ; Chang, Hui ; Qiu, Jun ; Peng, Shuo ; Yu, Keping ; Chen, Sheng-Bo ; Rao, Hong |
Link: | |
Zeitschrift: | Frontiers in Neuroscience, 2024-03-28, S. 1-8 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 1662-4548 (print) |
DOI: | 10.3389/fnins.2024.1349781 |
Schlagwort: |
|
Sonstiges: |
|