Keywords: Diagnosis/Prediction, Cancer, 3D convolutional neural network, glioblastoma, solitary brain metastasis, magnetic resonance imaging, automatic segmentation and classification
Motivation: Differentiating between glioblastoma (GBM) and solitary brain metastasis (SBM) before surgery is challenging yet essential for clinical decisions.
Goal(s): To construct a three-dimensional (3D) deep learning (DL) model for the automated segmentation and classification of GBM and SBM based on multiparametric MRI.
Approach: 314 patients from multiple medical centers were recruited. Tumor segmentation was performed using no-new-UNet, followed by the development and comparison of 3D DL, 2D DL, and radiomics models for classification. Additionally, three radiologists with varying experience levels conducted a two-round classification analysis, with and without a 3D DL model as a reference.
Results: 3D DL model showed the best performance.
Impact: Reading with 3D DL model improves the diagnostic accuracy of radiologists, thereby enhancing patient treatment and prognosis potentially. Considering its promising performance, it is recommended for routine clinical application.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords