Article ; Online: A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images.
Patterns (New York, N.Y.)
2022 Volume 3, Issue 11, Page(s) 100613
Abstract: ... enhanced T1-weighted and fast spoiled gradient echo brain MRI exams (n = 1,582) were preprocessed and input ...
Abstract | Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site and are currently made with biopsy and histology. Here, we develop a deep-learning approach for accurate non-invasive digital histology with whole-brain magnetic resonance imaging (MRI) data. Contrast-enhanced T1-weighted and fast spoiled gradient echo brain MRI exams (n = 1,582) were preprocessed and input to the proposed deep-learning workflow for tumor segmentation, modality transfer, and primary site classification into one of five classes. Tenfold cross-validation generated an overall area under the receiver operating characteristic curve (AUC) of 0.878 (95% confidence interval [CI]: 0.873,0.883). These data establish that whole-brain imaging features are discriminative enough to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep radiomic approach has great potential for classifying metastatic tumor types from whole-brain MRI images. Further refinement may offer an invaluable clinical tool to expedite primary cancer site identification for precision treatment and improved outcomes. |
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Language | English |
Publishing date | 2022-10-27 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 2666-3899 |
ISSN (online) | 2666-3899 |
DOI | 10.1016/j.patter.2022.100613 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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