1. A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI
- Author
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Lyu, Qing, Namjoshi, Sanjeev V., McTyre, Emory, Topaloglu, Umit, Barcus, Richard, Chan, Michael D., Cramer, Christina K., Debinski, Waldemar, Gurcan, Metin N., Lesser, Glenn J., Lin, Hui-Kuan, Munden, Reginald F., Pasche, Boris C., Sai, Kiran Kumar Solingapuram, Strowd, Roy E., Tatter, Stephen B., Watabe, Kounosuke, Zhang, Wei, Wang, Ge, and Whitlow, Christopher T.
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Physics - Medical Physics - Abstract
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery 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. Ten-fold cross-validation generated overall AUC of 0.878 (95%CI:0.873,0.883), lung class AUC of 0.889 (95%CI:0.883,0.895), breast class AUC of 0.873 (95%CI:0.860,0.886), melanoma class AUC of 0.852 (95%CI:0.842,0.862), renal class AUC of 0.830 (95%CI:0.809,0.851), and other class AUC of 0.822 (95%CI:0.805,0.839). These data establish that whole-brain imaging features are discriminative 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.
- Published
- 2021