  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.
