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Treatment decisions for brain metastatic disease rely on knowledge of theprimary organ site, and currently made with biopsy and histology. Here wedevelop a novel deep learning approach for accurate non-invasive digitalhistology with whole-brain MRI data. Our IRB-approved single-site retrospectivestudy was comprised of patients (n=1,399) referred for MRI treatment-planningand gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted andT2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) werepreprocessed and input to the proposed deep learning workflow for tumorsegmentation, modality transfer, and primary site classification into one offive 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 classAUC 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 otherclass AUC of 0.822 (95%CI:0.805,0.839). These data establish that whole-brainimaging features are discriminative to allow accurate diagnosis of the primaryorgan site of malignancy. Our end-to-end deep radiomic approach has greatpotential for classifying metastatic tumor types from whole-brain MRI images.Further refinement may offer an invaluable clinical tool to expedite primarycancer site identification for precision treatment and improved outcomes.

A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI