학술논문
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI
Document Type
Working Paper
Author
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; Whitlow, Christopher T.
Source
Subject
Language
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.