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

Qing Lyu, Sanjeev V. Namjoshi, Emory McTyre, Umit Topaloglu, Richard Barcus, Michael D. Chan, Christina K. Cramer, Waldemar Debinski, Metin N. Gurcan, Glenn J. Lesser, Hui Kuan Lin, Reginald F. Munden, Boris C. Pasche, Kiran K.S. Sai, Roy E. Strowd, Stephen B. Tatter, Kounosuke Watabe, Wei Zhang, Ge Wang, Christopher T. Whitlow

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number100613
JournalPatterns
Volume3
Issue number11
DOIs
StatePublished - Nov 11 2022

Keywords

  • brain metastasis
  • classification
  • deep learning
  • DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
  • MRI
  • primary organ site
  • vision transformer

ASJC Scopus subject areas

  • Decision Sciences(all)

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