@article{4770cb73deb44eedbf48fcd2218f5eb4,
title = "A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images",
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.",
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",
author = "Qing Lyu and Namjoshi, {Sanjeev V.} and Emory McTyre and Umit Topaloglu and Richard Barcus and Chan, {Michael D.} and Cramer, {Christina K.} and Waldemar Debinski and Gurcan, {Metin N.} and Lesser, {Glenn J.} and Lin, {Hui Kuan} and Munden, {Reginald F.} and Pasche, {Boris C.} and Sai, {Kiran K.S.} and Strowd, {Roy E.} and Tatter, {Stephen B.} and Kounosuke Watabe and Wei Zhang and Ge Wang and Whitlow, {Christopher T.}",
note = "Funding Information: We would like to thank Nikita Namjoshi and Josh Tan (Department of Radiology, Wake Forest University) for comments and suggestions. We would also like to thank Drs. Guangxu Jin and Liang Liu (Wake Forest Baptist Comprehensive Cancer Center Bioinformatics Shared Resource, Wake Forest University) for their inputs. This work was supported by National Institutes of Health grants R01EB026646 , R01CA233888 , R01HL151561 , R21CA264772 , and R01EB031102 (G.W.), National Cancer Institute grants P01CA207206 and P30CA012197 (C.T.W.), and National Institutes of Health grants P01CA207206 and R01CA074145 (W.D.). Funding Information: We would like to thank Nikita Namjoshi and Josh Tan (Department of Radiology, Wake Forest University) for comments and suggestions. We would also like to thank Drs. Guangxu Jin and Liang Liu (Wake Forest Baptist Comprehensive Cancer Center Bioinformatics Shared Resource, Wake Forest University) for their inputs. This work was supported by National Institutes of Health grants R01EB026646, R01CA233888, R01HL151561, R21CA264772, and R01EB031102 (G.W.), National Cancer Institute grants P01CA207206 and P30CA012197 (C.T.W.), and National Institutes of Health grants P01CA207206 and R01CA074145 (W.D.). Conceptualization, C.T.W. G.W. and S.V.N.; methodology, Q.L. and S.V.N.; investigation, C.T.W. R.B. E.M. and U.T.; visualization, Q.L. S.V.N. G.W. and C.T.W.; funding acquisition, C.T.W. G.W. and W.D.; project administration, C.T.W. G.W. and W.Z.; supervision, C.T.W. G.W. and W.Z.; writing – original draft, Q.L. S.V.N. G.W. and C.T.W.; writing – review & editing, all authors. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
month = nov,
day = "11",
doi = "10.1016/j.patter.2022.100613",
language = "English (US)",
volume = "3",
journal = "Patterns",
issn = "2666-3899",
publisher = "Cell Press",
number = "11",
}