Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A transformer-based deep learning approach for classifying brain
  metastases into primary organ sites using clinical whole brain MRI   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.

32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci