{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-transformer-based-deep-learning-approach","title":"A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI","arxiv_id":"2110.03588","date":"2021-10-07","proceeding":null,"authors":["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 Kumar Solingapuram Sai","Roy E. Strowd","Stephen B. Tatter","Kounosuke Watabe","Wei zhang","Ge Wang","Christopher T. Whitlow"],"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.","url_abs":"https://arxiv.org/abs/2110.03588v6","url_pdf":"https://arxiv.org/pdf/2110.03588v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-transformer-based-deep-learning-approach","repo_url":"https://github.com/qinglyu0828/whole-brain-mri_metastases_cls","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}