| Autor: |
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 |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Patterns. 3:100613 |
| ISSN: |
2666-3899 |
| DOI: |
10.1016/j.patter.2022.100613 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
