Kim, AlbertBridge, ChristopherLin, John2026-05-1920262026-05-152026Lin, John. 2026. Non-invasive identification of clinically actionable gene fusions in gliomas using deep learning-enabled analysis of magnetic resonance imaging scans. Masters Thesis, Harvard Medical School.32699950https://dash.harvard.edu/handle/1/42738363Gliomas are the most common primary malignant brain tumors in adults, with prognosis varying substantially across molecular subtypes. Oncogenic gene fusions have recently emerged as a promising class of actionable therapeutic targets, yet their detection relies on invasive tissue sampling via biopsy or surgical resection. Here we present a deep learning framework that leverages magnetic resonance imaging (MRI) to non-invasively predict gene fusion status in gliomas. Using contrastive self-supervised learning, we pretrained a foundation model on a diverse cohort of 30,270 brain MRI images from 10,336 patients across multiple institutions and tumor types. Validation on brain tumor type classification showed that the model learned meaningful representations (AUC: 0.84), outperforming existing foundation models. Application to gene fusion status classification, however, yielded more limited discrimination (AUC: 0.54), suggesting that gene fusions may manifest as comparatively subtle or heterogeneous MRI patterns. Our findings demonstrate that foundation models can learn transferable representations across diverse neuroimaging tasks, although non-invasive identification of gene fusions in gliomas remains an open challenge.application/pdfenDeep learningFoundation modelGene fusionGliomaMagnetic resonance imagingBioinformaticsThesis or Dissertation2026-05-190000-0001-7365-0926