Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation:Radiopathomic mapping of glioma could improve standard of care by helping guide surgical resection and subsequent treatment. Most current methods for predicting tumor pathology using MRI neglect intra-tumoral heterogeneity.
Goal(s): We aim to use multi-parametric MRI and deep learning to spatially map pathology for treatment naïve glioma.
Approach: We utilized histopathologically analyzed tissue samples taken during surgical resection with known coordinates on pre-surgical multi-parametric MRI and semi-supervised ensemble networks.
Results: Our model classifies Ki-67 with an AUROC of 0.84 and 0.73 for combined Ki-67 and percent cancerous cells. Including physiologic MRI and pretraining on patches of unknown pathology improved performance.
Impact: We performed radiopathomic mapping in patients with newly-diagnosed glioma using presurgical physiological + anatomical MRI and semi-supervised ensemble networks and achieved AUROCs of 0.84 and 0.73 for Ki-67 and combined Ki-67 and % cancerous cells, respectively.
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