The diagnosis, prognosis, and management of patients with gliomas are largely dictated by the pathological analysis of tissue biopsied from a selected region within the lesion. However, due to the heterogeneous and infiltrative nature of gliomas, identifying the optimal region for biopsy with conventional magnetic resonance imaging (MRI) can be quite difficult. This is especially true for low grade gliomas, which often are non-enhancing tumors. To improve the management of patients with these tumors, the field of neuro-oncology requires an imaging modality that can specifically identify a tumor's most anaplastic/aggressive region(s) for biopsy targeting. The addition of metabolic mapping using spectroscopic MRI (sMRI) to supplement conventional MRI could improve biopsy targeting and, ultimately, diagnostic accuracy. Here, we describe a pipeline for the integration of state-of-the-art, high-resolution whole-brain 3D sMRI maps into a stereotactic neuronavigation system for guiding biopsies in gliomas with nonenhancing components. We also outline a machine-learning method for automated histology analysis that generates normalized, quantitative metrics describing tumor infiltration in immunohistochemically-stained tissue specimens. As a proof of concept, we describe the combination of these two techniques in a small cohort of grade III glioma patients. In this work, we aim to set forth a systematic pipeline to stimulate histopathology-image validation of advanced MRI techniques, such as sMRI.
Keywords: biopsy planning; grade III glioma; image-histology correlation; quantitative histological image analysis; spectroscopic MRI.