Imaging of intratumoral heterogeneity in high-grade glioma

• Published in: Cancer letters 2020-05, Vol.477, p.97-106
• Main Authors: Hu, Leland S., Hawkins-Daarud, Andrea, Wang, Lujia, Li, Jing, Swanson, Kristin R.
• Format: Article
• Language: English
• Subjects: Advanced; Algorithms; Biopsy; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Brain Neoplasms - pathology; Brain Neoplasms - therapy; Contrast Media; Dosimetry; Edema; Glioblastoma; Glioma; Glioma - diagnostic imaging; Glioma - genetics; Glioma - pathology; Glioma - therapy; Heterogeneity; Histologic; Humans; Imaging; Intratumoral; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical prognosis; MRI; Neoplasm Recurrence, Local - diagnostic imaging; Positron emission tomography; Radiation therapy; Radiogenomics; Spectrum analysis; Subpopulations; Therapeutic applications; Therapy, Computer-Assisted; Tumors
• ISSN: 0304-3835; 1872-7980
• DOI: 10.1016/j.canlet.2020.02.025

High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity. •HGGs like GBM can exhibit profound intratumoral heterogeneity that confounds accurate diagnosis and effective therapy.•DSC Perfusion can help distinguish high-grade from low-grade components within otherwise non-specific non-enhancing gliomas.•DSC Perfusion can distinguish HGG tumor recurrence from post-treatment effects and help quantify recurrent tumor burden.•PET imaging and ML models with multi-parametric MRI can resolve the regional heterogeneity of GBM tumor density and extent.•Radiogenomics models can resolve the intratumoral genetic heterogeneity of GBM to guide personalized therapeutic paradigms.