Current Advances and Challenges in Radiomics of Brain Tumors - Yi, Zhenjie; Long, Lifu; Zeng, Yu; Liu, Zhixiong (Frontiers in Oncology, 2021)
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics refer to extracting large mass of quantitative features from complex clinical imaging arrays, then transforming them into high-dimensional data which can subsequently be mined to find their relevance with tumor's histological features, progression, grade, or even overall survival. Radiogenomics are to combine radiology with genomic to detect the correlations between gene expression and radiomic features. Compared to traditional brain imaging, radiomics provide quantitative information linked to meaningful biologic characteristics and application of deep learning in which shed light on full automation of imaging diagnosis. Recent studies have shown radiomics’ application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas and brain metastases. Besides clinical applications, we further discuss the current limitations along with future development of radiomics along with deep learning.