Based on the imaging techniques and incorporated with computer vision and machine learning ( 2), radiomics was born. These tumors stand out for their high diversity and heterogeneity, along with dismal prognosis, ranking them among the top 10 causes of cancer deaths, accounting for a significant proportion of the deaths in men less than 40 years and women less than 20 years in the United States in 2018 ( 1), and it is estimated that they will cause 18,600 deaths in 2021.Ĭlinical radiology is a routinely performed examination for patients who are suspicious of brain or other CNS tumors recently more and more sophisticated analytic methods have sprung up which supplement traditional imaging techniques. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.īrain and other CNS tumors, including gliomas, pituitary tumors, and others such as brain metasteses, mainly occur in lung cancer and breast cancer patients. Recent studies have shown that 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.
Kevin renderman brain tumor full#
Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor’s histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Imaging diagnosis is crucial for early detection and monitoring of brain tumors.
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