Abstract
Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.
Highlights
Cancer Genome Atlas Program (TCGA) [93,94,95], Gene Expression Omnibus (GEO) [96], the growing presence of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [97] lies in both merging the acquisition of further data towards robust clinical endpoints as well as merging dose volume histogram and pattern of failure data present currently in distinct silos with exisiting results [29,98,99,100,101]
Advances in radiogenomics may well provide the needed evidence to allow for more personalised dose distributions, which are highly achievable with current technology
Increased emphasis needs to be placed on biologically optimising radiation therapy (RT) to improve outcomes with the solid backing of molecular science
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Neuro-oncology is comprised of malignancies that often carry poor prognoses and significant neurological sequelae as well as the potential for life altering acute and late effects [1]. These are tumors that tend to recur in the radiation field emphasizing the need to understand tumor radiosensitivity and resistance to treatment both of which cannot be robustly addressed by creating robust connections to molecular science. The focus was on specific clinical areas of controversy: management of low-grade gliomas (LGG), high grade gliomas (HGG), the specific scenario of the elderly patient with HGG as well as rare central nervous system tumors, craniospinal irradiation, and re-irradiation with a proposal of how computational analysis may drive personalised radiation therapy (RT) to optimise patient outcomes and merge traditional radiation planning concepts with improved molecular profiling
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