Radiomics in neuro-oncology: Basics, workflow, and applications
Introduction
The diagnosis of brain cancer is predominantly based on neuroimaging findings, and, ultimately, the histomolecular evaluation of tissue samples obtained from tumor resection or biopsy. For decades, mostly anatomical neuroimaging techniques such as contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) have been used for brain tumor diagnostics, treatment planning, and follow-up. More recently, the increasing number of additional imaging parameters primarily derived from advanced MRI and amino acid PET, as well as technical developments such as the event of hybrid PET/CT and PET/MRI scanners, generate a large amount of complex neuroimaging data in patients with brain tumors.
A timely evaluation of this amount of diagnostic information that potentially can be implemented in clinical routine is costly and hardly feasible without considerable computer support. Here, methods from the emerging field of artificial intelligence (AI) offer new options to support clinicians further. In particular, AI provides the possibilities to partially or fully automate various steps within the diagnostic routine, so that especially time-consuming processes such as the manual detection and segmentation of lesions are performed by a computer and require only a final validation by a clinician. Furthermore, the speed of image processing and analysis can be enhanced using AI-based methods, thereby increasing productivity. Besides, the automated AI-based analysis of imaging data may help to increase the comparability of the obtained results as it is independent of the experience level of the evaluating clinician.
Moreover, AI offers the potential to extract yet undiscovered features from routinely acquired images. Specifically, quantitative and semi-quantitative image features can be extracted from routinely acquired neuroimaging data, which are usually beyond human perception. Finally, subsets of these image features, combined with patient information such as survival data, molecular markers, or genomics, can be used to develop mathematical models that characterize the underlying brain tumor biology. Subsequently, these models can be used for essential clinical questions, e.g., the assessment of prognosis or treatment response, as well as the noninvasive diagnosis of molecular markers. The computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is summarized under the term radiomics [1], [2], [3], [4], [5], a specialized application within the broad field of AI.
This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.
Section snippets
Radiomics
“Images are more than pictures, they are data”. This intuitive and precise definition by Robert Gillies and colleagues nicely illustrates the basic idea of radiomics [3]. Radiomics can be subdivided into feature-based and deep learning-based radiomics and is usually applied to routinely acquired imaging data, thereby allowing additional data analysis at a low cost. Since radiomics features are either mathematically predefined (feature-based radiomics) or generated from the data by training
Applications of radiomics in Neuro-Oncology
Radiomics in patients with brain tumors is mainly based on the analysis of conventional MRI. Several studies have investigated the usefulness of radiomics for the differentiation of treatment-related changes from tumor progression in patients with gliomas and brain metastases, which is a clinical question of considerable importance. Furthermore, several studies have also evaluated radiomics for the classification as well as the molecular characterization of brain tumors, which is of high
Conclusions
In summary, feature-based and deep learning-based radiomics is increasingly evaluated in the field of neuro-oncology. Radiomics should be considered as an additional source of diagnostic information that, especially in combination with clinical, histopathological, molecular, and conventional imaging parameters, has a great potential to significantly improve the diagnostics and management of patients with brain tumors.
Notwithstanding, most studies lack further validation of the generated models
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