Abstract
Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.
Highlights
Brain metastases are one of the most common neurological complications of extracranial cancer and account for more than half of all brain tumors [1]
This review summarizes the current status of radiomics in patients with brain metastases
The diagnostic accuracy of conventional FET positron emission tomography (PET) parameters was in the range of 81–83% and could be slightly increased to 85% when combined with textural features
Summary
Brain metastases are one of the most common neurological complications of extracranial cancer and account for more than half of all brain tumors [1]. Using machine learning techniques and advanced statistical methods, subsets of these imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. A cascaded system of single layer neural networks is trained to identify and learn relevant structures within the image data that are useful for classification without any prior definition or selection These complex structures are combined to generate features with a higher level of abstraction. Artificial neural networks strongly depend on the input data and usually require large amounts of image for the identification of robust and representative features which limits its applicability in neuro-oncological research, where the number of available datasets usually is small. Thereby, the amount of data necessary for training the network can be reduced since the network already has some prior knowledge about brain lesions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
