Stereotactic radiotherapy (SRT) is current standard of care for brain
oligometastases. SRT is characterized by a high dose delivered in less than
five fractions and with a steep dose gradient to surrounding healthy brain
tissue. However, follow-up MRI is often ambiguous due to the frequent
presence of treatment-related changes as is radiation necrosis or
pseudoprogression. Importantly, a valid assessment of local control is
essential for indication of further systemic therapy as is modern targeted therapy or immunotherapy. The practice of medicine is changing with the
development of new Artificial Intelligence methods of machine learning.
Nowadays, radiology, as well as radiotherapy, strongly relies on the help of
computers, especially when dealing with large 3D image data. Finding
important image content that is often hidden to the naked eye is called
radiomics, which means extraction of image or object features (descriptors)
that quantitatively characterize the image or its parts in mathematical terms.
In conclusion, it is reasonably assumed that there will be a significant
increase of incidence of patients with brain metastases indicated to SRT.
Exact and valid evaluation of follow-up MRI after targeted SRT represents
an unmet clinical need which is increasingly important for the individual
patient in respect to increasing possibilities of modern systemic treatment of
disseminated disease. Moreover, due to the limited availability of advanced
MRI or PET/CT methods for brain imaging for further imaging of equivocal
findings on standard contrast enhanced structural MRI, the development of
other approaches is recommended. The aim of presented study is to develop
a radiomic model for the evaluation of contrast enhanced structural brain
MRI after SRT of brain metastases in terms of differential diagnosis of
radiotherapy-related changes and true progression in order to increase
sensitivity and specificity of MRI evaluation.

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