Author/Authors :
Tsougos, Ioannis Medical Physics Department - Medical School - University of Thessaly - Larissa, Greece , Vamvakas, Alexandros Medical Physics Department - Medical School - University of Thessaly - Larissa, Greece , Kappas, Constantin Medical Physics Department - Medical School - University of Thessaly - Larissa, Greece , Fezoulidis, Ioannis Radiology Department - Medical School - University of Thessaly - Larissa, Greece , Vassiou, Katerina Radiology Department - Medical School - University of Thessaly - Larissa, Greece
Abstract :
Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of
numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the
combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics
that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which
facilitate the monitoring of treatment response, as well as the prediction of patient’s outcome, by means of unravelling of the
complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. *e scope of this review is to provide
applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis
and prognosis.
