The integration of artificial intelligence (AI) into medicine has by far been the hottest topic at nearly all medical conferences for radiology, cardiology and several other subspecialties over the past two...
Radiomics is the study of information hidden in imaging exams that machine algorithms are trained to identify to help doctors more accurately diagnose patients, stage cancers, determine optimum therapies, predict patient outcomes or their risk level choose the radiation therapy dose level of risk. The field of medical study extracts large amounts of quantitative features from medical images using data characterization algorithms. These features, called radiomic features, may be able to uncover disease characteristics that fail to be appreciated by the naked eye. It is expected this field will be dominated by artificial intelligence software in the coming years.
Semi-automatic lesion identification: (A) Manual ROI indication. In blue, it is possible to observe the axes that cross the lesion manually delineated by the radiologist on a plane of the MPR. The intensity of the lesion boundary (estimated) is represented with a red outline. (B) Additional axes can be dragged onto other orthogonal MPR views. From left to right, it is possible to observe the initial long axis outlined by the radiologist and the 2D contours on the axial, coronal and sagittal views of the lesion used as a starting point for the RPM algorithms. (C) Resulting 3D contour of the lesion (in blue).
MRI scans of patients with radiation necrosis (above) and cancer recurrence (below) are shown in the left column. Close-ups in the center column show the regions are indistinguishable on routine scans. Radiomic descriptors unearth subtle differences showing radiation necrosis, in the upper right panel, has less heterogeneity, shown in blue, compared to cancer recurrence, in the lower right, which has a much higher degree of heterogeneity, shown in red. Credit: Pallavi Tiwari