No abstract

According to the World Health Organization, cancer is the second leading cause of death in the world. The myriad of variations, paths of development, and mutations make this abnormality challenging to treat. With the advent of medical imaging, complex qualitative information is collected with the aim of characterizing the pathology; however, the uncomfortable and time-consuming histology remains the state of care within hospitals. This manuscript presents a strategy to extract quantifiable features from the images. The method captures shape perturbation as variations in reference to a perfect circle that is used in a standardized dimensional space. A multifeatured scheme is created when the quantification is applied in all slices produced by imaging modalities such as computed tomography, magnetic resonance imaging, and tomosynthesis. Later, the numbers obtained by the introduced algorithm are used in an artificial intelligence pipeline that correlates spicularity with aggressiveness using the histology as supervising factor.