Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics.

ABSTRACT: Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learnin

SUBMITTER: Rodrigues A 

PROVIDER: S-EPMC8657292 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature