Radiomics analysis using stability selection supervised component analysis for right-censored survival data.

ABSTRACT: Radiomics is a newly emerging field that involves the extraction of massive quantitative features from biomedical images by using data-characterization algorithms. Distinctive imaging features identified from biomedical images can be used for prognosis and therapeutic response prediction, and they can provide a noninvasive approach for personalized therapy. So far, many of the published radiomics studies utilize existing out of the box algorithms to identify the prognostic markers from biomedical images that are not specific to radiomics data. To better utilize biomedical images, we propose a novel machine learning approach, stability selection supervised principal component analysis (SSSuperPCA) that identifies stable features from radiomics big data coupled with dimension reduction for r

SUBMITTER: Yan KK 

PROVIDER: S-EPMC7501167 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature