Deep learning radiomics in cancer imaging - AACR 2021 

This presentation was part of the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging - January 13-14 2021
Plenary session 2: learning from images: radiomics
www.aacr.org/m...
Abstract:
Radiographic medical images contain a vast wealth of information allowing for accurate non-invasive tumor characterization and ultimately improving cancer care. Radiomics enables the extraction of mineable high-dimensional features from images, quantification of tumor phenotypes for survival, recurrence, and treatment response prediction, and ultimately better patient stratification. Recent advances in AI, deep learning in particular, has allowed for the automated extraction of imaging features without the need for pre-definition. In this talk, we will be exploring deep learning radiomics applications in cancer patients from both single time point and longitudinal imaging data. We will also be identifying challenges regarding the stability, reproducibility, and transparency of such approaches.