Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high
cost and technical barriers limit the application of these tests, hindering optimal treatment …

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the
individual through developments in immunotherapies and targeted therapies. However, long …

Background The objective of this comprehensive pan-cancer study is to evaluate the
potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from …

Background Digitized histopathological tissue slides and genomics profiling data are
available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has …

We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and
eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a …

Histopathology and genomic profiling are cornerstones of precision oncology and are
routinely obtained for patients with cancer. Traditionally, histopathology slides are manually …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

BACKGROUND Identifying genetic mutations in cancer patients have been increasingly
important because distinctive mutational patterns can be very informative to determine the …

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically
approved applications use this technology. Most approaches, however, predict categorical …

Over the past decade, many new cancer treatments have been developed and made
available to patients. However, in most cases, these treatments only benefit a specific …