Radiomics describes a broad set of computational methods that extract quantitative features
from radiographic images. The resulting features can be used to inform imaging diagnosis, …

The characterization of cancer genomes has provided insight into somatically altered genes
across tumors, transformed our understanding of cancer biology, and enabled tailoring of …

… For example, Cheerla and Gevaert 48 used an intermediate fusion strategy to integrate
histopathology, clinical and expression data to predict patient survival for multiple cancer types. …

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition
of progenitor and stem-cell-like features. Here, we provide novel stemness indices for …

… A to O correspond to gene set clusters that are differentially expressed in at least one subtype.
These functional categories were investigated by extracting overlapping genes in >10% of …

Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which
are the basis of day-to-day clinical decision support—are often underused to guide …

… Since these methods used overlap O = V ( G t ⋂ A u t o ) / V ( G t ⋃ A u t o ) to measure the
model performance, we additionally reported our results using the same measurement in this …

Purpose To identify prognostic imaging biomarkers in non–small cell lung cancer (NSCLC)
by means of a radiogenomics strategy that integrates gene expression and medical images …

An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence
(AI) may aid in triaging skin diseases and identifying malignancies. However, most AI …

The application of primary organoid cultures containing epithelial and mesenchymal elements
to cancer modeling holds promise for combining the accurate multilineage differentiation …