Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

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 …

Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …

The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …

In colorectal cancers, the tumor microenvironment plays a key role in prognosis and therapy
efficacy. Patient-derived tumor organoids (PDTO) show enormous potential for preclinical …

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to
intratumour heterogeneity. The interplay of these biological processes and their respective …

Background Gastrointestinal cancers account for approximately 20% of all cancer diagnoses
and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence–based …

Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …