Traditional task-specific computational pathology models require a substantial labeled dataset for training to perform various tasks, while foundation models can be trained on large-scale, unlabeled ...

In a recent study published in Nature Medicine, researchers demonstrated the use of the foundational model Virchow for computational analysis of pathological reports, prediction of biomarkers, and ...

Elacestrant combinations in patients (pts) with ER+/HER2- locally advanced or metastatic breast cancer (mBC): Safety update from ELEVATE, a phase (Ph) 1b/2, open-label, umbrella study. This is an ASCO ...

Discover the impact of digital pathology on drug discovery and biomarker research this International Women’s Day with Dr ...

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may ...

When the performance of AI models was assessed within stratified patient subgroups, such as only high-grade breast cancers or only MSI-positive tumors, accuracy fell substantially, revealing that the ...

Scientists have used computational tools, including machine learning, to differentiate between subtypes of rheumatoid arthritis. In this study, which was reported in Nature Communications, the ...

TOKYO & BASKING RIDGE, N.J.--(BUSINESS WIRE)-- Results from an exploratory analysis of the TROPION-Lung01 phase 3 trial showed TROP2 as measured by quantitative continuous scoring (QCS), AstraZeneca’s ...

Computational pathology, which assesses molecular-level features of diseases directly from tissue images (rather than testing the tissue via methods such as staining or sequencing) is making rapid ...