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 develo** and …

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 …

The success of training computer-vision models heavily relies on the support of large-scale,
real-world images with annotations. Yet such an annotation-ready dataset is difficult to …

Background Endometrial cancer can be molecularly classified into POLE mut, mismatch
repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) …

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for
optimal treatment. DNA methylation profiles, which capture the methylation status of …

Personalized treatment strategies for cancer frequently rely on the detection of genetic
alterations which are determined by molecular biology assays. Historically, these processes …

Breast cancer remains a highly prevalent disease with considerable inter-and intra-tumoral
heterogeneity complicating prognostication and treatment decisions. The utilization and …

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 …

Survival outcome assessment is challenging and inherently associated with multiple clinical
factors (eg, imaging and genomics biomarkers) in cancer. Enabling multimodal analytics …

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …