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 …

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …

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

Summary Background The DoMore-v1-CRC marker was recently developed using deep
learning and conventional haematoxylin and eosin-stained tissue sections, and was …

Pathologists worldwide are facing remarkable challenges with increasing workloads and
lack of time to provide consistently high-quality patient care. The application of artificial …

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy
for many types of solid tumors. However, the majority of patients with cancer will not …

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

Colorectal cancers (CRCs) with high microsatellite instability (MSI-H) and deficient mismatch
repair (dMMR) show molecular and clinicopathological characteristics that differ from those …

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and
has become a cornerstone of modern precision oncology. Histopathological evaluation is …