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

Tumour-infiltrating lymphocytes (TILs) are considered crucial in anti-tumour immunity.
Accordingly, the presence of TILs contains prognostic and predictive value. In 2011, we …

The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

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

Artificial intelligence (AI) can extract visual information from histopathological slides and
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists
grade the microscopic appearance of breast tissue using the Nottingham criteria, which are …

Abstract Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key
biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is …

Computational pathology is revolutionizing the field of pathology by integrating advanced
computer vision and machine learning technologies into diagnostic workflows. It offers …

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 …