Gastric cancer possesses great histological and molecular diversity, which creates obstacles
for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's …

Background We aimed to develop a deep learning (DL) model to predict DNA mismatch
repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained …

Tumour mutational burden (TMB) is an important biomarker for predicting the response to
immunotherapy in patients with cancer. Gold-standard measurement of TMB is performed …

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass
slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract …

Lung cancer is a leading cause of death in both men and women globally. The recent
development of tumor molecular profiling has opened opportunities for targeted therapies for …

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-
based analysis of histological tissue sections of the primary tumor. So far, this has been …

Deep learning (DL)-based image analysis has recently seen widespread application in
digital pathology. Recent studies utilizing DL in cytopathology have shown promising …

Background Precise prognosis prediction in patients with colorectal cancer (ie, forecasting
survival) is pivotal for individualised treatment and care. Histopathological tissue slides of …

Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic
factor for breast cancer (BC) patients and is correlated with improved survival. However …

Background Computational pathology uses deep learning (DL) to extract biomarkers from
routine pathology slides. Large multicentric datasets improve performance, but such …