Background: While established deep learning approaches for histopathology usually consist
of a two-step process, a cell or region segmentation and subsequent feature calculation, end …

The biological complexity reflected in histology images requires advanced approaches for
unbiased prognostication. Machine learning and particularly deep learning methods are …

Background Gastric cancer is one of the leading causes of cancer death in the world.
Improving gastric cancer survival prediction can enhance patient prognostication and …

Aim Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node
metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue …

Background Lymph node metastasis (LNM) in gastric cancer is a prognostic factor and has
implications for the extent of lymph node dissection. The lymphatic drainage of the stomach …

Objective: We aimed to develop a deep learning-based signature to predict prognosis and
benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images …

Background and purpose Risk prediction of overall survival (OS) is crucial for gastric cancer
(GC) patients to assess the treatment programs and may guide personalized medicine. A …

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently,
artificial intelligence approaches, particularly machine learning and deep learning, are …

Gastric cancer is one of the deadliest cancers worldwide. Accurate prognosis is essential for
effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment …

Background Peritoneal recurrence is the predominant pattern of relapse after curative-intent
surgery for gastric cancer and portends a dismal prognosis. Accurate individualised …