Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples.

HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks.

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model.

Gastrointestinal tissue-based molecular biomarkers: a practical categorisation based on the 2019 World Health Organization classification of epithelial digestive tumours.

Molecular biomarkers have come to constitute one of the cornerstones of oncological pathology. The method of classification not only directly affects the manner in which patients are diagnosed and treated, but also guides the development of drugs and of artificial intelligence tools. The aim of this article is to organise and update gastrointestinal molecular biomarkers in order to produce an easy-to-use guide for routine diagnostics.