In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and'hand-crafted' feature-based methodologies.
The Houston Chronicle reports that state figures show more than 90 percent of the 117 workers compensation claims filed by Texas firefighters with cancer have been denied in the past six years. Union officials say cities have ignored a state law requiring the government to presume that firefighters' cancers are caused by exposure to carcinogens on the job.