In Lung Cancer, Training Computers to Be Pathologists

Computerized image process technology is emerging as a field that improves upon histopathologic determination across several malignancies. Now a report published online August 16 in Nature Communications indicates that computers can be trained to analyze –hematoxylin and eosin (H&E)–stained whole slide histopathologic images of patients with lung cancer with a higher degree of accuracy than trained pathologists. They do so by extracting information from approximately 10,000 image features, such as cell size, shape, distribution of pixel intensity in the cells and nuclei, and texture of cells and nuclei. This “machine-learning” approach was very accurate in picking out lung cancer with a squamous histology from lung cancer adenocarcinomas and was also able to predict prognosis — long-term from short-term survival — with a greater than 85% accuracy. “This objective approach can…


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