Abstract
Background and Objective: Digital pathology represents an invaluable source of information and a long-term investment with high returns, with the possible deployment of artificial intelligence (AI) tools for both the clinical and research activity. Moreover, the rising nosological complexity of the oncologic diseases, e.g., lung cancer, is stressing the need of integration among different subspecialities (e.g., radiology, molecular biology, and immuno-oncology) for the final characterization of cancer. In this setting, digital pathology can play a pivotal role in the “integration” of these different competencies, and the application of AI for prognostic/predictive purposes can represent a further “third” revolution in pathology. The objective of the present review is to provide an updated overview of the possible role of digital and integrative pathology in detecting gene mutations and, especially, translocations in different types of tumors, focusing on the promising implications that this advancement can have in lung cancer characterization.
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