Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis

A critical knowledge gap has been noted in breast cancer detection, prognosis, and evaluation between tumor microenvironment and associated neoplasm. Artificial intelligence (AI) has multiple subsets or methods for data extraction and evaluation, including artificial neural networking, which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training. Deep machine learning and AI hold great potential to accurately assess tumor microenvironment models employing vast data management techniques. Despite the significant potential AI holds, there is still much debate surrounding the appropriate and ethical curation of medical data from picture archiving and communication systems. AI output's clinical significance depends on its human predecessor's data training sets. Integration between biomarkers, risk factors, and imaging data will allow the best predictor models for patient-based outcomes. (Am J Pathol 2021, 191: 1364-1373; https://doi.org/10.1016/j.ajpath.2021.01.014)

Automated summary

There is a critical knowledge gap in breast cancer detection, prognosis, and evaluation when it comes to the tumor microenvironment and associated neoplasms. AI, particularly using artificial neural networking, has the potential to accurately assess tumor microenvironment models, but ethical curation of medical data remains a debate. Integrating biomarkers, risk factors, and imaging data can provide the best predictive models for patient outcomes.