@article{10.37349/etat.2022.00113,
abstract = {The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools},
author = {Cè, Maurizio and Caloro, Elena and Pellegrino, Maria E and Basile, Mariachiara and Sorce, Adriana and Oliva, Giancarlo and Cellina, Michaela},
doi = {10.37349/etat.2022.00113},
journal = {Exploration of Targeted Anti-tumor Therapy},
pages = {795--816},
title = {{Artificial intelligence in breast cancer imaging : risk stratification , lesion detection and classification , treatment planning and prognosis — a narrative review}},
url = {https://www.explorationpub.com/Journals/etat/Article/1002113},
volume = {3},
year = {2022},
number = {6}
}