TY  - JOUR
T1  - Artificial intelligence in breast cancer imaging : risk stratification , lesion detection and classification , treatment planning and prognosis — a narrative review
AU  - Cè, Maurizio
AU  - Caloro, Elena
AU  - Pellegrino, Maria E
AU  - Basile, Mariachiara
AU  - Sorce, Adriana
AU  - Oliva, Giancarlo
AU  - Cellina, Michaela
Y1  - 2022///
JO  - Exploration of Targeted Anti-tumor Therapy
VL  - 3
IS  - 6
SP  - 795
EP  - 816
DO  - 10.37349/etat.2022.00113
UR  - https://www.explorationpub.com/Journals/etat/Article/1002113
AB  - 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
ER  -