Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review

• Main Authors: Maurizio Cè (Author), Elena Caloro (Author), Maria E. Pellegrino (Author), Mariachiara Basile (Author), Adriana Sorce (Author), Deborah Fazzini (Author), Giancarlo Oliva (Author), Michaela Cellina (Author)
• Format: Book
• Published: Open Exploration Publishing Inc., 2022-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

10.37349/etat.2022.00113
2692-3114