Artificial intelligence for breast cancer prevention: the vision ahead.
Authors
Affiliations (2)
Affiliations (2)
- Lega Italiana per la Lotta contro i Tumori (LILT) Milano Monza Brianza, Milan, Italy. [email protected].
- Breast Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Abstract
Over the past decade, artificial intelligence (AI) has entered the medical field, particularly in radiology. Fifty years after the transition from "silver to silicon", we are experiencing a second digital revolution fueled by AI and radiomics. Breast imaging-and mammography in particular-plays a special role in this transformation due to the availability of large screening datasets. The advantages that AI tools bring to mammography interpretation are substantial: they can increase cancer detection rates by over 25% and reduce reading workload by more than 40%. In addition, AI can aid in quality control of mammograms and-importantly-in stratifying breast cancer risk, enabling personalized screening strategies. However, enhancing screening programs alone is not sufficient for BC prevention. True prevention aims to stop cancer before it starts. As defined by the Oxford Learner's Dictionary of Academic English, prevention is "the act of stopping something bad from happening." Screening represents secondary prevention and, on its own, is not enough. As physicians-and not merely image analysts-we must also focus on primary (true) prevention by promoting a healthier lifestyle, regular physical exercise, balanced diet and weight control, and smoking cessation. Pharmacological prevention should be considered when indicated, such as for women who were diagnosed with selected B3 lesions or have a personal history of breast cancer. Web-based tools and mobile apps-especially when combined with wearable devices-can support these efforts. AI has the potential to help both primary and secondary breast cancer prevention. Much research is expected in this area. KEY POINTS: AI can enhance screening mammography by increasing detection rates (by over 25%) and reducing the reading workload (by over 40%), as well as through deep learning-based risk stratification. Breast radiologists should prioritize primary (true) prevention by promoting healthy lifestyle choices, open to integrating web-based tools, mobile apps, and wearable devices. AI holds promise for both supporting primary and secondary breast cancer prevention, with radiologists playing a pivotal role in its implementation.