Advances in breast cancer risk modeling: integrating clinics, imaging, pathology and artificial intelligence for personalized risk assessment

• Published in: Future oncology (London, England) Vol. 19; no. 38; pp. 2547 - 2564
• Main Authors: Pesapane, Filippo, Battaglia, Ottavia, Pellegrino, Giuseppe, Mangione, Elisa, Petitto, Salvatore, Fiol Manna, Eliza Del, Cazzaniga, Laura, Nicosia, Luca, Lazzeroni, Matteo, Corso, Giovanni, Fusco, Nicola, Cassano, Enrico
• Format: Journal Article
• Language: English
• Published: England Future Medicine Ltd 01.12.2023
• Subjects: Artificial Intelligence; breast; Breast - diagnostic imaging; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - genetics; Early Detection of Cancer - methods; Female; Humans; imaging; Mammography - methods; pathology; Risk Assessment; Risk Factors; screening; women's health; risk factors; screening; pathology; breast; imaging; women's health
• ISSN: 1479-6694; 1744-8301; 1744-8301
• DOI: 10.2217/fon-2023-0365

Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence (AI) in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.