Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence / M. Ivanova, C. Pescia, D. Trapani, K. Venetis, C. Frascarelli, E. Mane, G. Cursano, E. Sajjadi, C. Scatena, B. Cerbelli, G. D'Amati, F.M. Porta, E. Guerini-Rocco, C. Criscitiello, G. Curigliano, N. Fusco. - In: CANCERS. - ISSN 2072-6694. - 16:11(2024), pp. 1981.1-1981.20. [10.3390/cancers16111981]

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

C. Pescia
Secondo
;
D. Trapani;K. Venetis;C. Frascarelli;G. Cursano;E. Sajjadi;E. Guerini-Rocco;C. Criscitiello;G. Curigliano
Penultimo
;
N. Fusco
Ultimo
2024

Abstract

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.
artificial intelligence; biomarkers; breast cancer; deep learning; early breast cancer; pathology; predictive algorithms; risk stratification
Settore MED/08 - Anatomia Patologica
Settore MED/06 - Oncologia Medica
Settore MEDS-04/A - Anatomia patologica
Settore MEDS-09/A - Oncologia medica
2024
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1087410
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