Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.
Mariia Ivanova, Carlo Pescia, Dario Trapani, Konstantinos Venetis, Chiara Frascarelli, Eltjona Mane, Giulia Cursano, Elham Sajjadi, Cristian Scatena, Bruna Cerbelli, Giulia d'Amati, Francesca Maria Porta, Elena Guerini-Rocco, Carmen Criscitiello, Giuseppe Curigliano, Nicola Fusco
Author Information
Mariia Ivanova: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Carlo Pescia: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Dario Trapani: Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Konstantinos Venetis: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Chiara Frascarelli: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Eltjona Mane: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Giulia Cursano: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Elham Sajjadi: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Cristian Scatena: Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy. ORCID
Bruna Cerbelli: Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, Italy.
Giulia d'Amati: Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy. ORCID
Francesca Maria Porta: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Elena Guerini-Rocco: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Carmen Criscitiello: Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Giuseppe Curigliano: Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
Nicola Fusco: Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy. ORCID
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.
