Academic Journal
Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence
| العنوان: | Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence |
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| المؤلفون: | Ivanova M., Pescia C., Trapani D., Venetis K., Frascarelli C., Mane E., Cursano G., Sajjadi E., Scatena C., Cerbelli B., d'Amati G., Porta F. M., Guerini-Rocco E., Criscitiello C., Curigliano G., Fusco N. |
| المساهمون: | M. Ivanova, C. Pescia, D. Trapani, K. Veneti, 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 |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute (MDPI) |
| سنة النشر: | 2024 |
| المجموعة: | The University of Milan: Archivio Istituzionale della Ricerca (AIR) |
| مصطلحات موضوعية: | artificial intelligence, biomarker, breast cancer, deep learning, early breast cancer, pathology, predictive algorithm, risk stratification, Settore MED/08 - Anatomia Patologica, Settore MED/06 - Oncologia Medica, Settore MEDS-04/A - Anatomia patologica, Settore MEDS-09/A - Oncologia medica |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/38893102; info:eu-repo/semantics/altIdentifier/wos/WOS:001245614600001; volume:16; issue:11; firstpage:1; lastpage:20; numberofpages:20; journal:CANCERS; https://hdl.handle.net/2434/1087410 |
| DOI: | 10.3390/cancers16111981 |
| الاتاحة: | https://hdl.handle.net/2434/1087410 https://doi.org/10.3390/cancers16111981 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.B8BFB1C2 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/cancers16111981 |
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