Current Updates in Management of HER2-Positive and HER2-Low Breast Cancer

Thaper, Arushi; Tran, Jennifer; Ali, Azka

doi:10.1007/s12609-023-00486-2

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…The current approach is transitioning from a binary HER2 assessment to recognizing HER2-low tumors (1+ or 2+ with negative in situ hybridization (ISH)), as emphasized by data with novel treatment, specifically trastuzumab deruxtecan in HER2-low BC [ 97 ]. This shift has fundamentally transformed the treatment approach for HER2-low disease, with the proven effectiveness of antibody–drug conjugates (ADCs) serving as a significant addition to the therapeutic arsenal [ 98 , 99 ]. The focus now turns to the importance of precise HER2 evaluation, which can be facilitated by advanced AI technologies [ 27 , 57 , 100 ].…”

Section: Prognostic and Predictive Models On Immunohistochemistry-sta...mentioning

confidence: 99%

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

Ivanova,

Pescia,

Trapani

et al. 2024

Cancers

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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.

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