Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

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.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images

“…Numerous studies have highlighted the efficacy of deep learning models in extracting critical information from routine pathological images, offering valuable clinical insights [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, deep learning has been utilized for quantitative image analysis to forecast disease progression patterns, prognoses, and other clinical outcomes [ 27 , 28 , 29 , 30 , 31 ]. Despite these advancements, there remains a paucity of study specifically addressing AI-based STAS prediction using histopathological images.…”

“…In contrast, deep learning approaches have shown promise in capturing intricate features within images more effectively. By employing end-to-end training, these methods enhance predictive capabilities and provide more accurate prognostic information [ 27 , 28 , 29 , 30 , 31 ]. For example, recent studies have demonstrated the application of deep learning models to various medical imaging tasks, achieving high performance metrics.…”

The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy

“…In patients with EBC, precisely estimating the possibility of local or distant recurrence is of vital importance for assessing the risk–benefit degree of systemic therapies [ 5 , 6 ]. In particular, to guide the selection and de-escalation of adjuvant therapy, there is a need for diagnostic algorithms that enhance the information obtained from histological criteria by integrating them with molecular data [ 7 , 8 ].…”