Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.

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.

Advances in breast cancer risk modeling: integrating clinics, imaging, pathology and artificial intelligence for personalized risk assessment.

Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.

Assessment of estrogen receptor low positive status in breast cancer: Implications for pathologists and oncologists.

Estrogen receptor (ER) status assessment by immunohistochemistry (IHC) is the gold standard test for the identification of patients with breast cancer who may benefit from endocrine therapy (ET). Whilst most ER+ breast cancers have a high IHC score, about 3% of cases display a low positivity, with 1% to 10% of cells being weakly stained. These tumors are generally classified within the luminal-like category; however, their risk profile seems to be more similar to that of ER-negative breast cancers. The decision on ET for patients with a diagnosis of ER-low breast cancer should be carefully considered in light of the risks and possible benefits of the treatment. Potential pitfalls hinder pathologists and oncologists from establishing an appropriate threshold for "low positivity". Furthermore, several pre-analytical and analytical variables might trouble the pathological identification of these clinically challenging cases. In this review, we sought to discuss the adversities that can be accounted for the pathological identification of ER-low breast cancers in real-world clinical practice, and to provide practical suggestions for the perfect ER testing in light of the most updated recommendations and guidelines.

Breast Cancer Systemic Treatments and Upper Limb Lymphedema: A Risk-Assessment Platform Encompassing Tumor-Specific Pathological Features Reveals the Potential Role of Trastuzumab.

Breast cancer related lymphedema (BCRL) is frequent but strategies for an individualized risk assessment are lacking. We aimed to define whether tumor-specific pathological features, coupled with clinical and therapeutic data, could help identify patients at risk. Data from 368 patients with node-positive breast cancers were retrospectively collected, including 75 patients with BCRL (0.4⁻25.6 years follow-up). BCRL was assessed during the standard follow-up oncology visits using the circumferential measurement. Clinicopathologic and therapeutic factors associated with BCRL were integrated into a Cox proportional hazards regression model. Lymphovascular invasion (LVI) was more common in BCRL patients (n = 33, 44% vs. n = 85, 29%, p = 0.01), akin extra nodal extension (ENE) of the metastasis (n = 57, 76% vs. n = 180, 61%, p = 0.02). Sentinel lymph node excision without axillary dissection and extra-axillary radiotherapy were BCRL-unrelated. A higher number of BCRL-positive patients were treated with taxane-based chemotherapy with or without trastuzumab, compared to BCRL-negative patients (p < 0.01). Treatment with trastuzumab and/or taxanes, adjusted for systemic infections, laterality, therapy, and pathological features (i.e., LVI and ENE), had a significant impact in BCRL-free survival (p < 0.01). This work offers new insights on BCRL risk stratification, where the integration of clinical, therapeutic, and tumor-specific pathological data suggests a possible role of anti-human epidermal growth factor receptor 2 (HER2) therapy in BCRL pathogenesis.

Tackling the diversity of breast cancer related lymphedema: Perspectives on diagnosis, risk assessment, and clinical management.

Breast cancer related lymphedema (BCRL) develops as a consequence of surgical treatment and/or radiation therapy in a significant number of breast cancer patients. The etiology of this condition is multifactorial and has not yet been completely elucidated. Risk factors include high body mass index, radical surgical procedures (i.e. mastectomy and axillary lymph node dissection), number of lymph nodes removed and number of metastatic lymph nodes, as well as nodal radiation, and chemotherapy. However, these predisposing factors explain only partially the BCRL occurrence, suggesting the possible involvement of individual determinants. Despite the implementation of conservative approaches, BCRL still remains in a proportion of cases an incurable and progressive condition with major physical and psychological implications. To date, diagnostic methods and staging systems lack uniformity, leading to a possible underestimation of the real incidence of this condition, decreasing early detection and thus the possibility of an effective treatment. Several preventive and therapeutic options are available, both conservative and surgical, but are not included in a standardized intervention protocol, tailored on patient's specific characteristics. In this review, we provide a comprehensive overview of the current state-of-knowledge of BCRL management, novel advantages in the assessment of pre-operative evaluation and risk prediction and discuss strengths and weaknesses of diagnostic and treatment strategies currently accessible in clinical practice.