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.

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

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

Standardized molecular pathology workflow for ctDNA-based ESR1 testing in HR+/HER2- metastatic breast cancer.

Mutations in the estrogen receptor alpha gene (ESR1) can lead to resistance to endocrine therapy (ET) in hormone receptor-positive (HR+)/ HER2- metastatic breast cancer (MBC). ESR1 mutations can be detected in up to 40 % of patients pretreated with ET in circulating tumor DNA (ctDNA). Data from prospective randomized trials highlight those patients with HR+/HER2- MBC with detectable ESR1 mutations experience better outcomes when receiving novel selective estrogen receptor degraders (SERDs). There is a high need for optimizing ESR1 testing strategies on liquid biopsy samples in HR+/HER2- MBC, including a hugh quality workflow implementation and molecular pathology reporting standardization. Our manuscript aims to elucidate the clinical and biological rationale for ESR1 testing in MBC, while critically examining the currently available guidelines and recommendations for this specific type of molecular testing on ctDNA. The objective will extend to the critical aspects of harmonization and standardization, specifically focusing on the pathology laboratory workflow. Finally, we propose a clear and comprehensive model for reporting ESR1 testing results on ctDNA in HR+/HER2- MBC.

PIK3CA testing in hormone receptor-positive/HER2-negative metastatic breast cancer: real-world data from Italian molecular pathology laboratories.

Introduction: PIK3CA gene mutations occur in approximately 40% of hormone receptor-positive/HER2-negative (HR+/HER2-) metastatic breast cancers (MBCs), electing them to targeted therapy. Testing PIK3CA status is complex due to selection of biological specimen and testing method. Materials & methods: This work investigates real-life experience on PIK3CA testing in HR+/HER2- MBC. Clinical, technical and molecular data on PIK3CA testing were collected from two referral laboratories. Additionally, the results of a nationwide PIK3CA survey involving 116 institutions were assessed. Results: Overall, n = 35 MBCs were PIK3CA-mutated, with mutations mostly occurring in exons 9 (n = 19; 51.4%) and 20 (n = 15; 40.5%). The nationwide survey revealed significant variability across laboratories in terms of sampling methodology, technical assessment and clinical report signing healthcare figures for PIK3CA molecular testing in diagnostic routine practice. Conclusion: This study provides insights into the real-world routine of PIK3CA testing in HR+/HER2- MBC and highlights the need for standardization and networking in predictive pathology.

ESR1 mutations in HR+/HER2-metastatic breast cancer: Enhancing the accuracy of ctDNA testing.

Activating mutations of the estrogen receptor alpha gene (ESR1) are common mechanisms of endocrine therapy (ET) resistance in hormone receptor-positive (HR + )/Human Epidermal Growth Factor Receptor 2 (HER2)-negative metastatic breast cancer (MBC). Recent clinical findings emphasize that both old and new generations of selective ER degraders (SERDs) demonstrate enhanced clinical effectiveness in patients with MBC who have detectable ESR1 mutations via liquid biopsy. This stands in contrast to individuals with MBC carrying these mutations and undergoing conventional endocrine monotherapies like aromatase inhibitors (AIs). Liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), has emerged as a promising, minimally invasive alternative to conventional tissue-based testing for identifying ESR1 mutations. Within the context of the PADA-1 and EMERALD trials, distinct molecular methodologies and assays, specifically digital droplet PCR (ddPCR) and next-generation sequencing (NGS), have been employed to evaluate the mutational status of ESR1 within ctDNA. This manuscript critically examines the advantages and indications of various ctDNA testing methods on liquid biopsy for HR+/HER2-negative MBC. Specifically, we delve into the capabilities of ddPCR and NGS in identifying ESR1 mutations. Each methodology boasts unique strengths and limitations: ddPCR excels in its analytical sensitivity for pinpointing hotspot mutations, while NGS offers comprehensive coverage of the spectrum of ESR1 mutations. The significance of meticulous sample handling and timely analysis is emphasized, acknowledging the transient nature of cfDNA. Furthermore, we underscore the importance of detecting sub-clonal ESR1 mutations, as these variants can exert a pivotal influence on predicting both endocrine therapy resistance and responsiveness to SERDs. In essence, this work discusses the role of ctDNA analysis for detecting ESR1 mutations and their implications in tailoring effective therapeutic strategies for HR+/HER2- MBC.

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking.

BACKGROUND: Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. METHODS: In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis. RESULTS: These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. CONCLUSIONS: The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.

An Indeterminate for Malignancy FNA Report Does Not Increase the Surgical Risk of Incidental Thyroid Carcinoma.

Incidental thyroid carcinomas (ITCs) are a fairly frequent finding in daily routine practice, with papillary thyroid microcarcinoma being the most frequent entity. In our work, we isolated incidental cases arising in thyroids removed for other cytologically indeterminate and histologically benign nodules. We retrospectively retrieved cases with available thyroid Fine Needle Aspiration (FNA, 3270 cases), selecting those with an indeterminate cytological diagnosis (Bethesda classes III−IV, 652 cases). Subsequently, we restricted the analysis to surgically treated patients (163 cases) finding an incidental thyroid carcinoma in 22 of them. We found a 13.5% ITC rate, with ITCs representing 46.8% of all cancer histologically diagnosed in this indeterminate setting. Patients received a cytological diagnosis of Bethesda class III and IV in 41% and 59% of cases, respectively. All ITC cases turned out to be papillary thyroid microcarcinomas; 36% of cases were multifocal, with foci bilaterally detected in 50% of cases. We found an overall ITC rate concordant with the literature and with our previous findings. The assignment of an indeterminate category to FNA did not increase the risk of ITCs in our cohort. Rather, a strong statistical significance (p < 0.01) was found comparing the larger size of nodules that underwent FNA and the smaller size of their corresponding ITC nodule.