Derivation of a nuclear heterogeneity image index to grade DCIS

Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance

Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.

Future Practices of Breast Pathology Using Digital and Computational Pathology

Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.

Artificial intelligence in breast cancer histopathology

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.

Updates on treating ductal carcinoma in situ: what's to know in 2021

PURPOSE OF REVIEW

Ductal carcinoma in situ (DCIS) is a noninvasive stage of disease but understood to be a nonobligate precursor to invasive breast cancer. As such, women with DCIS are routinely recommended for standard breast cancer treatment to prevent progression to invasive disease. DCIS, however, represents a heterogeneous group of lesions that differs in its biologic behavior and risk of progression. Thus, optimal treatment is unclear. This review presents the clinical trials evaluating the de-escalation of therapy, attempts at risk stratification, and future directions in the management of this disease.

RECENT FINDINGS

The de-escalation of therapy for patients with DCIS is being actively explored. Although no group of patients based on clinicopathologic features has yet been identified as suitable for omission of therapy, molecular tests appear better able to stratify patients at low risk for whom omission of radiation may be considered. Trials considering omission of surgery are ongoing, and the use of Herceptin and vaccine therapy are also being explored.

SUMMARY

The current review provides a centralized summary enabling the clinician to better understand the complexity of DCIS and the controversies over the optimal management of this disease. It highlights the need for better risk stratification to individualize patient care.

VIDEO ABSTRACT

http://links.lww.com/COOG/A77.

Nuclear Dynamics and Chromatin Structure: Implications for Pancreatic Cancer

Changes in nuclear shape have been extensively associated with the dynamics and functionality of cancer cells. In most normal cells, nuclei have a regular ellipsoid shape and minimal variation in nuclear size; however, an irregular nuclear contour and abnormal nuclear size is often observed in cancer, including pancreatic cancer. Furthermore, alterations in nuclear morphology have become the 'gold standard' for tumor staging and grading. Beyond the utility of altered nuclear morphology as a diagnostic tool in cancer, the implications of altered nuclear structure for the biology and behavior of cancer cells are profound as changes in nuclear morphology could impact cellular responses to physical strain, adaptation during migration, chromatin organization, and gene expression. Here, we aim to highlight and discuss the factors that regulate nuclear dynamics and their implications for pancreatic cancer biology.

Tissue mechanics in stem cell fate, development, and cancer

Cells in tissues experience a plethora of forces that regulate their fate and modulate development and homeostasis. Cells sense mechanical cues through localized mechanoreceptors or by influencing cytoskeletal or plasma membrane organization. Cells translate force and modulate their behavior through a process termed mechanotransduction. Cells tune their tension upon exposure to chronic force by engaging cellular machinery that modulates actin tension, which in turn stimulates matrix remodeling and stiffening and alters cell-cell adhesions until cells achieve a state of tensional homeostasis. Loss of tensional homeostasis can be induced through oncogene activity and/or tissue fibrosis, accompanies tumor progression, and is associated with increased cancer risk. The mechanical stresses that develop in tumors can also foster the mesenchymal-like transdifferentiation of cells to induce a stem-like phenotype that contributes to their aggression, metastatic dissemination, and treatment resistance. Thus, strategies that ameliorate tumor mechanics may comprise an effective strategy to prevent aggressive tumor behavior.

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.