Breast cancer is one of the most common cancers among women, with its heterogeneity posing significant challenges for diagnosis and treatment, profoundly impacting patient prognosis and quality of life. Whole Slide Imaging (WSI) in digital pathology provides high-resolution images that enable a comprehensive examination of the tumor microenvironment, offering advanced tools for breast cancer diagnosis and prognostic evaluation. However, manually reviewing whole slide images (WSIs) for tissue segmentation is time-consuming and prone to errors, highlighting the need for multi-target deep learning models to automate the segmentation of these complex structures. Multi-target segmentation offers distinct advantages by simultaneously processing multiple interrelated tissue regions within a single image, thereby enhancing accuracy and efficiency. Despite the potential of deep learning techniques in automating pathological analysis, their clinical adoption faces significant challenges. To address these, this paper proposes six criteria focused on clinical acceptability of deep learning methods: inherent limitations of WSIs, feature extraction, annotation requirements, efficiency, automated quantification, and interpretability. A rigorous review of publicly available datasets and deep learning methods identifies key challenges for clinical adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzes 29 core articles, highlighting the critical role of multi-target segmentation in breast cancer digital pathology while assessing the limitations of these techniques in clinical applications. Based on this analysis, this paper proposes six criteria to enhance the diagnostic performance of deep learning methods in multi-target segmentation for breast cancer digital pathology and to improve the clinical acceptability of deep learning methods.

Background: Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection of lymphocytes and molecular biomarkers is essential for improving diagnostic precision and patient prognosis. Whole slide images (WSIs) are central to digital pathology workflows in breast cancer assessment. However, applying deep learning techniques to WSIs presents persistent challenges, including variability in image quality, limited availability of high-quality annotations, poor model interpretability, high computational demands, and suboptimal processing efficiency. Methods: This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), examines deep learning-based detection methods for breast cancer published between 2020 and 2024. The analysis includes 39 peer-reviewed studies and 20 widely used WSI datasets. Results: To enhance clinical relevance and guide model development, this study introduces a five-dimensional evaluation framework covering accuracy and performance, robustness and generalization, interpretability, computational efficiency, and annotation quality. The framework facilitates a balanced and clinically aligned assessment of both established methods and recent innovations. Conclusions: This review offers a comprehensive analysis and proposes a practical roadmap for addressing core challenges in WSI-based breast cancer detection. It fills a critical gap in the literature and provides actionable guidance for researchers, clinicians, and developers seeking to optimize and translate WSI-based technologies into clinical workflows for comprehensive breast cancer assessment.

Background/Objectives: Tumor-infiltrating lymphocytes (TILs) are emerging as a crucial prognostic biomarker in triple-negative breast cancer (TNBC). However, their clinical utility remains constrained by the subjectivity and interobserver variability of manual scoring, despite standardization efforts by the International TILs Working Group (TIL-WG). This study aimed to evaluate the interobserver agreement among pathologists in scoring stromal and intratumoral TILs from H&E-stained TNBC slides and to identify contributing histological factors. Methods: Two consultant pathologists at Hospital Canselor Tuanku Muhriz, Kuala Lumpur, independently assessed 64 TNBC cases using TIL-WG guidelines. Interobserver agreement was quantified using the intraclass correlation coefficient (ICC) and Cohen’s kappa coefficient. Cases with over 10% scoring discrepancies underwent review by a third pathologist, and a consensus discussion was held to explore the underlying confounders. Results: Our results showed moderate interobserver agreement for stromal TILs (ICC = 0.58) and strong agreement for intratumoral TILs (ICC = 0.71). Significant variability was attributed to three main confounding variables: heterogeneous TIL distribution, poorly defined tumor-stroma interface, and focal dense lymphoid infiltrates. Conclusions: These findings highlight the need for standardized TIL scoring protocols and suggest that validated AI-based tools may help mitigate observer variability in future TIL assessments.