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Digital Pathology or whole slide imaging (WSI) is a diagnostic evaluation technique that produces digital images of high quality from tissue fragments. These images are formed on glass slides and evaluated by pathologist with the aid of microscope. As the concept of digital pathology is introduced, these high quality images are digitized and produced on-screen whole slide images in the form of digital files. This has paved the way for pathologists to collaborate with other pathology professionals in case of any additional recommendations and also provides remote working opportunities. The application of digital pathology in clinical practice is glazed with several advantages and adopted by pathologists and researchers for clinical, educational and research purposes. Moreover, digital pathology system integration requires an intensive effort from multiple stakeholders. All pathology departments have different needs, case usage, and blueprints, even though the framework elements and variables for effective clinical integration can be applied to any institution aiming for digital transformation. This article reviews the background and developmental phases of digital pathology and its application in clinical services, educational and research activities. [ABSTRACT FROM AUTHOR]

Digital pathology is revolutionizing clinical diagnostics by offering enhanced efficiency, accuracy, and accessibility of pathological examinations. This study explores the implementation and validation of digital pathology in a large tertiary academic center, focusing on its gradual integration and transition into routine clinical diagnostics. In a comprehensive validation process over a 6-month period, we compared sign-out of digital and physical glass slides of a wide range of different tissue specimens and histopathological diagnoses. Key metrics such as diagnostic concordance and user satisfaction were assessed by involving the pathologists in a validation training and study phase. We measured turnaround times before and after transitioning to digital pathology to assess the impact on overall efficiency. Our results demonstrate a 99% concordance between the analog and digital reports while at the same time reducing the time to sign out a case by almost a minute, suggesting potential long-term efficiency gains. Our digital transition positively impacted our pathology workflow: Pathologists reported increased flexibility and satisfaction due to the ease of accessing and sharing digital slides. However, challenges were identified, including technical issues related to image quality and system integration. Lessons learned from this study emphasize the importance of robust training programs, adequate IT support, and ongoing evaluation to ensure successful integration. This validation study confirms that digital pathology is a viable and beneficial tool for accurate clinical routine diagnostics in large academic centers, offering insights for other institutions considering similar endeavors., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2025. The Author(s).)

Tumor-stroma ratio (TSR quantification in primary colorectal cancer is an important prognostic parameter, with stroma-rich tumors considered to have an unfavorable prognosis. Earlier studies involved relevant region selection and TSR quantification by human analysts, who are prone to interobserver variability. The aim of the current study was to develop a fully automated, quantitative algorithm for TSR analysis based on precise segmentation of H&E-stained histological tissue sections. The TSR quantification algorithm was developed based on the segmentation backbone, allowing accurate pixel-wise mapping of all relevant tissue classes (n = 12), including tumor cells, tumoral stroma, necrosis, and mucin. Three well-characterized cohorts of patients with stage I-IV primary operable colorectal cancer and available digital H&E histological slides were included (n = 548, n = 147, and n = 622, respectively). Three sizes of area for TSR analysis were tested (1.0, 1.5, and 2.0 mm). The maximal TSR value per case was used for prognostic analysis involving different clinical endpoints. Regional heterogeneity of TSR was high in most tumors, with the algorithm effectively finding the most relevant region for analysis. Maximal case-level TSR values depended on the size of the area for analysis, which also significantly influences the prognostic performance of the TSR and must be a matter of standardization. In Cox analysis, an analytical size of 1 mm allowed the best performance, with an independent prognostic role retained in the context of other pathological variables for progression-free survival, cancer-specific survival, and overall survival endpoints. A powerful, fully automated, fully quantitative, objective tool for TSR assessment in primary colorectal cancer was developed and validated to have independent prognostic value. Standardization of TSR quantification is important given that analytical parameters can substantially influence the prognostic performance., Competing Interests: Declarations. Ethical approval: All study steps were performed in accordance with the Declaration of Helsinki. This study was approved by the Ethical committee of the University of Cologne (20–1583). Given retrospective/archive nature of used data, the necessity of obtaining patients’ permission was waived by ethical committee. Competing interests: The authors declare no competing interests., (© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical specialties. In clinical oncology, the most promising applications include those involving image analysis, such as digital pathology. In this Perspective, we provide a comprehensive examination of the developments in AI in digital pathology between 2019 and 2024. We evaluate the current landscape from the lens of technological innovations, regulatory trends, deployment and implementation, reimbursement and commercial implications. We assess the technological advances that have driven improvements in AI, enabling more robust and scalable solutions for digital pathology. We also examine regulatory developments, in particular those affecting in-house devices and laboratory-developed tests, which are shaping the landscape of AI-based tools in digital pathology. Finally, we discuss the role of reimbursement frameworks and commercial investment in the clinical adoption of AI-based technologies. In this Perspective, we highlight both the progress and challenges in AI-driven digital pathology over the past 5 years, outlining the path forward for its adoption into routine practice in clinical oncology., Competing Interests: Competing interests: S. Badve is on the advisory board for Mindpeak; and is also an ad hoc adviser for Agilent, AstraZeneca, Daichii-Sanyo and Roche-Ventana. A.M. is an equity holder in Picture Health, Elucid Bioimaging and Inspirata Inc.; currently he serves on the advisory board of Picture Health; has sponsored research agreements with AstraZeneca, Boehringer-Ingelheim, Bristol Myers-Squibb and Eli-Lilly; has developed technology licensed to Picture Health and Elucid Bioimaging; is involved in two different R01 grants with Inspirata Inc.; and is a member for the Frederick National Laboratory Advisory Committee. A.A., S. Bharadwaj, G.C. and T.P. declare no competing interests., (© 2025. Springer Nature Limited.)

Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment. The use of deep learning algorithms for predicting WSIs has opened up new avenues for advanced medical diagnostics. Additionally, stain normalization can reduce the color and intensity variations present in WSI from different hospitals. As a result, deep learning classification accuracy improves. However, WSI reading and color normalization are still largely performed by using CPUs, leading to sub-optimal performance. We proposed a High-Performance Heterogeneous Computing Toolkit for Tissue Image (HCTTI) that integrates multiple computer system-level optimizations and encompasses WSI reading, tile normalization, and tile saving. We explored the potential advantages and limitations of different WSI readers and color normalization techniques in WSI analysis and the performance of different tile serialization formats for saving tiles. We found that HCTTI is 7 × faster than OpenSlide for reading WSIs, GPU implementation of the Macenko normalization algorithm is 9 × faster than TIAToolbox implementation, and HDF5 is faster than png and Zarr for storing normalized images in both writing (13 × acceleration compared to png) and reading (2 × acceleration compared to png), Specifically, HDF5 provides superior performance in handling large, complex datasets due to its efficient chunking and compression capabilities, as well as its broad support for hierarchical data management, making it very suitable for workloads like deep learning training I/O pattern that involves randomly reading large amount of small files in each training epoch. We also achieved linear acceleration in our multi-node distributed GPU implementation. To our knowledge, HCTTI is the first comprehensive toolkit that comprises distributed WSI reading, normalization, and serialization. It is 13 × speedup compared to TIAToolbox implementation for normalizing a single WSI. Our findings could help pave the way for more effective and efficient deep learning-based approaches to WSI analysis, with the potential to transform medical diagnosis and treatment for a wide range of conditions. The source code of HCTTI is available at https://github.com/wangbo00129/HCTTI ., Competing Interests: Declarations. Consent to Participate: Informed consent was obtained from all individual participants included in the study. Consent for Publication: All images within this publication have sufficient approval to be shared publicly. Conflict of Interest: The authors declare no competing interests., (© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)