Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

Artificial Intelligence for Head and Neck Squamous Cell Carcinoma: From Diagnosis to Treatment

Head and neck squamous cell carcinoma (HNSCC) remains a globally prevalent malignancy with high morbidity and mortality. Despite therapeutic advances, patient outcomes are hindered by tumor heterogeneity, treatment-related toxicity, and the limitations of traditional prognostic tools. Artificial intelligence (AI) offers the opportunity to improve personalized HNSCC management by integrating complex radiologic, pathologic, and molecular data into actionable information insights. This review synthesizes recent developments in AI applications across the HNSCC care continuum, from diagnosis through treatment planning, emphasizing their clinical relevance and translational potential. AI has shown promise in enhancing diagnostic accuracy through automated tumor burden assessment, extranodal extension prediction, and endoscopic image analysis. Deep learning applied to radiology and digital pathology enables the extraction of prognostic features that may inform risk stratification and treatment de-escalation, particularly in human papillomavirus-associated oropharyngeal carcinoma. Multimodal AI models that fuse imaging, histopathology, and electronic health records have demonstrated superior performance in predicting survival outcomes compared with unimodal approaches. Additional applications include early toxicity detection during radiotherapy, adaptive treatment planning, and surgical complication forecasting. AI also holds potential in predicting immunotherapy response by identifying imaging and histologic correlates of tumor immunogenicity. Barriers to clinical translation remain, and continued development of explainable models, prospective trials, and seamless integration into clinical workflows will be critical for broad adoption. AI has already begun to affect HNSCC radiotherapy and surgical planning, and with thoughtful implementation, it may enable safer, more personalized care across the HNSCC treatment landscape.

Future of Artificial Intelligence-Machine Learning Trends in Pathology and Medicine

Artificial intelligence (AI) and machine learning (ML) are transforming the field of medicine. Health care organizations are now starting to establish management strategies for integrating such platforms (AI-ML toolsets) that leverage the computational power of advanced algorithms to analyze data and to provide better insights that ultimately translate to enhanced clinical decision-making and improved patient outcomes. Emerging AI-ML platforms and trends in pathology and medicine are reshaping the field by offering innovative solutions to enhance diagnostic accuracy, operational workflows, clinical decision support, and clinical outcomes. These tools are also increasingly valuable in pathology research in which they contribute to automated image analysis, biomarker discovery, drug development, clinical trials, and productive analytics. Other related trends include the adoption of ML operations for managing models in clinical settings, the application of multimodal and multiagent AI to utilize diverse data sources, expedited translational research, and virtualized education for training and simulation. As the final chapter of our AI educational series, this review article delves into the current adoption, future directions, and transformative potential of AI-ML platforms in pathology and medicine, discussing their applications, benefits, challenges, and future perspectives.

Breaking barriers: we need a multidisciplinary approach to tackle cancer drug resistance

Most cancer-related deaths result from drug-resistant disease(1,2). However, cancer drug resistance is not a primary focus in drug development. Effectively mitigating and treating drug-resistant cancer will require advancements in multiple fields, including early detection, drug discovery, and our fundamental understanding of cancer biology. Therefore, successfully tackling drug resistance requires an increasingly multidisciplinary approach. A recent workshop on cancer drug resistance, jointly organised by Cancer Research UK, the Rosetrees Trust, and the UKRI-funded Physics of Life Network, brought together experts in cell biology, physical sciences, computational biology, drug discovery, and clinicians to focus on these key challenges and devise interdisciplinary approaches to address them. In this perspective, we review the outcomes of the workshop and highlight unanswered research questions. We outline the emerging hallmarks of drug resistance and discuss lessons from the COVID-19 pandemic and antimicrobial resistance that could help accelerate information sharing and timely adoption of research discoveries into the clinic. We envisage that initiatives that drive greater interdisciplinarity will yield rich dividends in developing new ways to better detect, monitor, and treat drug resistance, thereby improving treatment outcomes for cancer patients.

Exploring Multiplex Immunohistochemistry (mIHC) Techniques and Histopathology Image Analysis: Current Practice and Potential for Clinical Incorporation

BACKGROUND

By simultaneously staining multiple immunomarkers on a single tissue section, multiplexed immunohistochemistry (mIHC) enhances the amount of information that can be observed in a single tissue section and thus can be a powerful tool to visualise cellular interactions directly in the tumour microenvironment. Performing mIHC remains technically and practically challenging, and this technique has many limitations if not properly validated. However, with proper validation, heterogeneity between histopathological images can be avoided.

AIMS

This review aimed to summarize the currently used methods and to propose a standardised method for effective mIHC.

MATERIALS AND METHODS

An extensive literature review was conducted to identify different methods currently in use for mIHC.

RESULTS

Guidelines for antibody selection, panel design, antibody validation and analytical strategies are given. The advantages and disadvantages of each method are discussed.

CONCLUSION

This review summarizes widely used pathology imaging software and discusses the potential for automation of pathology image analysis so that mIHC technology can be a truly powerful tool for research as well as clinical use.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Using digital pathology to analyze the murine cerebrovasculature

Research on the cerebrovasculature may provide insights into brain health and disease. Immunohistochemical staining is one way to visualize blood vessels, and digital pathology has the potential to revolutionize the measurement of blood vessel parameters. These tools provide opportunities for translational mouse model research. However, mouse brain tissue presents a formidable set of technical challenges, including potentially high background staining and cross-reactivity of endogenous IgG. Formalin-fixed paraffin-embedded (FFPE) and fixed frozen sections, both of which are widely used, may require different methods. In this study, we optimized blood vessel staining in mouse brain tissue, testing both FFPE and frozen fixed sections. A panel of immunohistochemical blood vessel markers were tested (including CD31, CD34, collagen IV, DP71, and VWF), to evaluate their suitability for digital pathological analysis. Collagen IV provided the best immunostaining results in both FFPE and frozen fixed murine brain sections, with highly-specific staining of large and small blood vessels and low background staining. Subsequent analysis of collagen IV-stained sections showed region and sex-specific differences in vessel density and vessel wall thickness. We conclude that digital pathology provides a useful tool for relatively unbiased analysis of the murine cerebrovasculature, provided proper protein markers are used.

Effects of various cross-linked collagen scaffolds on wound healing in rats model by deep-learning CNN

Scar tissue is connective tissue formed on the wound during the wound-healing process. The most significant distinction between scar tissue and normal tissue is the appearance of covalent cross-linking and the amount of collagen fibers in the tissue. This study investigates the efficacy of four types of collagen scaffolds in promoting wound healing and regeneration in a Sprague-Dawley murine model-the histomorphology analysis of collagen scaffolds and developing a deep learning model for accurate tissue classification. Four female rats (n = 24) groups received collagen scaffolds prepared through physical and chemical crosslinking. Wound healing progress was evaluated by monitoring granulation tissue formation, collagen matrix organization, and collagen fiber deposition, with histological scoring for quantification-the EDC and HA groups demonstrated enhanced tissue regeneration. The EDC and HA groups observed significant differences in wound regeneration outcomes. Deep-learning CNN models with data augmentation techniques were used for image analysis to enhance objectivity. The CNN architecture featured pre-trained VGG16 layers and global average pooling (GAP) layers. Feature visualization using Grad-CAM heatmaps provided insights into the neural network's focus on specific wound features. The model's AUC score of 0.982 attests to its precision. In summary, collagen scaffolds can promote wound healing in mice, and the deep learning image analysis method we proposed may be a new method for wound healing assessment.

Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology

The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno-oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell-derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up-to-date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment

Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.