Hasil untuk "Pathology"

P. Saukko, B. Knight

The Forensic Autopsy The Pathophysiology of Death The Establishment of Identity of Human Remains The Pathology of Wounds Head and Spinal Injuries Chest and Abdominal Injuries Self-Inflicted Injury Gunshot and Explosion Deaths Transportation Injuries Abuse of Human Rights: Deaths in Custody Burns and Scalds Electrical Fatalities Complications of Injury Suffocation and 'Asphyxia' Fatal Pressure on the Neck Immersion Deaths Neglect, Starvation and Hypothermia Deaths Associated with Sexual offences Deaths Associated with Pregnancy Child Homicide Sudden Death in Infancy Fatal Child Abuse Deaths Associated with Surgical Procedures Dysbaric Fatalities and Barotrauma The Pathology of Sudden Death Forensic Dentistry for the Pathologist Poisoning and the Pathologist Forensic Aspects of Alcohol Carbon Monoxide Poisoning Organophosphorus Poisoning Poisoning by Medicines Death from Narcotic and Hallucinogenic Drugs Corrosive and Metallic Poisoning Deaths from Organic Solvents Appendix

Richard J. Chen, Tong Ding, Ming Y. Lu et al.

Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen et al.

Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content. A data-efficient and interpretable deep-learning method for the multi-class classification of whole-slide images that relies only on slide-level labels is applied to the detection of lymph node metastasis and to cancer subtyping.

S. Tian, Weidong Hu, L. Niu et al.

There is currently a lack of pathologic data on the novel coronavirus (severe acute respiratory syndrome coronavirus 2) pneumonia, or coronavirus disease 2019 (COVID-19), from autopsy or biopsy. Two patients who recently underwent lung lobectomies for adenocarcinoma were retrospectively found to have had COVID-19 at the time of the operation. These two cases thus provide important first opportunities to study the pathology of COVID-19. Pathologic examinations revealed that apart from the tumors, the lungs of both patients exhibited edema, proteinaceous exudate, focal reactive hyperplasia of pneumocytes with patchy inflammatory cellular infiltration, and multinucleated giant cells. Hyaline membranes were not prominent. Because both patients did not exhibit symptoms of pneumonia at the time of operation, these changes likely represent an early phase of the lung pathology of COVID-19 pneumonia.

Gabriele Campanella, M. Hanna, Luke Geneslaw et al.

Ming Y. Lu, Bowen Chen, Drew F. K. Williamson et al.

P. Bankhead, M. Loughrey, José A. Fernández et al.

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

M. Uhlén, Cheng Zhang, Sunjae Lee et al.

Hanwen Xu, N. Usuyama, Jaspreet Bagga et al.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1–3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision–language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling. Prov-GigaPath, a whole-slide pathology foundation model pretrained on a large dataset containing around 1.3 billion pathology images, attains state-of-the-art performance in cancer classification and pathomics tasks.

Zhi Huang, Federico Bianchi, Mert Yuksekgonul et al.

R. Dean, J. V. van Kan, Z. Pretorius et al.

M. Sofroniew, H. Vinters

Astrocytes are specialized glial cells that outnumber neurons by over fivefold. They contiguously tile the entire central nervous system (CNS) and exert many essential complex functions in the healthy CNS. Astrocytes respond to all forms of CNS insults through a process referred to as reactive astrogliosis, which has become a pathological hallmark of CNS structural lesions. Substantial progress has been made recently in determining functions and mechanisms of reactive astrogliosis and in identifying roles of astrocytes in CNS disorders and pathologies. A vast molecular arsenal at the disposal of reactive astrocytes is being defined. Transgenic mouse models are dissecting specific aspects of reactive astrocytosis and glial scar formation in vivo. Astrocyte involvement in specific clinicopathological entities is being defined. It is now clear that reactive astrogliosis is not a simple all-or-none phenomenon but is a finely gradated continuum of changes that occur in context-dependent manners regulated by specific signaling events. These changes range from reversible alterations in gene expression and cell hypertrophy with preservation of cellular domains and tissue structure, to long-lasting scar formation with rearrangement of tissue structure. Increasing evidence points towards the potential of reactive astrogliosis to play either primary or contributing roles in CNS disorders via loss of normal astrocyte functions or gain of abnormal effects. This article reviews (1) astrocyte functions in healthy CNS, (2) mechanisms and functions of reactive astrogliosis and glial scar formation, and (3) ways in which reactive astrocytes may cause or contribute to specific CNS disorders and lesions.

Xiyue Wang, Junhan Zhao, Eliana Marostica et al.

H. Braak, K. Del Tredici, U. Rüb et al.

C. Fletcher, K. Unni, F. Mertens

L. Luna

Ming Y. Lu, Bowen Chen, Drew F. K. Williamson et al.

Computational pathology1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders3,4. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making. PathChat, a multimodal generative AI copilot for human pathology, has been trained on a large dataset of visual-language instructions to interactively assist users with diverse pathology tasks.

Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson et al.

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. Advances in digitizing human tissue slides and progress in artificial intelligence have boosted progress in the field of computational pathology. This Review consolidates recent methodological advances and provides future perspectives as the field expands to take on a broader range of clinical and research tasks. Supported by advances in artificial intelligence, curation of multi-institutional cohorts and the development of high-performance computing, computational pathology is now reaching clinical-grade performance for certain tasks. Artificial intelligence-based methods in computational pathology can be distinguished into methods for predicting clinical end points from tissue specimens and assistive tools for clinical or research tasks. Multiple instance learning is a rapidly growing paradigm for predicting clinical end points, such as disease diagnosis and molecular alterations, from whole-slide images. Computational pathology can be used for automating tasks that pathologists already perform in daily practice and for discovering morphological biomarkers for clinical outcomes of interest. Initiatives for collecting larger, well-curated and multimodal datasets, together with advances in artificial intelligence frameworks, are required for the clinical adoption of computational pathology tools. Supported by advances in artificial intelligence, curation of multi-institutional cohorts and the development of high-performance computing, computational pathology is now reaching clinical-grade performance for certain tasks. Artificial intelligence-based methods in computational pathology can be distinguished into methods for predicting clinical end points from tissue specimens and assistive tools for clinical or research tasks. Multiple instance learning is a rapidly growing paradigm for predicting clinical end points, such as disease diagnosis and molecular alterations, from whole-slide images. Computational pathology can be used for automating tasks that pathologists already perform in daily practice and for discovering morphological biomarkers for clinical outcomes of interest. Initiatives for collecting larger, well-curated and multimodal datasets, together with advances in artificial intelligence frameworks, are required for the clinical adoption of computational pathology tools.

Alejandra Aroca-Crevillén, Tommaso Vicanolo, Samuel Ovadia et al.

Infections, cardiovascular disease, and cancer are major causes of disease and death worldwide. Neutrophils are inescapably associated with each of these health concerns, by either protecting from, instigating, or aggravating their impact on the host. However, each of these disorders has a very different etiology, and understanding how neutrophils contribute to each of them requires understanding the intricacies of this immune cell type, including their immune and nonimmune contributions to physiology and pathology. Here, we review some of these intricacies, from basic concepts in neutrophil biology, such as their production and acquisition of functional diversity, to the variety of mechanisms by which they contribute to preventing or aggravating infections, cardiovascular events, and cancer. We also review poorly explored aspects of how neutrophils promote health by favoring tissue repair and discuss how discoveries about their basic biology inform the development of new therapeutic strategies.

Ekansh Arora

Foundation models are increasingly applied to computational pathology, yet their behavior under cross-cancer and cross-species transfer remains unspecified. This study investigated how fine-tuning CPath-CLIP affects cancer detection under same-cancer, cross-cancer, and cross-species conditions using whole-slide image patches from canine and human histopathology. Performance was measured using area under the receiver operating characteristic curve (AUC). Few-shot fine-tuning improved same-cancer (64.9% to 72.6% AUC) and cross-cancer performance (56.84% to 66.31% AUC). Cross-species evaluation revealed that while tissue matching enables meaningful transfer, performance remains below state-of-the-art benchmarks (H-optimus-0: 84.97% AUC), indicating that standard vision-language alignment is suboptimal for cross-species generalization. Embedding space analysis revealed extremely high cosine similarity (greater than 0.99) between tumor and normal prototypes. Grad-CAM shows prototype-based models remain domain-locked, while language-guided models attend to conserved tumor morphology. To address this, we introduce Semantic Anchoring, which uses language to provide a stable coordinate system for visual features. Ablation studies reveal that benefits stem from the text-alignment mechanism itself, regardless of text encoder complexity. Benchmarking against H-optimus-0 shows that CPath-CLIP's failure stems from intrinsic embedding collapse, which text alignment effectively circumvents. Additional gains were observed in same-cancer (8.52%) and cross-cancer classification (5.67%). We identified a previously uncharacterized failure mode: semantic collapse driven by species-dominated alignment rather than missing visual information. These results demonstrate that language acts as a control mechanism, enabling semantic re-interpretation without retraining.