Pathological Images Research Articles

Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma

Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.

Accurate Pathological Prediction of Small Breast Cancer With Pathological Component-based Image Evaluation: A Case Report

Accurate Pathological Prediction of Small Breast Cancer With Pathological Component-based Image Evaluation: A Case Report

A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism

A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism

DOP107 Real-time intestinal barrier assessment by endocytoscopy and confocal laser endomicroscopy uniquely correlates with multiple barrier protein expression and reflects the gut-brain axis

Abstract Background The intestinal barrier is gaining recognition as a key indicator of mucosal healing in IBD. Nonetheless, its assessment remains challenging. This study evaluates advanced endoscopic tools for real-time gut barrier assessment, examining their correlation with automated epithelial and vascular barrier markers and exploring their potential to elucidate the gut-brain axis. Methods IBD patients undergoing endoscopic assessment or surveillance and healthy controls were included. The intestinal mucosa was assessed with advanced imaging, i.e. ultra-high magnification endocytoscopy (ECS) or confocal laser endomicroscopy (CLE), using newly developed scores for barrier evaluation.1,2 Barrier healing was defined as ECS ≤ 1 -ileum and colon- and CLE ≤ 4 -colon- or ≤ 3 -ileum-. Biopsies were collected from different bowel segments, including representative inflamed and non-inflamed areas. Epithelial and vascular barrier markers, including ZO-1, Claudin-2, E-cadherin, PV-1 and CD-31, were analysed through confocal microscopy, and their expression was automatically quantified using QuPath. The validated IBD disk, focusing on energy, sleep and emotional components, was employed to evaluate neuropsychological functional impairment and indirectly assess gut-brain interaction.3 A per-biopsy-based linear regression model was used for inferential analysis using R software. Results A cohort of 33 IBD patients (15 CD and 18 UC) and 2 healthy controls were recruited (65 biopsies; 3.8 X106 cells). 9/16 (56%) patients who underwent ECS in the colon and 6/9 (67%) in the ileum had ECS scores indicative of barrier healing. CLE assessment revealed features suggestive of barrier healing in 3/11 patients (27%) in the colon and 1/14 (7%) in the ileum. ECS and CLE sub- and total scores demonstrated unique correlation with epithelial and vascular barrier proteins. For the epithelial barrier, CLE total scores in colon and ileum and villi architecture at ECS significantly correlated with Claudin-2 mean expression (p<0.01). Regarding the vascular barrier, a significant correlation was found between ECS-assessed vascular architecture in ileum and colon and PV-1 mean expression (p<0.01). In contrast, CLE-assessed blood flow in the colon was significantly correlated with CD-31 expression (p=0.03). Interestingly, patients with higher levels of sleeping difficulties based on IBD disk showed higher Claudin-2 and lower E-cadherin expression, while those with altered CD-31 expression reported lower energy levels. Conclusion CLE and ECS may offer a unique ability for real-time assessment of barrier impairment in IBD, showing promises for targeting real-time barrier healing and unravelling the complexities of gut-brain axis. References 1Iacucci M, Jeffery L, Acharjee A, et al. Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study. Inflamm Bowel Dis. 2023;29(9):1409-1420. doi:10.1093/ibd/izac233 2Majumder S, Santacroce G, Maeda Y, et al. Endocytoscopy with automated multispectral intestinal barrier pathology imaging for assessment of deep healing to predict outcomes in ulcerative colitis. Gut. 2024;73(10):1603-1606. Published 2024 Sep 9. doi:10.1136/gutjnl-2024-332894 3Le Berre C, Flamant M, Bouguen G, et al. VALIDation of the IBD-Disk Instrument for Assessing Disability in Inflammatory Bowel Diseases in a French Cohort: The VALIDate Study. J Crohns Colitis. 2020;14(11):1512-1523. doi:10.1093/ecco-jcc/jjaa100

IForensic, multicentric validation of digital whole slide images (WSI) in forensic histopathology setting according to the College of American Pathologists guidelines.

Pathology has benefited from the rapid progress of image-digitizing technology during the last decade. However, the application of digital whole slide images (WSI) in forensic pathology still needs to be improved. WSI validation is crucial to ensure diagnostic performance, at least equivalent to glass slides and light microscopy. The College of American Pathologists Pathology and Laboratory Quality Center recently updated internal digital pathology system validation recommendations. Following these guidelines, this pilot study aimed to validate the performance of a digital approach for forensic histopathological diagnosis. Six independent skilled forensic pathologists from different forensic medicine institutes evaluated 100 glass slides of forensic interest (80 stained with standard hematoxylin and eosin, 20 with special staining), including different organs and tissues, with light microscopy (Olympus BX51, Tokyo, Japan). Glass slides were scanned using the AperioGT 450 DX Digital Slides Scanner (Leica Biosystems, Nussloch, Germany). After two wash-out weeks, forensic pathologists evaluated WSIs in front of a widescreen using computer devices with dedicated software (O3 viewer, O3 Enterprise, Zucchetti, Trieste, Italy). Side-by-side comparisons between diagnoses performed on tissue glass slides versus WSIs were above the threshold stated in the validation guidelines (mean concordance of 97.8%). CSUQ Version 3 questionnaire showed high satisfaction for all pathologists (mean result: 6.6/7). Our institutional digital forensic pathology system has been validated for practical casework application. This approach opens new scenarios in practical forensic casework investigations, such as sharing live histological ex-glass slides online, as well as educational and research perspectives, with improving impacts on the whole daily workflow.

Intratumoral microbiota, fatty acid metabolism, and tumor microenvironment constitute an unresolved trinity in colon adenocarcinoma

The intratumoral microbiota, fatty acid metabolism (FAM), and tumor microenvironment (TME) all provide insights into the management of colon adenocarcinoma (COAD). But the biological link among the three remains unclear. Here, we analyzed intratumoral microbiome samples and matched host transcriptome samples from 420 patients with COAD in The Cancer Genome Atlas (TCGA). All patients were divided into two subtypes (FAM_high and FAM_low) based on the Gene set variation analysis (GSVA) score of FAM pathway. Furthermore, we found significant difference in the intratumoral microbiota signatures between the two subtypes. In-depth analysis suggested that specific microbes in tumors may indirectly modify the TME, particularly stromal cell populations, by modulating the FAM process. More importantly, the crosstalk between the three can have a significant impact on prognosis, response to immunotherapy, and drug sensitivity of patients. Pathological image profiling showed that changes in the TME originating from intratumoral microbiota disturbance could be reflected in pathological image features. In summary, our study provides novel insights into the biological links among the intratumoral microbiota, FAM, and the TME in COAD, and offer guidance for the therapeutic opportunities that target intratumoral microbes.

Guidelines for standard operation of imaging modalities in orbital diseases (2024).

Orbital disorders include conditions originating from the orbital bones, surrounding tissues, and post-orbital septum. They also include systemic ailments affecting the orbit. Different clinical symptoms make up the complex range of orbital disorders. Because these disorders mostly impact the orbital area instead of the intraocular compartment, there is little diagnostic usefulness for typical ophthalmic visual tests. As such, the vital instruments for diagnosing and evaluating orbital illnesses have become ophthalmic imaging modalities, including ocular ultrasonography (B-scan), computed tomography (CT), and magnetic resonance imaging (MRI). One way to improve the precision and promptness of diagnosing orbital diseases is to standardize the functioning of widely used imaging equipment and define the radiological features of orbital abnormalities. Such programs are crucial for the care of patients with orbital disorders since they considerably reduce the number of misdiagnoses and missed diagnoses in these individuals. The underlying concepts, operational techniques, and normal and pathological imaging findings associated with common diagnostic tools for orbital illnesses are all thoroughly reviewed in this guideline. The objective is to improve primary healthcare settings' diagnostic competence in the field of orbital pathology and to standardize procedures for diagnosing orbital disorders.

Intelligent classification of lung cancer pathology images through comparative morphological feature learning

Lung cancer pathology images are categorized with enhanced accuracy in this research, addressing the significant challenge posed by the limited availability of labeled images, a limitation exacerbated by the complexity of cellular morphologies. Background The accurate classification of lung cancer pathology images is of paramount importance for both diagnostic and therapeutic purposes. However, the development of robust classification models is often hindered by the intricate cellular morphologies and the scarcity of labeled images, which is a critical bottleneck in the field. Objectives The study is designed to incorporate unlabeled data into the training process, thereby enhancing the classification of lung cancer pathology images through the use of comparative learning techniques. Methods A methodology is introduced wherein confidently classified unlabeled images are integrated with labeled ones, enriching the training dataset. This approach draws on principles of farthest and nearest neighbor contrastive learning to cultivate a more challenging learning environment and to augment the variability of contrastive samples. To effectively extract key cellular morphological features, an encoder based on the ResNet50 architecture, fortified with deformable and dynamic convolutional techniques, is utilized. Results Demonstrated by experimental results, the proposed classification strategy achieves a significant improvement in the accuracy of lung cancer image classification, even under conditions characterized by a limited availability of labeled data, thus underscoring the robustness of the method. Conclusion The integration of comparative learning with both labeled and unlabeled images, complemented by the application of advanced convolutional techniques, is shown to be a promising avenue for enhancing the classification of lung cancer pathology images. This research is presented as a practical solution to the urgent need for accurate and efficient diagnostic tools in the field of oncology.

Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study.

Pancreatic cancer remains one of the most lethal malignancies worldwide, with a poor prognosis often attributed to late diagnosis. Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning. To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features. We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution. Pathological types were determined by histopathological examination of the surgical specimens or biopsy samples. The imaging features were assessed using computed tomography, magnetic resonance imaging, and endoscopic ultrasound. Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics. There were 320 (64%) cases of pancreatic ductal adenocarcinoma, 75 (15%) of intraductal papillary mucinous neoplasms, 50 (10%) of neuroendocrine tumors, and 55 (11%) of other rare types. Distinct imaging features were identified in each pathological type. Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography, whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules. Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging. Statistical analysis revealed significant correlations between specific imaging features and pathological types (P < 0.001). This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features. These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

Investigating lung cancer microenvironment from cell segmentation of pathological image and its application in prognostic stratification

Lung cancer, particularly adenocarcinoma, ranks high in morbidity and mortality rates worldwide, with a relatively low five-year survival rate. To achieve precise prognostic assessment and clinical intervention for patients, thereby enhancing their survival prospects, there is an urgent need for more accurate stratification schemes. Currently, the TNM staging system is predominantly used in clinical practice for prognostic evaluation, but its accuracy is constrained by the reliance on physician experience. Although biomarker discovery based on molecular pathology offers a new perspective for prognostic assessment, its dependence on expensive gene panel testing limits its widespread clinical application. Pathological images contain abundant diagnostic information, providing a new avenue for prognostic evaluation. In this study, we employed advanced Hover-Net technology to accurately quantify the abundance of epithelial cells, lymphocytes, macrophages, and neutrophils from pathological images, and delved into the clinical and biological significance of these cellular abundances. Our research findings reveal that, in contrast to patients classified as N0 stage, those belonging to the N1 stage demonstrated a marked elevation in the infiltration of epithelial cells, lymphocytes, macrophages, and neutrophils. Notably, the infiltration patterns of lymphocytes and neutrophils exhibited an inverse relationship with the activation status of numerous pivotal gene pathways, including the HALLMARK_HEME_METABOLISM pathway. Furthermore, our analysis distinguished FABP7 as a prognostic biomarker, exhibiting pronounced differential expression between patients with high and low levels of neutrophil infiltration, indicate that cellular abundance analysis based on pathological images can provide a more accurate and cost-effective prognostic evaluation, offering new strategies for the clinical management of lung adenocarcinoma.

A vision-language foundation model for precision oncology.

Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care1,2. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models. In this study, we developed the Multimodal transformer with Unified maSKed modeling (MUSK), a vision-language foundation model designed to leverage large-scale, unlabelled, unpaired image and text data. MUSK was pretrained on 50 million pathology images from 11,577 patients and one billion pathology-related text tokens using unified masked modelling. It was further pretrained on one million pathology image-text pairs to efficiently align the vision and language features. With minimal or no further training, MUSK was tested in a wide range of applications and demonstrated superior performance across 23 patch-level and slide-level benchmarks, including image-to-text and text-to-image retrieval, visual question answering, image classification and molecular biomarker prediction. Furthermore, MUSK showed strong performance in outcome prediction, including melanoma relapse prediction, pan-cancer prognosis prediction and immunotherapy response prediction in lung and gastro-oesophageal cancers. MUSK effectively combined complementary information from pathology images and clinical reports and could potentially improve diagnosis and precision in cancer therapy.

Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.

Colorectal cancer (CRC) is one of the most common and deadly forms of cancer worldwide, necessitating accurate and early detection to improve treatment outcomes. Traditional diagnostic methods often rely on manual examination of pathological images, which can be time-consuming and prone to human error. This study presents an advanced approach for colorectal cancer detection using a Random Hinge Exponential Distribution coupled Attention Network (RHED-CANet) on pathological images. The input dataset is sourced from the TCGA-CRC-DX cohort and the CRC dataset, both widely recognized for their comprehensive coverage of colorectal cancer cases. Pre-processing and feature extraction are performed using a Modified Square Root Sage-Husa Adaptive Kalman Filter combined with a Spike-Driven Transformer, enhancing noise reduction and feature clarity. Segmentation is achieved through an EfficientNetV2L Inception Transformer, ensuring precise delineation of cancerous regions. The final classification utilizes the RHED-CANet, a network tailored to handle the complexities of pathological data with high accuracy. This methodology achieved remarkable results, with an accuracy of 99.9% and a precision of 99.7%. These performance metrics underscore the method's ability to minimize false positives and enhance diagnostic accuracy. The proposed approach offers significant advantages, including a reduction in diagnostic time and a substantial improvement in detection accuracy, making it a promising tool for clinical applications. Despite its excellent accuracy, the suggested RHED-CANet technique has drawbacks, such as overfitting the TCGA-CRC-DX and CRC datasets by reducing generalizability on other datasets comprising other cancer types or image qualities. The actual application of the techniques in real-time clinical applications may be hampered by this computational load, especially in settings with limited resources, and the model's potential computational complexity due to multiple advanced processing steps. Additionally, the efficiency of training may be impacted by biased inputs, particularly for minor CRC subtypes.

Using HALO digital image analysis for automated detection of bovine viral diarrhea virus antigen in ear-notch specimens.

Detecting calves that are persistently infected with bovine viral diarrhea virus (BVDV) is essential to disease prevention. Immunohistochemistry (IHC) performed on formalin-fixed, paraffin-embedded ear-notch samples is commonly used for surveillance detection of BVDV antigens. However, due to the low percentage of positive samples in most submissions, the current workflow often entails considerable time reviewing negative results. Herein we aimed to utilize digital pathology and whole-slide imaging, coupled with advanced image analysis software, to enhance the efficiency of positive IHC detection in surveillance. Despite some challenges encountered during the implementation phase, the benefits of the reduced potential for human error and significant time savings for technicians and pathologists are evident. The screening of 518 slides, containing 2,884 ear notches, reached 97.4% sensitivity and 89.4% specificity compared to the gold standard of direct human assessment. The time taken for the personnel to operate the software and organize results was significantly shorter than the time needed for technicians and pathologists to manually examine the slides. Future refinements in software integration, staining protocols, and QC measures promise to further optimize this approach.

Retrospective analysis of amantadine response and predictive factors in intensive care unit patients with non-traumatic disorders of consciousness.

Disorders of consciousness (DoC) in non-traumatic ICU-patients are often treated with amantadine, although evidence supporting its efficacy is limited. This retrospective study analyzed non-traumatic DoC-patients treated with amantadine between January 2016 and June 2021. Data on patient demographics, clinical characteristics, treatment specifications, and outcomes were extracted from electronic medical records. Patients were classified as responders if their Glasgow Coma Scale (GCS) improved by ≥3 points within 5 days. Good outcome was defined as a modified Rankin Scale (mRS) of 0-2. Machine learning techniques were used to predict response to treatment. Of 442 patients (mean age 73.2 ± 10.7 years, 41.0% female), 267 (60.4%) were responders. Baseline characteristics were similar between groups, except that responders had lower baseline GCS (7 [IQR 5-9] vs. 8 [IQR 5-10], p = 0.030), better premorbid mRS (2 [IQR 1-2] vs. 2 [IQR 1-3], p < 0.001) and fewer pathological cerebral imaging findings (45.7% vs. 61.1%, OR 0.56, 95% CI: 0.36-0.86, p = 0.008). Responders exhibited significantly lower mortality at discharge (13.5% vs. 27.4%, OR 0.41, 95% CI: 0.25-0.67, p < 0.001) and follow-up (16.9% vs. 32.0%, OR 0.43, 95% CI: 0.24-0.77, p = 0.002). Good outcomes were more frequent in responders at follow-up (4.9% vs. 1.1%, OR 6.14, 95% CI: 1.35-28.01, p = 0.004). In multivariate analysis higher premorbid mRS (OR 0.719, 95% CI 0.590-0.875, p < 0.001), pathological imaging results (OR 0.546, 95% CI 0.342-0.871, p = 0.011), and experiencing cardiac arrest (OR 0.542, 95% CI 0.307-0.954, p = 0.034) were associated with lower odds of response. Machine learning identified key predictors of response, with the Stacking Classifier achieving the highest performance (accuracy 64.5%, precision 66.6%, recall 64.5%, F1 score 61.3%). This study supports the potential benefits of intravenous amantadine in non-traumatic DOC-patients. Higher premorbid mRS, and pathological cerebral imaging were key predictors of non-response, offering potential avenues for patient selection and treatment customization. Findings from this study informed the design of our ongoing prospective study, which aims to further evaluate the long-term efficacy of amantadine.

Current Progress and Future Directions in Non-Alzheimer's Disease Tau PET Tracers.

Alzheimer's disease (AD) and non-AD tauopathies are dominant public health issues driven by several factors, especially in the aging population. The discovery of first-generation radiotracers, including [18F]FDDNP, [11C]PBB3, [18F]flortaucipir, and the [18F]THK series, for the in vivo detection of tauopathies has marked a significant breakthrough in the fields of neuroscience and radiopharmaceuticals, creating a robust new category of labeled compounds: tau positron emission tomography (PET) tracers. Subsequently, other tau PET tracers with improved binding properties have been developed using various chemical scaffolds to target the three-repeat/four-repeat (3R/4R) tau folds in AD. In 2020, [18F]flortaucipir was approved by the U.S. Food and Drug Administration for PET imaging of tau pathology in adult patients with cognitive deficits undergoing evaluation for AD. Despite remarkable progress in the development of AD tau PET tracers, imaging agents for rare non-AD tauopathies (4R tauopathies [predominantly expressing a 4R tau isoform], involved in progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease, and globular glial tauopathy, and 3R tauopathies [predominantly expressing a 3R tau isoform], such as Pick's disease) remain substantially underdeveloped. In this review, we discuss recent progress in tau PET tracer development, with particular emphasis on clinically validated tracers for AD and their potential use for non-AD tauopathies. Additionally, we highlight the critical need for further development of tau PET tracers specifically designed for non-AD tauopathies, an area that remains significantly underexplored despite its importance in advancing the understanding and diagnosis of these disorders.

Mapping theme trends and recognizing hot spots in acute spinal cord injury:a bibliometric analysis.

Acute spinal cord injury causes severe motor and sensory dysfunction, significantly burdening individuals and society. This study uses bibliometric analysis to identify research trends and key areas, providing insights for future advancements in treatment. Scientific publications on acute spinal cord injury were collected from PubMed and the Web of Science Core Collection (WoSCC) between 2000 and 2022. Data were analyzed using Bibliometric, CiteSpace, and BICOMB, with gCLUTO applied for co-word bicluster analysis based on MeSH term matrices. 2,513 publications on acute spinal cord injury were published, with the number of articles increasing annually from 38 to 268. Spinal Cord has emerged as the leading journal in this field, and the United States maintains its dominant position in global research impact. The University of Toronto ranks first among research institutions, with significant contributions from researchers such as Fehlings and Kwon. Research on acute spinal cord injury primarily focuses on seven key areas: metabolism, pharmacology, surgical timing, rehabilitation, pathology, clinical predictors, and diagnostic imaging. Our study reveals substantial growth in acute spinal cord injury research over the past two decades, emphasizing leading countries, researchers, institutions, and journals. Animal models remain pivotal in drug development for basic medicine and neuroscience. Consensus has been reached among experts regarding the timing of surgical intervention, while artificial intelligence and multidisciplinary approaches are emerging as promising avenues for comprehensive treatment. Additionally, Ongoing research into spinal cord injury pathophysiology provides essential guidance for pharmacological and surgical treatments.

Label credibility correction based on cell morphological differences for cervical cells classification

Cervical cancer is one of the deadliest cancers that pose a significant threat to women’s health. Early detection and treatment are commonly used methods to prevent cervical cancer. The use of pathological image analysis techniques for the automatic interpretation of cervical cells in pathological slides is a prominent area of research in the field of digital medicine. According to The Bethesda System, cervical cytology necessitates further classification of precancerous lesions based on positive interpretations. However, clinical definitions among different categories of lesion are complex and often characterized by fuzzy boundaries. In addition, pathologists can deduce different criteria for judgment based on The Bethesda System, leading to potential confusion during data labeling. Noisy labels due to this reason are a great challenge for supervised learning. To address the problem caused by noisy labels, we propose a method based on label credibility correction for cervical cell images classification network. Firstly, a contrastive learning network is used to extract discriminative features from cell images to obtain more similar intra-class sample features. Subsequently, these features are fed into an unsupervised method for clustering, resulting in unsupervised class labels. Then unsupervised labels are corresponded to the true labels to separate confusable and typical samples. Through a similarity comparison between the cluster samples and the statistical feature centers of each class, the label credibility analysis is carried out to group labels. Finally, a cervical cell images multi-class network is trained using synergistic grouping method. In order to enhance the stability of the classification model, momentum is incorporated into the synergistic grouping loss. Experimental validation is conducted on a dataset comprising approximately 60,000 cells from multiple hospitals, showcasing the effectiveness of our proposed approach. The method achieves 2-class task accuracy of 0.9241 and 5-class task accuracy of 0.8598. Our proposed method achieves better performance than existing classification networks on cervical cancer.

Deep learning methods for improving the accuracy and efficiency of pathological image analysis.

This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap generation algorithm, which leverages meticulous image preprocessing, data enhancement strategies, ensemble learning, attention mechanisms, and deep feature fusion techniques. This algorithm not only produces highly accurate and interpretatively rich heatmaps but also significantly improves the accuracy and efficiency of pathological image analysis. Unlike existing methods, our approach integrates these advanced techniques into a cohesive framework, enhancing its ability to reveal critical features in pathological images. Rigorous experimental validation demonstrated that our algorithm excels in key performance indicators such as accuracy, recall rate, and processing speed, underscoring its potential for broader applications in pathological image analysis and beyond.

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

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. This review aimed to summarize the currently used methods and to propose a standardised method for effective mIHC. An extensive literature review was conducted to identify different methods currently in use for mIHC. Guidelines for antibody selection, panel design, antibody validation and analytical strategies are given. The advantages and disadvantages of each method are discussed. 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.

A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images

A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images