Air stability of monolayer WSi2N4 in dark and bright conditions.

Two-dimensional materials with chemical formula MA2Z4 are a promising class of materials for optoelectronic applications. To exploit their potential, their stability with respect to air pollution has to be analyzed under different conditions. In a first-principle study based on density functional theory, we investigate the adsorption of three common environmental gas molecules (O2, H2O, and CO2) on monolayer WSi2N4, an established representative of the MA2Z4 family. The computed adsorption energies, charge transfer, and projected density of states of the polluted monolayer indicate a relatively weak interaction between substrate and molecules resulting in an ultrashort recovery time of the order of nanoseconds. O2 and water introduce localized states in the upper valence region but do not alter the semiconducting nature of WSi2N4 nor its band-gap size apart from a minor variation of a few tens of meV. Exploring the same scenario in the presence of photogenerated electrons and holes, we do not notice any substantial difference except for O2 chemisorption when negative charge carriers are in the system. In this case, monolayer WSi2N4 exhibits signs of irreversible oxidation, testified by an adsorption energy of -5.5 eV leading to an infinitely long recovery time, a rearrangement of the outermost atomic layer bonding with the pollutant, and n-doping of the system. Our results indicate stability of WSi2N4 against H2O and CO2 in both dark and bright conditions, suggesting the potential of this material in nanodevice applications.

High-Throughput Monitoring of Pathogenic Fungal Growth Using Whole Slide Imaging for Rapid Antifungal Susceptibility Assessment.

Invasive fungal infections are a major health threat with high morbidity and mortality, highlighting the urgent need for rapid diagnostic tools to detect antifungal resistance. Traditional culture-based antifungal susceptibility testing (AFST) methods often fall short due to their lengthy process. In our previous research, we developed a whole-slide imaging (WSI) technique for the high-throughput assessment of bacterial antibiotic resistance. Building on this foundation, this study expands the application of WSI by adapting it for rapid AFST through high-throughput monitoring of the growth of hundreds of individual fungi. Due to the distinct "budding" growth patterns of fungi, we developed a unique approach that utilizes specific cell number change to determine fungi replication, instead of cell area change used for bacteria in our previous study, to accurately determine the growth rates of individual fungal cells. This method not only accelerates the determination of antifungal resistance by directly observing individual fungal cell growth, but also yields accurate results. Employing Candida albicans as a representative model organism, reliable minimum inhibitory concentration (MIC) of fluconazole inhibiting 100% cells of Candida albicans (denoted as MIC100) was obtained within 3h using the developed method, while the modified broth dilution method required 72h for the similar reliable result. In addition, our approach was effectively utilized to test blood culture samples directly, eliminating the need to separate the fungi from whole blood samples spiked with Candida albicans. These features indicate the developed method holds great potential serving as a general tool in rapid antifungal susceptibility testing and MIC determination.

Digital Pathology for Better Clinical Practice.

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

[Artificial intelligence in pathological anatomy]. / Iskusstvennyi intellekt v patologicheskoi anatomii.

The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence. The article also discusses three options for the further development of AI assistants for pathologists based on the technologies of large language models (strong AI) ChatGPT (PathAsst), Flan-PaLM2 and LIMA. As a result of the analysis of the literature, key problems in the field were identified: the equipment of pathology institutions, the lack of experts in training neural networks, the lack of strict criteria for the clinical viability of AI diagnostic technologies.

RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge.

BACKGROUND AND OBJECTIVE: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. METHODS: We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The registration time is low, allowing one to perform efficient registration even for large datasets. The method was proposed for the ACROBAT 2023 challenge organized during the MICCAI 2023 conference and scored 1st place. The method is released as open-source software. RESULTS: The proposed method is evaluated using three open datasets: (i) Automatic Nonrigid Histological Image Registration Dataset (ANHIR), (ii) Automatic Registration of Breast Cancer Tissue Dataset (ACROBAT), and (iii) Hybrid Restained and Consecutive Histological Serial Sections Dataset (HyReCo). The target registration error (TRE) is used as the evaluation metric. We compare the proposed algorithm to other state-of-the-art solutions, showing considerable improvement. Additionally, we perform several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset. CONCLUSIONS: The article presents an automatic and robust registration method that outperforms other state-of-the-art solutions. The method does not require any fine-tuning to a particular dataset and can be used out-of-the-box for numerous types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level (resolution up to 220k x 220k). We provide free access to the software. The results are fully and easily reproducible. The proposed method is a significant contribution to improving the WSI registration quality, thus advancing the field of digital pathology.

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression.

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i.e., survival prediction (by 4.9% on C-index), pathological stage classification (by 11.6% on accuracy), and lymph node classification (by 12.0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features.

Expectations and Experiences Among Clinical Staff Regarding Implementation of Digital Pathology: A Qualitative Study at Two Departments of Pathology.

The aim of this study was to assess and evaluate the individual expectations and experiences regarding the implementation of digital pathology (DIPA) among clinical staff in two of the pathology departments in the Region of Southern Denmark before and during the implementation in their department. Seventeen semi-structured interviews based upon McKinsey 7-S framework were held both prior to and during implementation with both managers and employees at the two pathology departments. The interviewees were pathologists, medical doctors in internship in pathology (interns), biomedical laboratory scientists (BLS), secretaries, and a project lead. Using deductive and inductive coding resulted in five overall themes and appertaining sub-themes. The findings pointed to an overall positive attitude towards DIPA from the beginning. The clinical staff perceived being rewarded already during implementation with benefits such as improved collaboration both inter- and intra-departmentally promoting better acceptance of DIPA. The clinical staff also experienced some challenges, e.g., increase in turnaround times, which affected and concerned staff on a personal level. Especially BLS expressed experiencing a demanding and stressful transition due to unexpected increase in workload as well as some barriers for a potentially better implementation process. The key findings of this study were a need for better preparation of staff through transparent communication of the upcoming challenges of the transition to DIPA, more system-specific training beforehand, more allocation of time and resources in the implementation process, and more focus on BLS' work tasks in the requirement specifications.

Benchmarking digital displays (monitors) for histological diagnoses: the nephropathology use case.

AIM: The digital transformation of the pathology laboratory is being continuously sustained by the introduction of innovative technologies promoting whole slide image (WSI)-based primary diagnosis. Here, we proposed a real-life benchmark of a pathology-dedicated medical monitor for the primary diagnosis of renal biopsies, evaluating the concordance between the 'traditional' microscope and commercial monitors using WSI from different scanners. METHODS: The College of American Pathologists WSI validation guidelines were used on 60 consecutive renal biopsies from three scanners (Aperio, 3DHISTECH and Hamamatsu) using pathology-dedicated medical grade (MG), professional grade (PG) and consumer-off-the-shelf (COTS) monitors, comparing results with the microscope diagnosis after a 2-week washout period. RESULTS: MG monitor was faster (1090 vs 1159 vs 1181 min, delta of 6-8%, p<0.01), with slightly better performances on the detection of concurrent diseases compared with COTS (κ=1 vs 0.96, 95% CI=0.87 to 1), but equal concordance to the commercial monitors on main diagnosis (κ=1). Minor discrepancies were noted on specific scores/classifications, with MG and PG monitors closer to the reference report (r=0.98, 95% CI=0.83 to 1 vs 0.98, 95% CI=0.83 to 1 vs 0.91, 95% CI=0.76 to 1, κ=0.93, 95% CI=077 to 1 vs 0.93, 95% CI=0.77 to 1 vs 0.86, 95% CI=0.64 to 1, κ=1 vs 0.50, 95% CI=0 to 1 vs 0.50, 95% CI=0 to 1, for IgA, antineutrophilic cytoplasmic antibody and lupus nephritis, respectively). Streamlined Pipeline for Amyloid detection through congo red fluorescence Digital Analysis detected amyloidosis on both monitors (4 of 30, 13% cases), allowing detection of minimal interstitial deposits with slight overestimation of the Amyloid Score (average 6 vs 7). CONCLUSIONS: The digital transformation needs careful assessment of the hardware component to support a smart and safe diagnostic process. Choosing the display for WSI is critical in the process and requires adequate planning.

Computational pathology: A survey review and the way forward.

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access.

Background: Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. Methods: WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 µm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). Results: A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection "Whole Slide Images as Non-fungible Tokens Project" on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. Conclusion: Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

Accuracy of frozen section remote subspecialty consultation using real-time telepathology and whole-slide imaging in gynecologic cases.

OBJECTIVES: Intrapathology consultation is recommended for complex cases during frozen section (FS) as routine practice. In our institution, solicited second opinions were traditionally provided by in-person consultation (IPC). Whole-slide imaging (WSI) was implemented in 2018 as an alternative but replaced by videoconferencing in 2020. Here, we assess the accuracy of remote FS consultation using these digital modalities vs IPC. METHODS: Gynecologic FS cases over a 4-year period overseen by 2 intraoperative consultants were grouped by consultation method: (1) IPC, (2) WSI, and (3) videoconferencing. Accuracy was determined by concordance between the FS and final report diagnoses. Turnaround time between the 3 groups was analyzed using SPSS statistical software (IBM). RESULTS: Using WSI and videoconferencing, 100% concordance was observed, while the IPC group had a 98.5% concordance rate. Videoconferencing, however, showed longer turnaround times (mean, 45.59 minutes) than IPC (mean, 33.36 minutes). Although turnaround time positively correlated with the number of FS specimens, blocks, and H&E slides per case, no statistically significant differences in the number of specimens, blocks, and H&E slides generated were found among the consultation methods. CONCLUSIONS: Even though turnaround time using videoconferencing is longer, the accuracy of WSI and videoconferencing for remote FS consultation is equivalent to IPC. It is therefore a safe method for conducting intrapathology FS consultation in challenging surgical cases.

Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging.

With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 µm in length by 0.5-1 µm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ensure optimal treatment and best possible patient outcome. Artificial intelligence (AI) in the form of deep neural networks has recently shown enormous potential in the field of medicine including pathology, where we and others have found common cutaneous tumors can be detected with high sensitivity and specificity. To become a widely applied tool, AI approaches will also need to reliably detect and distinguish less common tumor entities including the diverse group of cutaneous adnexal tumors. METHODS: To assess the potential of AI to recognize cutaneous adnexal tumors, we selected a diverse set of these entities from five German centers. The algorithm was trained with samples from four centers and then tested on slides from the fifth center. RESULTS: The neural network was able to differentiate 14 different cutaneous adnexal tumors and distinguish them from more common cutaneous tumors (i.e. basal cell carcinoma and seborrheic keratosis). The total accuracy on the test set for classifying 248 samples into these 16 diagnoses was 89.92 %. Our findings support AI can distinguish rare tumors, for morphologically distinct entities even with very limited case numbers (< 50) for training. CONCLUSION: This study further underlines the enormous potential of AI in pathology which could become a standard tool to aid pathologists in routine diagnostics in the foreseeable future. The final diagnostic responsibility will remain with the pathologist.

A mixed-methods study on the use of a nongynecological virtual microscopy platform in cytology education.

OBJECTIVES: Expanding the virtual microscopy (VM) slide collection for nongynecological cytology is important to teaching. In a mixed-methods approach, this study evaluated VM's performance and user experience to determine its feasibility and usage in an educational setting. METHODS: From September through October 2022, the students reviewed 44 whole-slide imaged nongynecological slides. The concordance rate with reference diagnosis was compared with that from light microscopy (LM) from 4 months earlier. In addition to assessing the overall performance from VM, imaged urinary cytology's accuracy was reviewed for both urinary and nonurinary cytology. Finally, the students' weekly feedback logs were analyzed to gain insights for improving the digital screening experience. RESULTS: The overall nongynecological diagnostic accuracy was significant between the 2 screening platforms (P < .001), favoring LM over the VM platform. Light microscopy also performed better than VM in urine cytology cases, with 84.2% concordance against reference diagnosis, compared with 61.1% for the VM platform (P = .03). As for the accuracy of nonurinary cases, its glass slide (LM) agreement with the reference diagnosis was also superior at 84.8%, compared with 58.8% for VM (P = .03). Finally, the overarching theme discerned from reviewing the user logs was concern over image quality, which was mentioned 76 times. CONCLUSIONS: The VM results were poorer compared with LM in our validation. Its use seems promising, but more focus is needed to improve the VM screening platform.

Color Standardization and Stain Intensity Calibration for Whole Slide Image-Based Immunohistochemistry Assessment.

Automated quantification of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) using whole slide imaging (WSI) is expected to eliminate subjectivity in visual assessment. However, the color intensity in WSI varies depending on the staining process and scanner device. Such variations affect the image analysis results. This paper presents methods to diminish the influence of color variation produced in the staining process using a calibrator slide consisting of peptide-coated microbeads. The calibrator slide is stained along with tissue sample slides, and the 3,3'-diaminobenzidine (DAB) color intensities of the microbeads are used for calibrating the color variation of the sample slides. An off-the-shelf image analysis tool is employed for the automated assessment, in which cells are classified by the thresholds for the membrane staining. We have adopted two methods for calibrating the color variation based on the DAB color intensities obtained from the calibrator slide: (1) thresholds for classifying the DAB membranous intensity are adjusted, and (2) the color intensity of WSI is corrected. In the experiment, the calibrator slides and tissue of breast cancer slides were stained together on different days and used to test our protocol. With the proposed protocol, the discordance in the HER2 evaluation was reduced to one slide out of 120 slides.

SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images.

BACKGROUND: Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural network encodes each tile. Many recent works use supervised attention-based models to aggregate tile-level features into a slide-level representation, which is then used for downstream analysis. Training supervised attention-based models is computationally intensive, architecture optimization of the attention module is non-trivial, and labeled data are not always available. Therefore, we developed an unsupervised and fast approach called SAMPLER to generate slide-level representations. METHODS: Slide-level representations of SAMPLER are generated by encoding the cumulative distribution functions of multiscale tile-level features. To assess effectiveness of SAMPLER, slide-level representations of breast carcinoma (BRCA), non-small cell lung carcinoma (NSCLC), and renal cell carcinoma (RCC) WSIs of The Cancer Genome Atlas (TCGA) were used to train separate classifiers distinguishing tumor subtypes in FFPE and frozen WSIs. In addition, BRCA and NSCLC classifiers were externally validated on frozen WSIs. Moreover, SAMPLER's attention maps identify regions of interest, which were evaluated by a pathologist. To determine time efficiency of SAMPLER, we compared runtime of SAMPLER with two attention-based models. SAMPLER concepts were used to improve the design of a context-aware multi-head attention model (context-MHA). FINDINGS: SAMPLER-based classifiers were comparable to state-of-the-art attention deep learning models to distinguish subtypes of BRCA (AUC = 0.911 ± 0.029), NSCLC (AUC = 0.940 ± 0.018), and RCC (AUC = 0.987 ± 0.006) on FFPE WSIs (internal test sets). However, training SAMLER-based classifiers was >100 times faster. SAMPLER models successfully distinguished tumor subtypes on both internal and external test sets of frozen WSIs. Histopathological review confirmed that SAMPLER-identified high attention tiles contained subtype-specific morphological features. The improved context-MHA distinguished subtypes of BRCA and RCC (BRCA-AUC = 0.921 ± 0.027, RCC-AUC = 0.988 ± 0.010) with increased accuracy on internal test FFPE WSIs. INTERPRETATION: Our unsupervised statistical approach is fast and effective for analyzing WSIs, with greatly improved scalability over attention-based deep learning methods. The high accuracy of SAMPLER-based classifiers and interpretable attention maps suggest that SAMPLER successfully encodes the distinct morphologies within WSIs and will be applicable to general histology image analysis problems. FUNDING: This study was supported by the National Cancer Institute (Grant No. R01CA230031 and P30CA034196).

Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images.

Muscle-invasive bladder cancer (MIBC) is a highly heterogeneous and costly disease with significant morbidity and mortality. Understanding tumor histopathology leads to tailored therapies and improved outcomes. In this study, we employed a weakly supervised learning and neural architecture search to develop a data-driven scoring system. This system aimed to capture prognostic histopathological patterns observed in H&E-stained whole-slide images. We constructed and externally validated our scoring system using multi-institutional datasets with 653 whole-slide images. Additionally, we explored the association between our scoring system, seven histopathological features, and 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The adjusted hazard ratio for overall mortality was 1.46 (95% CI 1.05-2.02; z: 2.23; p = 0.03), thus identifying two prognostic subgroups in high-grade MIBC. Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, and several gene mutations, including FGFR3 in MIBC. Our findings underscore the risk of confounding bias when reducing the complex biological and clinical behavior of tumors to a single mutation. Histopathological changes can only be fully captured through comprehensive multi-omics profiles. The introduction of our scoring system has the potential to enhance daily clinical decision making for MIBC. It facilitates shared decision making by offering comprehensive and precise risk stratification, treatment planning, and cost-effective preselection for expensive molecular characterization.

The Content of Bioactive Compounds and Technological Properties of Matcha Green Tea and Its Application in the Design of Functional Beverages.

Matcha is a powdered green tea obtained from the Camellia sinensis L. plant intended for both "hot" and "cold" consumption. It is a rich source of bioactive ingredients, thanks to which it has strong antioxidant properties. In this research, an organoleptic evaluation was carried out, and the physical characteristics (i.e., instrumental color measurement (L*a*b*), water activity, water solubility index (WSI), water holding capacity (WHC) of 10 powdered Matcha green teas, and in the 2.5% Matcha water solutions, pH, °Brix and osmolality were tested. Also, the content of phenolic ingredients, i.e., selected phenolic acids, flavonoids and total polyphenols, was assessed. The content of chlorophyll, vitamin C and antioxidant potential were also examined. Matcha M-4 was used to design two functional model beverages, in the form of ready-to-use powdered drinks, consisting of Matcha green tea, protein preparations, inulin, maltodextrin and sugar. The obtained powdered drink, when dissolved in the preferred liquid (water, milk, juice), is regenerative, high-protein and rich in bioactive ingredients from the Matcha drink, with prebiotic properties derived from the added inulin. The beverage is also characterized by low osmolality. It can be recommended as a regenerating beverage for a wide group of consumers, athletes and people with deficiencies, among others protein, and elderly people, as well as in the prevention and supportive treatment of bone and joint tissue diseases.

Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases.

BACKGROUND: The establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs. DESIGN: Hematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses. RESULTS: The accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of "fixed" workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance. CONCLUSION: Most features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists' preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists' display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard "unlocked" vendor-agnostic displays for clinical digital pathology workflow rather than purchasing "locked" and more expensive displays that are part of a digital pathology system.