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The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale., (© The Author(s) 2024. Published by Oxford University Press.)

The practice of cytopathology has been significantly refined in recent years, largely through the creation of consensus rule sets for the diagnosis of particular specimens (Bethesda, Milan, Paris, and so forth). In general, these diagnostic systems have focused on reducing intraobserver variance, removing nebulous/redundant categories, reducing the use of "atypical" diagnoses, and promoting the use of quantitative scoring systems while providing a uniform language to communicate these results. Computational pathology is a natural offshoot of this process in that it promises 100% reproducible diagnoses rendered by quantitative processes that are free from many of the biases of human practitioners., Competing Interests: Disclosure Nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Thyroid cytology is a rapidly evolving field that has seen significant advances in recent years. Its main goal is to accurately diagnose thyroid nodules, differentiate between benign and malignant lesions, and risk stratify nodules when a definitive diagnosis is not possible. The current landscape of thyroid cytology includes the use of fine-needle aspiration for the diagnosis of thyroid nodules with the use of uniform, tiered reporting systems such as the Bethesda System for Reporting Thyroid Cytopathology. In recent years, molecular testing has emerged as a reliable preoperative diagnostic tool that stratifies patients into different risk categories (low, intermediate, or high) with varying probabilities of malignancy and helps guide patient treatment., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment., (© 2024. The Author(s).)

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real-time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumours, and to map tumours to their original anatomical position/orientation., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: The suggested atypia of undetermined significance (AUS) rate for thyroid fine-needle aspiration biopsies is 10% or less. Prompted by a high institutional AUS rate, we examined using molecular testing results (MTR) as a potential quality metric tool to reduce the AUS rate. We correlated MTR with AUS cytologic findings, surgical pathology follow-up, and individual pathologist AUS rates., Materials and Methods: Demographic data, cytologic diagnoses, MTR, and surgical pathology diagnoses were retrospectively obtained. MTR were classified as either positive or negative. AUS rates and MTR proportions were compared among pathologists. The cytomorphologic features of 143 AUS cases were assessed and correlated with MTR., Results: Between 2017 and 2022, 710 of 3247 thyroid fine-needle aspirations were classified as AUS, with a yearly average rate of 22% (range = 19%-26%). AUS cases included: 331 (47%) with architectural atypia; 204 (29%) with oncocytic (Hürthle cell) atypia; 99 (14%) with combined architectural and cytologic atypia; and 76 (10%) with isolated cytologic atypia. Most AUS cases with molecular testing had negative MTR (360/492, 73%). AUS with cytologic atypia had higher positive MTR risk (logarithm of odds ratio = 1.27, 95% credible interval [0.5-2.04], P = 0.001). The average positive MTR rate by pathologist was 21.5% (range 0%-35%); higher positive MTR rates had better correlation with subsequent neoplastic/malignant histologic diagnoses. The MTR sensitivity for malignant disease was 89% and the negative predictive value was 91%., Conclusions: MTR analysis reveals the importance of cytologic atypia as a determinant of malignancy risk in AUS cases. Periodic analysis of MTR data alongside individual pathologist AUS rates can help refine diagnostic criteria and potentially reduce AUS overuse., (Copyright © 2023 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)

Background: Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting., Methods: In this study, the authors report on the development and large-scale validation of a deep-learning tool, AutoParis-X, which can facilitate rapid, semiautonomous examination of urine cytology specimens., Results: The results of this large-scale, retrospective validation study indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide variety of cell-related and cluster-related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm ratio for cells in these clusters., Conclusions: The authors developed a publicly available, open-source, interactive web application that features a simple, easy-to-use display for examining urine cytology whole-slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head-to-head clinical trials., (© 2023 American Cancer Society.)

Background: Urine cytology is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological examinations for assessment and early detection of recurrence, beyond identifying a positive finding that requires more invasive methods to confirm recurrence and decide on therapeutic options. Because screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists, and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk-stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer., Methods: In this study, a computational machine learning tool, AutoParis-X, was leveraged to extract imaging features from urine cytology examinations longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk., Results: Results indicate that imaging predictors extracted using AutoParis-X can predict recurrence as well or better than traditional cytological/histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence., Conclusions: Further research will clarify how computational methods can be effectively used in high-volume screening programs to improve recurrence detection and complement traditional modes of assessment., (© 2023 American Cancer Society.)

Background: Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication, further illustrating the potential utility of such methods. However, current approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to "pretrain" models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. In addition, model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and trust in the technology., Methods: Here, we develop an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology (i.e., tissue slides) data, and we compare performance of crossmodal pretraining, contrastive learning, and transfer learning versus the standard procedure., Results: Our models outperform the existing state-of-the-art method (average 11.54% C-index increase), and baseline clinically driven models (average 11.7% C-index increase). Model interpretations elucidate consideration of biologically meaningful factors in making prognosis predictions., Discussion: Our results demonstrate that the selection of pretraining strategies is crucial for obtaining highly accurate prognostication models, even more so than devising an innovative model architecture, and further emphasize the all-important role of the tumor microenvironment on disease progression., (© 2023. The Author(s).)

Background: Mohs micrographic surgery is a procedure used for non-melanoma skin cancers that has 97-99% cure rates largely owing to 100% margin analysis enabled by en face sectioning with real-time, iterative histologic assessment. However, the technique is limited to small and aggressive tumors in high-risk areas because the histopathological preparation and assessment is very time intensive. To address this, paired-agent imaging (PAI) can be used to rapidly screen excised specimens and identify tumor positive margins for guided and more efficient microscopic evaluation., Methods: A mouse xenograft model of human squamous cell carcinoma ( n = 8 mice, 13 tumors) underwent PAI. Targeted (ABY-029, anti-epidermal growth factor receptor (EGFR) affibody molecule) and untargeted (IRDye 680LT carboxylate) imaging agents were simultaneously injected 3-4 h prior to surgical tumor resection. Fluorescence imaging was performed on main, unprocessed excised specimens and en face margins (tissue sections tangential to the deep margin surface). Binding potential (BP) - a quantity proportional to receptor concentration - and targeted fluorescence signal were measured for each, and respective mean and maximum values were analyzed to compare diagnostic ability and contrast. The BP and targeted fluorescence of the main specimen and margin samples were also correlated with EGFR immunohistochemistry (IHC)., Results: PAI consistently outperformed targeted fluorescence alone in terms of diagnostic ability and contrast-to-variance ratio (CVR). Mean and maximum measures of BP resulted in 100% accuracy, while mean and maximum targeted fluorescence signal offered 97% and 98% accuracy, respectively. Moreover, maximum BP had the greatest average CVR for both main specimen and margin samples (average 1.7 ± 0.4 times improvement over other measures). Fresh tissue margin imaging improved similarity with EGFR IHC volume estimates compared to main specimen imaging in line profile analysis; and margin BP specifically had the strongest concordance (average 3.6 ± 2.2 times improvement over other measures)., Conclusions: PAI was able to reliably distinguish tumor from normal tissue in fresh en face margin samples using the single metric of maximum BP. This demonstrated the potential for PAI to act as a highly sensitive screening tool to eliminate the extra time wasted on real-time pathological assessment of low-risk margins., Competing Interests: ABY-029 was produced with funds provided by the National Cancer Institute under an academic-industrial partnership with Affibody AB and LI-COR Biosciences, Inc. The authors of this manuscript are not employed, nor received funding for research from these industrial partners. KSS received the Odyssey M imaging system from LI-COR Biosciences, Inc. as a gift. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Torres, Hodge, Levy, Vaickus, Chen, LeBouef and Samkoe.)

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays., (Copyright © 2023 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Background: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM)., Aims: We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia., Methods: Spatial-temporal data were extracted from consecutive 3D-HDAM studies performed between 2018 and 2020 at Dartmouth-Hitchcock Health. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity., Results: 302 3D-HDAM studies representing 1208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy [AUC 0.91 (95% confidence interval 0.89-0.93)] to traditional [AUC 0.93(0.92-0.95)] and hybrid [AUC 0.96(0.94-0.97)] predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive [odds ratio 4.21(2.78-6.38)] versus traditional/hybrid approaches., Conclusions: Deep learning is capable of considering complex spatial-temporal information on 3D-HDAM technology. Future studies are needed to evaluate the clinical context of these preliminary findings., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1-10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x-y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model's ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications., Competing Interests: None to disclose., (© 2023 The Authors.)

Background: Urine cytology is commonly used as a screening test for high-grade urothelial carcinoma for patients with risk factors or hematuria and is an essential step in longitudinal monitoring of patients with previous bladder cancer history. However, the semisubjective nature of current reporting systems for urine cytology (e.g., The Paris System) can hamper reproducibility. For instance, the incorporation of urothelial cell clusters into the classification schema is still an item of debate and perplexity among expert cytopathologists because several previous works have disputed their diagnostic relevance., Methods: In this work, an automated preprocessing tool for urothelial cell cluster assessment was developed that divides urothelial cell clusters into meaningful components for downstream assessment (ie, population-based studies, workflow automation)., Results: In this work, an automated preprocessing tool for urothelial cell cluster assessment was developed that divides urothelial cell clusters into meaningful components for downstream assessment (ie, population-based studies, workflow automation). Results indicate that cell cluster atypia (i.e., defined by whether the cell cluster harbored multiple atypical cells, thresholded by a minimum number of cells), cell border overlap and smoothness, and total number of clusters are important markers of specimen atypia when considering assessment of urothelial cell clusters., Conclusions: Markers established through techniques to separate cell clusters may have wider applicability for the design and implementation of machine learning approaches for urine cytology assessment., (© 2022 American Cancer Society.)

Introduction: Urine cytology is used to screen for urothelial carcinoma in patients with hematuria or risk factors (eg, smoking, industrial dye exposure) and is an essential clinical triage and longitudinal monitoring tool for patients with known bladder cancer. However, urine cytology is semisubjective and thus susceptible to issues including specimen quality, interobserver variability, and "hedging" towards equivocal ("atypical") diagnoses. These factors limit the predictive value of urine cytology and increase reliance on invasive procedures (cystoscopy). The Paris System for Reporting Urine Cytology (TPS) was formulated to provide more quantitative/reproducible endpoints with well-defined criteria for urothelial atypia. TPS is often compared to other assessment techniques to justify its adoption. TPS results in decreased use of the atypical category and better reproducibility. Previous reports comparing diagnoses pre- and post-TPS have not considered temporal differences between diagnoses made under prior systems and TPS. By aggregating across time, studies may underestimate the magnitude of differences between assessment methods., Materials and Methods: We conducted a large-scale longitudinal reassessment of urine cytology using TPS criteria from specimens collected from 2008 to 2018, prior to the mid-2018 adoption of TPS at an academic medical center., Results: Findings indicate that differences in atypical assignment were largest at the start of the period and these differences progressively decreased towards insignificance just prior to TPS implementation., Conclusions: This finding suggests that cytopathologists had begun to utilize the quantitative TPS criteria prior to official adoption, which may more broadly inform adoption strategies, communication, and understanding for evolving classification systems in cytology., (Copyright © 2022 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)

Introduction: Molecular testing has helped clinicians and cytopathologists to further categorize indeterminate thyroid fine needle aspiration (FNA) specimens. The purpose of the present study was to evaluate the accuracy of commercially available molecular tests, review their effects on patient treatment, and correlate the molecular alterations with the histologic findings., Materials and Methods: A pathology laboratory information system search identified thyroid FNAs performed at our institution between January 1, 2015 and June 30, 2020. The results of surgical follow-up and ancillary molecular testing were collected. We evaluated the accuracy of these tests and whether they could reduce the number of surgeries performed., Results: Our laboratory information system search identified 510 cases reported as atypia of undetermined significance, 94 as suspicious for follicular neoplasm, and 44 as suspicious for follicular neoplasm, Hurthle cell type. Of the specimens, 343 had no ancillary molecular testing, 146 were sent for ThyGenX/ThyraMIR, and 136 were sent for ThyroSeq. Of the patients without molecular testing, 50.4% had undergone follow-up surgery compared with 30.8% after ThyGenX/ThyraMIR and 38.2% after ThyroSeq testing, resulting in 38.9% and 24.2% fewer surgeries and an odds ratio of 0.04 (95% confidence interval, 0.00-0.33) and 0.14 (95% confidence interval, 0.01-0.95), respectively. For ThyGenX/ThyraMIR testing, the risk of malignancy for high and moderate to high risk alterations was 80%, 28.6% for moderate and low to moderate risk alterations, and 23.1% for low risk alterations. For ThyroSeq, the risk of malignancy was 87.5% for high risk alterations, 36.8% for intermediate to high risk alterations, 27.3% for intermediate risk alterations, and 0% for low risk alterations. The areas under the curve for ThyGenX/ThyraMIR and ThyroSeq testing were 0.65 and 0.85, respectively., Conclusions: These findings suggest that, at our institution, both ThygenX/ThyraMIR and ThyroSeq can be used to effectively stratify cytology specimens based on the risk of malignancy and reduce the number of surgeries performed at our institution., (Copyright © 2021 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.

Background: The Bethesda System for Reporting Thyroid Cytopathology contains an atypia of undetermined significance (AUS) category with heterogeneous and distinct inclusion criteria. The purpose of this study was to investigate differences in malignancy rates and molecular alterations based on the presence of different criteria., Methods: A laboratory information search was conducted to identify thyroid fine-needle aspiration specimens signed out as AUS. The cases were reclassified as architectural atypia (3A), cytologic atypia (3C), both architectural and cytologic atypia (3B), or Hürthle cell aspirate (3H). Surgical follow-up and concurrent molecular test results, if available, were collected., Results: Five hundred ten specimens, including 258 reclassified as 3A, 40 reclassified as 3B, 119 reclassified as 3C, and 86 reclassified as 3H, were identified. The risks of malignancy for the subcategories were 13.4%, 26.3%, 44.1%, and 13.8%, respectively. Additionally, BRAF V600E mutations were more prevalent in specimens with cytologic atypia (3B/3C), whereas low-risk alterations, including KRAS, PTEN, and PAX8-PPARy2, were more prevalent in those with architectural atypia (3A)., Conclusions: Subdividing AUS specimens on the basis of the type of atypia can yield categories associated with distinct molecular alterations and risks of malignancy., (© 2021 American Cancer Society.)

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks? Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images. Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age. Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Levy, Lebeaux, Hoen, Christensen, Vaickus and MacKenzie.)

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules., (© 2021. The Author(s).)

Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n = 574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ([Formula: see text]; 95% CI: 0.84-0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, 95% CI: 1.36-3.12, p < 0.001 for real stains; HR = 2.02, 95% CI: 1.40-2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

Follicular dendritic cell sarcoma (FDCS) is a rare malignant neoplasm of follicular dendritic cell origin which can present a diagnostic challenge. Due to the rarity of this neoplasm, its molecular pathogenesis has not been fully elaborated. A previous series of 13 cases reported that 38% contained mutations of genes encoding proteins involved in negative regulation of NF-κB. NF-κB is a family of transcription factors regulated through multiple cellular processes known as the canonical and noncanonical pathways. Here we present the case of a 62-year-old man who presented with abdominal pain and systemic symptoms and was found to have a mass in the porta hepatis. Fine needle aspiration cytology demonstrated a spindle cell neoplasm with vesicular chromatin and prominent nucleoli with admixed lymphocytes. Surgical resection showed an intranodal, 7.3 × 5.5 × 3.5 cm, solid mass composed of plump, spindle to histiocytoid cells with ovoid nuclei and small, prominent nucleoli arranged in a whorled and fascicular pattern. The lesional cells stained positively for CD21, CD23, and CD35 by immunohistochemistry, consistent with a diagnosis of FDCS. Next-generation sequencing revealed pathologic mutations in three genes involved in NF-κB regulation pathways: NFKBIA, TNFAIP3, and TRAF3. A pathologic TP53 mutation was also identified. This case report supports prior associations of the NF-κB pathway dysregulation and FDCS. Additionally, it is the first reported FDCS case with TRAF3 mutation as well as the first reported case to suggest disruption in both the canonical and noncanonical NF-κB pathways in the same lesion., (© 2020 Wiley Periodicals LLC.)

Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue consumed. Prior approaches have lacked the spatial accuracy needed for cell-specific analytical tasks. Here IHC performed on destained H&E slides is used to create a neural network that is potentially capable of predicting individual cell immunophenotype. Twelve slides were stained with H&E and scanned to create digital whole slide images. The H&E slides were then destained, and stained with SOX10 IHC. The SOX10 IHC slides were scanned, and corresponding H&E and IHC digital images were registered. Color-thresholding and machine learning techniques were applied to the registered H&E and IHC images to segment 3,396,668 SOX10-negative cells and 306,166 SOX10-positive cells. The resulting segmentation was used to annotate the original H&E images, and a convolutional neural network was trained to predict SOX10 nuclear staining. Sixteen thousand three hundred and nine image patches were used to train the virtual IHC (vIHC) neural network, and 1,813 image patches were used to quantitatively evaluate it. The resulting vIHC neural network achieved an area under the curve of 0.9422 in a receiver operator characteristics analysis when sorting individual nuclei. The vIHC network was applied to additional images from clinical practice, and was evaluated qualitatively by a board-certified dermatopathologist. Further work is needed to make the process more efficient and accurate for clinical use. This proof-of-concept demonstrates the feasibility of creating neural network-driven vIHC assays.

Importance: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathologic slides could benefit practitioners and patients., Objective: To evaluate the performance and generalizability of a deep neural network for colorectal polyp classification on histopathologic slide images using a multi-institutional data set., Design, Setting, and Participants: This prognostic study used histopathologic slides collected from January 1, 2016, to June 31, 2016, from Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, with 326 slides used for training, 157 slides for an internal data set, and 25 for a validation set. For the external data set, 238 slides for 179 distinct patients were obtained from 24 institutions across 13 US states. Data analysis was performed from April 9 to November 23, 2019., Main Outcomes and Measures: Accuracy, sensitivity, and specificity of the model to classify 4 major colorectal polyp types: tubular adenoma, tubulovillous or villous adenoma, hyperplastic polyp, and sessile serrated adenoma. Performance was compared with that of local pathologists' at the point of care identified from corresponding pathology laboratories., Results: For the internal evaluation on the 157 slides with ground truth labels from 5 pathologists, the deep neural network had a mean accuracy of 93.5% (95% CI, 89.6%-97.4%) compared with local pathologists' accuracy of 91.4% (95% CI, 87.0%-95.8%). On the external test set of 238 slides with ground truth labels from 5 pathologists, the deep neural network achieved an accuracy of 87.0% (95% CI, 82.7%-91.3%), which was comparable with local pathologists' accuracy of 86.6% (95% CI, 82.3%-90.9%)., Conclusions and Relevance: The findings suggest that this model may assist pathologists by improving the diagnostic efficiency, reproducibility, and accuracy of colorectal cancer screenings.

The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide .

Background: The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid-based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear-to-cytoplasmic (N:C) ratio and produce a qualitative "atypia score." The authors propose a hybrid deep-learning and morphometric model that reliably automates the Paris System., Methods: Whole-slide images (WSI) of liquid-based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed rich information matrices (RIMs). These RIM objects were enhanced using deep-learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information., Results: The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed-grid format, and generate pancellular statistics containing quantitative/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated., Conclusions: A hybrid deep-learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations., (© 2019 American Cancer Society.)

Cyanobacteria produce harmful toxins that have been associated with several acute conditions and chronic human diseases, like gastroenteritis, non-alcoholic liver disease, and amyotrophic lateral sclerosis. Aerosol from waterbodies appears to be a likely mechanism for exposure. We conducted a study of human biospecimens focused on the cyanobacterial aerosilization process by evaluating the extent to which cyanobacteria can invade the human respiratory tract. Our study suggests that humans routinely inhale aerosolized cyanobacteria, which can be harbored in the nostrils and the lungs. Using PCR, cyanobacteria were found at high frequencies in the upper respiratory tract (92.20%) and central airway (79.31%) of our study subjects. Nasal swabs were not predictive of bronchoalveolar lavage (BAL) when detecting inhaled cyanobacteria. Interestingly, we found no evidence that time of year was a significant factor for cyanobacteria positivity (BAL cytology p = 1.0 and PCR p = 1.0); (nasal swab cytology p = 0.051 and PCR p = 0.65). Additionally, we found that proximity to a waterbody was not a significant factor for cyanobacteria positivity in BAL and nasal swabs collected during cyanobacteria bloom season [May-October] (p = 0.46 and p = 0.38). These data suggest that cyanobacteria exposure may be a prevalent and chronic phenomenon not necessarily restricted to waterbodies alone. Sources of indoor exposure warrant future investigation. Given the widespread prevalence of cyanobacterial exposure in the airway, investigation of the aerosol spread of cyanotoxins, more specifically, is warranted. Our findings are consistent with the hypothesis that aerosol is a significant route for cyanobacteria exposure, and thus a likely route of transmission for cyanotoxin-associated human diseases., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Mixed acinar-neuroendocrine carcinoma (MANEC) of the pancreas is a rare tumor. We present two cases of MANEC diagnosed on endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA). The first patient is a 33-year-old male who had a 3.6 cm mass in the uncinate process and liver metastasis. The second patient is a 66-year-old male with a 10 cm mass in the pancreatic tail. The FNA smears from both cases were hypercellular with neoplastic cells in loosely cohesive clusters and many naked nuclei. In both cases, the tumor cells were positive for CKAE1/3, synaptophysin, chromogranin, and trypsin by immunohistochemistry. Final diagnoses of MANEC were rendered based on cytological features and immunohistochemical profiles. To date, 44 cases of MANEC have been reported in the English literature, only three of which were diagnosed on cytopathology specimens before surgical resection. Our report adds two more cases diagnosed on cytopathology alone. Herein, we discuss the various cytomorphologic and clinical features of MANEC and present a brief review of the literature. Diagn. Cytopathol., (© 2018 Wiley Periodicals, Inc.)

Background: Recent discussions have focused on redefining noninvasive follicular variant of papillary thyroid carcinoma (NI-FVPTC) as a neoplasm rather than a carcinoma. This study assesses the potential impact of such a reclassification on the implied risk of malignancy (ROM) for the diagnostic categories of The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC)., Methods: The study consisted of consecutive fine-needle aspiration biopsy (FNAB) cases collected between January 1, 2013 and June 30, 2014 from 5 academic institutions. Demographic information, cytology diagnoses, and surgical pathology follow-up were recorded. The ROM was calculated with and without NI-FVPTC and was presented as a range: all cases (ie, overall risk of malignancy [OROM]) versus those with surgical follow-up only., Results: The FNAB cohort consisted of 6943 thyroid nodules representing 5179 women and 1409 men with an average age of 54 years (range, 9-94 years). The combined average ROM and OROM for the diagnostic categories of TBSRTC were as follows: nondiagnostic, 4.4% to 25.3%; benign, 0.9% to 9.3%; atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), 12.1% to 31.2%; follicular neoplasm (FN), 21.8% to 33.2%; suspicious for malignancy (SM), 62.1% to 82.6%; and malignant, 75.9% to 99.1%. The impact of reclassifying NI-FVPTC on the ROM and OROM was most pronounced and statistically significant in the 3 indeterminate categories: the AUS/FLUS category had a decrease of 5.2% to 13.6%, the FN category had a decrease of 9.9% to 15.1%, and the SM category had a decrease of 17.6% to 23.4% (P < .05), whereas the benign and malignant categories had decreases of 0.3% to 3.5% and 2.5% to 3.3%, respectfully. The trend of the effect on the ROM and OROM was similar for all 5 institutions., Conclusions: The results from this multi-institutional cohort indicate that the reclassification of NI-FVPTC will have a significant impact on the ROM for the 3 indeterminate categories of TBSRTC., (© 2015 American Cancer Society.)

Background: Negative gynecologic cytology cases (ie, those diagnosed as negative for intraepithelial lesion or malignancy) are manually reviewed by 2 methods using semiautomated screening: 1) immediate full slide review (FSR) after fields-of-view analysis (FOV) (FOV + FSR), and 2) quality-assurance/high-risk, quintile-directed full manual review (FMR). Data supporting current guidelines were limited. The authors investigated FMR, FOV + FSR, and the review process in general., Methods: Gynecologic cytology cases from 2009 to 2014 at Massachusetts General Hospital were analyzed. The data comprised 93,169 patients, 194,656 specimens, and 49,979 human papillomavirus (HPV) tests., Results: In patients who underwent FMR, the epithelial cell abnormality (ECA) rate was correlated with the HPV-positive rate (correlation coefficient [r(2) ] = 0.82; Y = 0.19X + 0.02), and both rates decreased with age. For patients who underwent FOV + FSR, the ECA rate was also related to the HPV-positive rate (r(2) = 0.86; Y = 0.39X + 0.11), and both rates decreased with age. The FMR group had similar HPV-positive rates compared to the FOV + FSR group (2%-52% vs 9%-68%, respectively). HPV-positive patients had a higher risk of ECA than HPV-negative patients (40% vs 8%; P < .0001). Currently, manual review allocates resources inefficiently, to older, HPV-negative patients as compared to younger, HPV-positive patients. Additionally, too many patients who have HPV-positive specimens with ECA proceed from primary screening to FMR. This imbalance can be observed in the slope of the line comparing the FMR ECA rate with the HPV-positive rate (which might serve as a surrogate marker for adequacy of FOV + FSR screening). Therefore, laboratories should try to reduce the number of HPV-positive cases that reach FMR., Conclusions: Current review procedures devote too few resources to younger patients. FMR should be assigned according to HPV status and age. The slope of the line comparing the FMR ECA rate with the FMR HPV-positive rate might be a useful surrogate marker of screening accuracy., (© 2015 American Cancer Society.)

Background: The estimation of the nuclear-to-cytoplasmic ratio (N:C ratio) is an important factor in diagnosing atypia and malignancy in pathological specimens, particularly in cytology. Many algorithms for determining malignant potential make reference to specific, decimal N:C ratios without specifying how the ratio should be measured, with the implication that the observer is intended to estimate this ratio by eye. The authors wanted to determine how accurate trained morphologists (including attending pathologists, pathology residents, and cytotechnologists) are at estimating the N:C ratio without a measuring device., Methods: Two surveys were prepared containing ideal and real cell images of various N:C ratios. Participants were instructed to select their best estimate from a list of decimal ratios. The data were tabulated and analyzed to determine how accurate the estimates were and whether there was any performance difference between ideal and real images., Results: The absolute and percentage deviation from the actual N:C ratio decreased steadily with increasing N:C ratio. Aggregate performance was found to be closely correlated between real and ideal images, although interobserver variation was not significantly different among participants in the real images quiz, but was significantly different on the ideal images quiz., Conclusions: Trained morphologists make relatively accurate estimations of the N:C ratio and become increasingly more accurate as the depicted N:C ratio increases. This suggests that including N:C ratio decimals as a criteria for the diagnosis of atypia is valid for high N:C ratios., (© 2015 American Cancer Society.)

Background: Examining cytotechnologist workflow and how it relates to job performance and patient safety is important in determining guidelines governing allowable workloads. This report discusses the development of a software tool that significantly simplifies the process of analyzing cytotechnologist workload while simultaneously increasing the quantity and resolution of the data collected., Methods: The program runs in Microsoft Excel and minimizes manual data entry and data transcription by automating as many tasks as is feasible., Results: Data show the cytotechnologists tested were remarkably consistent in the amount of time it took them to screen a cervical cytology (Gyn) or a nongynecologic cytology (Non-Gyn) case and that this amount of time was directly proportional to the number of slides per case. Namely, the time spent per slide did not differ significantly in Gyn versus Non-Gyn cases (216 ± 3.4 seconds and 235 ± 24.6 seconds, respectively; P=.16). There was no significant difference in the amount of time needed to complete a Gyn case between the morning and the evening (314 ± 4.7 seconds and 312 ± 7.1 seconds; P=.39), but a significantly increased time spent screening Non-Gyn cases (slide-adjusted) in the afternoon hours (323 ± 20.1 seconds and 454 ± 67.6 seconds; P=.027), which was largely the result of significantly increased time spent on prescreening activities such as checking the electronic medical record (62 ± 6.9 seconds and 145 ± 36 seconds; P=.006)., Conclusions: This Excel-based data collection tool generates highly detailed data in an unobtrusive manner and is highly customizable to the individual working environment and clinical climate., (© 2013 American Cancer Society.)

Asthma imposes considerable patient and economic burdens, with the most severe cases causing the greatest affliction. Identifying stimuli that worsen asthma severity is an essential step to controlling both disease morbidity and the lessening economic impact. This study provides the first mechanistic investigation into how acute ethanol exposure will increase asthma severity in a murine model of mild cockroach allergen (CRA)-induced asthma. Outbred mice were sensitized to induce mild allergic asthma, with intratracheal CRA exposures on days 0 and 14. On day 21 mice were gavaged with water or 32% ethanol, and the third allergen exposure was given 30 min post-gavage. Asthmatic responses were measured at several time-points up to 42 h after the third allergen challenge. Ethanol-gavaged mice showed increased asthma severity within 90 min post-allergen challenge, with exacerbations lasting for 24 h. Ethanol caused greater airways obstruction, including an eightfold increase in epithelial cell mucin and increased mucus plugs, resulting in a 50% reduction in bronchiole patency. Ethanol gavage also induced significant increases in airways hyperreactivity. While T helper type 1 (Th1) and Th2 cytokines were not altered by ethanol gavage, pulmonary neutrophil and eosinophil recruitment were augmented. This increase was associated with increased chemokine production. Administration 2 h prior to ethanol gavage of a neutralizing antibody cocktail to keratinocyte-derived chemokine, macrophage inflammatory protein-2, eotaxin-1 and eotaxin-2 prevented ethanol-induced eosinophil recruitment and airways hyperreactivity. These data provide evidence that acute alcohol exposure immediately prior to a mild allergen-triggered asthmatic episode will exacerbate asthma severity mediated by increased production of chemokines., (© 2012 British Society for Immunology.)

Objective: The cause of death in murine models of sepsis remains unclear. The primary purpose of this study was to determine if significant lung injury develops in mice predicted to die after cecal ligation and puncture-induced sepsis compared with those predicted to live., Design: Prospective, laboratory controlled experiments., Setting: University research laboratory., Subjects: Adult, female, outbred Institute of Cancer Research mice., Interventions: Mice underwent cecal ligation and puncture to induce sepsis. Two groups of mice were euthanized at 24 and 48 hrs postcecal ligation and puncture and samples were collected. These mice were further stratified into groups predicted to die (Die-P) and predicted to live (Live-P) based on plasma interleukin-6 levels obtained 24 hrs postcecal ligation and puncture. Multiple measures of lung inflammation and lung injury were quantified in these two groups. Results from a group of mice receiving intratracheal normal saline without surgical intervention were also included as a negative control. As a positive control, bacterial pneumonia was induced with Pseudomonas aeruginosa to cause definitive lung injury. Separate mice were followed for survival until Day 28 postcecal ligation and puncture. These mice were used to verify the interleukin-6 cutoffs for survival prediction., Measurements and Main Results: After sepsis, both the Die-P and Live-P mice had significantly suppressed measures of respiratory physiology but maintained normal levels of arterial oxygen saturation. Bronchoalveolar lavage levels of pro- and anti-inflammatory cytokines were not elevated in the Die-P mice compared with the Live-P. In addition, there was no increase in the recruitment of neutrophils to the lung, pulmonary vascular permeability, or histological evidence of damage. In contrast, all of these pulmonary injury and inflammatory parameters were increased in mice with Pseudomonas pneumonia., Conclusions: These data demonstrate that mice predicted to die during sepsis have no significant lung injury. In murine intra-abdominal sepsis, pulmonary injury cannot be considered the etiology of death in the acute phase.

Asthma is a chronic condition with high morbidity and healthcare costs, and cockroach allergens are an established cause of urban pediatric asthma. A better understanding of cell types involved in promoting lung inflammation could provide new targets for the treatment of chronic pulmonary disease. Because of its role in regulating myeloid cell-dependent inflammatory processes, we examined A(2B) R expression by myeloid cells in a cockroach allergen model of murine asthma-like pulmonary inflammation. Both systemic and myeloid tissue-specific A(2B) R deletion significantly decreased pulmonary inflammatory cell recruitment, airway mucin production, and proinflammatory cytokine secretion after final allergen challenge in sensitized mice. A(2B) R deficiency resulted in a dramatic reduction on Th2-type airways responses with decreased pulmonary eosinophilia without augmenting neutrophilia, and decreased lung IL-4, IL-5, and IL-13 production. Chemokine analysis demonstrated that eotaxin 1 and 2 secretion in response to repeated allergen challenge is myeloid cell A(2B) R dependent. In contrast, there were no differences in the levels of the CXC chemokines keratinocyte-derived chemokine and MIP-2 in the myeloid cell A(2B) R-deficient mice, strengthening A(2B) R involvement in the development of Th2-type airways inflammation. Proinflammatory TNF-α, IFN-γ, and IL-17 secretion were also reduced in systemic and myeloid tissue-specific A(2B) R deletion mouse lines. Our results demonstrate Th2-type predominance for A(2B) R expression by myeloid cells as a mechanism of development of asthma-like pulmonary inflammation.

Asthma may be triggered by multiple mediators, including allergen-IgE cross-linking and non-IgE mechanisms. Several clinical studies have shown acute ethanol consumption exacerbates asthma, yet no animal model exists to study this process. We developed a model of ethanol-triggered asthma in allergen-sensitized mice to evaluate the mechanisms of ethanol inducing asthma-like responses. Outbred mice were exposed to cockroach allergens on Days 0 and 14; and on Day 21, mice received ethanol by oral gavage. Tracer studies confirmed alcohol aspiration did not occur. Within 30 minutes, alcohol induced degranulation of over 74% of mast cells, and multiple parameters of asthma-like pulmonary inflammation were triggered. Ethanol-gavaged mice had a fivefold increased production of eotaxin-2 (534 pg/mL) and a sevenfold increase in bronchoalveolar eosinophils (70,080 cells). Ethanol induced a 10-fold increase in IL-13, from 84 pg/mL in sensitized mice to 845 pg/mL in ethanol-gavaged sensitized mice. In cockroach allergen-sensitized mice, ethanol triggered asthma-like changes in respiratory physiology and a significant fivefold increase in airway mucin production. Importantly, none of these asthmatic exacerbations were observed in normal mice gavaged with ethanol. Cromolyn sodium effectively stabilized mast cells, yet increased mucin production and bronchoalveolar eosinophil recruitment. Together, these data show a single oral alcohol exposure will trigger asthma-like pulmonary inflammation in allergen-sensitized mice, providing a novel asthma model., (Copyright © 2012 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Objectives: The aim of this study is to define the kinetics of the pulmonary inflammatory response in cockroach allergen (CRA) sensitized and challenged outbred mice., Methods: Asthma-like pulmonary inflammation was induced with three pulmonary exposures to CRA, without the use of adjuvants. Mice were sacrificed at multiple time points and asthma-like pulmonary inflammation quantified., Results: Several pulmonary parameters showed a pronounced biphasic inflammatory response with an early stage (1.5 hours post challenge) and late stage (24 hours). The initial phase was characterized by the production of multiple inflammatory mediators, including CXC chemokines, and the recruitment of neutrophils to the lung. The number of pulmonary eosinophils decreased in the early phase but quickly rebounded. Both the early and late phases had increases in TNF production in addition to airways hyperreactivity. The model also demonstrated early production of mucin with clearance by 12 hours followed by new accumulation of mucin in the pulmonary epithelial cells. Eotaxins within the lung peaked at about 12 hours and the numbers of eosinophils in the lung remained constant throughout the 48 hours of the study., Conclusions: The pulmonary inflammatory parameters in response to a clinically relevant allergen define a biphasic response. These data may be used to investigate the pathogenesis of the disease and develop targeted therapies for the distinct phases.

Particulate matter heavily pollutes the urban atmosphere, and several studies show a link between increased ambient particulate air pollution and exacerbation of pre-existing pulmonary diseases, including asthma. We investigated how diesel exhaust particulates (DEPs) aggravate asthma-like pulmonary inflammation in a mouse model of asthma induced by a house dust extract (HDE) containing cockroach allergens and endotoxin. BALB/c mice were exposed to three pulmonary challenges via hypopharyngeal administration of an HDE collected from the home of an asthmatic child. One hour before each pulmonary challenge, mice were exposed to DEP or PBS. Pulmonary inflammation was assessed by histological features, oxidative stress, respiratory physiological features, inflammatory cell recruitment, and local CXC chemokine production. To prove the role of CXC chemokines in the augmented inflammation, CXC chemokine-specific antibodies were delivered to the lungs before DEP exposure. DEP exacerbated HDE-induced airway inflammation, with increased airway mucus production, oxidative stress, inflammatory cell infiltration, bronchoalveolar lavage concentrations of CXC chemokines, and airway hyperreactivity. Neutralization of airway keratinocyte-derived chemokine and macrophage inflammatory protein-2 significantly improves the respiratory function in addition to decreasing the infiltration of neutrophils and eosinophils. Blocking the chemokines also decreased airway mucus production. These results demonstrate that DEP exacerbates airway inflammation induced by allergen through increased pulmonary expression of the CXC chemokines (keratinocyte-derived chemokine and macrophage inflammatory protein-2)., (Copyright © 2011 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Outbred mice traditionally are considered to display high variability, thereby limiting their use in some studies. Researchers frequently are encouraged to use inbred strains of mice because of the greater homogeneity of these experimental animals. We compared the pulmonary inflammatory response of inbred BALB/cJ mice to that of outbred HSD-ICR mice by measuring multiple variables, including cytokines, chemokines, number of pulmonary inflammatory cells, and respiratory parameters. Cockroach allergens induced significant pulmonary inflammation in both BALB and ICR mice. Our comparisons of the coefficients of variance for 148 discrete data sets for each strain or stock indicated that BALB and ICR mice have roughly equivalent intrastrain or -stock variability in our model of asthma-like pulmonary inflammation. The average coefficient of variance, calculated as the ratio of the SD to the mean of a data set, was 0.35 ± 0.34 for BALB mice compared with 0.31 ± 0.32 for ICR mice. In conclusion, inbred BALB and outbred ICR mice have roughly equivalent intrastrain or -stock variability in a murine model of asthma-like pulmonary inflammation.

Background: Antigen desensitization through oral tolerance is becoming an increasingly attractive treatment option for allergic diseases. However, the mechanism(s) by which tolerization is achieved remain poorly defined. In this study we endeavored to induce oral tolerance to cockroach allergen (CRA: a complex mixture of insect components) in order to ameliorate asthma-like, allergic pulmonary inflammation., Methods: We compared the pulmonary inflammation of mice which had received four CRA feedings prior to intratracheal allergen sensitization and challenge to mice fed PBS on the same time course. Respiratory parameters were assessed by whole body unrestrained plethysmography and mechanical ventilation with forced oscillation. Bronchoalveolar lavage fluid (BAL) and lung homogenate (LH) were assessed for cytokines and chemokines by ELISA. BAL inflammatory cells were also collected and examined by light microscopy., Results: CRA feeding prior to allergen sensitization and challenge led to a significant improvement in respiratory health. Airways hyperreactivity measured indirectly via enhanced pause (Penh) was meaningfully reduced in the CRA-fed mice compared to the PBS fed mice (2.3 ± 0.4 vs 3.9 ± 0.6; p = 0.03). Directly measured airways resistance confirmed this trend when comparing the CRA-fed to the PBS-fed animals (2.97 ± 0.98 vs 4.95 ± 1.41). This effect was not due to reduced traditional inflammatory cell chemotactic factors, Th2 or other cytokines and chemokines. The mechanism of improved respiratory health in the tolerized mice was due to significantly reduced eosinophil numbers in the bronchoalveolar lavage fluid (43300 ± 11445 vs 158786 ± 38908; p = 0.007) and eosinophil specific peroxidase activity in the lung homogenate (0.59 ± 0.13 vs 1.19 ± 0.19; p = 0.017). The decreased eosinophilia was likely the result of increased IL-10 in the lung homogenate of the tolerized mice (6320 ± 354 ng/mL vs 5190 ± 404 ng/mL, p = 0.02)., Conclusion: Our results show that oral tolerization to CRA can improve the respiratory health of experimental mice in a CRA-induced model of asthma-like pulmonary inflammation by reducing pulmonary eosinophilia.

Pulmonary inflammation causes multiple alterations within the lung, including mucus production, recruitment of inflammatory cells, and airway hyperreactivity (AHR). Measurement of AHR by direct, invasive means (eg, mechanical ventilation) or noninvasive techniques, like whole body plethysmography (WBP), assesses the severity of pulmonary inflammation in animal models of inflammatory lung disease. Direct measurement of AHR is acknowledged as the most accurate method for assessing airway mechanics, but analysis of all data obtained from WBP may offer insights into which inflammatory aspects of the lung are altered along with AHR. Using WBP, we compared the respiratory parameters of two groups of mice sensitized with cockroach allergen. One group was treated with dexamethasone (Dex) before final challenge (Dex-Asthma), while the other group received vehicle treatment (Asthma). Respiratory parameters from plethysmography revealed that Dex-Asthma mice compensated to maintain high minute ventilation, whereas Asthma mice showed significant impairment in minute ventilation despite increased peak expiratory flow (103 ± 5 ml/min vs. 69 ± 70 ml/min). The WBP data suggest that enhanced air exchange in the Dex-Asthma mice results from significant decreases in airway mucus production. Additional studies with quantitative morphometry of histological sections confirmed that Dex reduced airway mucus. In conclusion, a detailed examination of WBP parameters can accurately assess the respiratory health of mice and will help direct additional studies.