An Overview of the Effect of Aging on the Female Urethra.

Urethral function declines by roughly 15% per decade and profoundly contributes to the pathogenesis of urinary incontinence. Individuals with poor urethral function are more likely to fail surgical management for stress incontinence that focus on improving urethral support. The reduced number of intramuscular nerves and the morphologic changes in muscle and connective tissue collectively impact urethral function as women age. Imaging technologies like MRI and ultrasound have advanced our understanding of these changes. However, substantial knowledge gaps remain. Addressing these gaps can be crucial for developing better prevention and treatment strategies, ultimately enhancing the quality of life for aging women.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

A Study of Thyroid Fine Needle Aspiration of Follicular Adenoma in the "Atypia of Undetermined Significance" Bethesda Category Using Digital Image Analysis.

BACKGROUND: Originally designed for computerized image analysis, ThinPrep is underutilized in that role outside gynecological cytology. It can be used to address the inter/intra-observer variability in the evaluation of thyroid fine needle aspiration (TFNA) biopsy and help pathologists to gain additional insight into thyroid cytomorphology. METHODS: We designed and validated a feature engineering and supervised machine learning-based digital image analysis method using ImageJ and Python scikit-learn . The method was trained and validated from 400 low power (100x) and 400 high power (400x) images generated from 40 TFNA cases. RESULT: The area under the curve (AUC) for receiver operating characteristics (ROC) is 0.75 (0.74-0.82) for model based from low-power images and 0.74 (0.69-0.79) for the model based from high-power images. Cytomorphologic features were synthesized using feature engineering and when performed in isolation, they achieved AUC of 0.71 (0.64-0.77) for chromatin, 0.70 (0.64-0.73) for cellularity, 0.65 (0.60-0.69) for cytoarchitecture, 0.57 (0.51-0.61) for nuclear size, and 0.63 (0.57-0.68) for nuclear shape. CONCLUSION: Our study proves that ThinPrep is an excellent preparation method for digital image analysis of thyroid cytomorphology. It can be used to quantitatively harvest morphologic information for diagnostic purpose.

Quantitative Image Analysis as an Adjunct to Manual Scoring of ER, PgR, and HER2 in Invasive Breast Carcinoma.

OBJECTIVES: Biomarker expression evaluation for estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2) is an essential prognostic and predictive parameter for breast cancer and critical for guiding hormonal and neoadjuvant therapy. This study compared quantitative image analysis (QIA) with pathologists' scoring for ER, PgR, and HER2. METHODS: A retrospective analysis was undertaken of 1,367 invasive breast carcinomas, including all histopathology subtypes, for which ER, PgR, and HER2 were analyzed by manual scoring and QIA. The resulting scores were compared, and in a subset of HER2 cases (n = 373, 26%), scores were correlated with available fluorescence in situ hybridization (FISH) results. RESULTS: Concordance between QIA and manual scores for ER, PgR, and HER2 was 93%, 96%, and 90%, respectively. Discordant cases had low positive scores (1%-10%) for ER (n = 33), were due to nonrepresentative region selection (eg, ductal carcinoma in situ) or tumor heterogeneity for PgR (n = 43), and were of one-step difference (negative to equivocal, equivocal to positive, or vice versa) for HER2 (n = 90). Among HER2 cases where FISH results were available, only four (1.0%) showed discordant QIA and FISH results. CONCLUSIONS: QIA is a computer-aided diagnostic support tool for pathologists. It significantly improves ER, PgR, and HER2 scoring standardization. QIA demonstrated excellent concordance with pathologists' scores. To avoid pitfalls, pathologist oversight of representative region selection is recommended.

Rationale and design of the Kidney Precision Medicine Project.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review.

Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.

Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology.

Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI is capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will likely not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology.

Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists' Future "Microscope".

Digital displays (monitors) are an indispensable component of a pathologists' daily workflow, from writing reports, viewing whole-slide images, or browsing the Internet. Due to a paucity of literature and experience surrounding display use and standardization in pathology, the Food and Drug Administration's (FDA) has currently restricted FDA-cleared whole-slide imaging systems to a specific model of display for each system, which at this time consists of only medical-grade (MG) displays. Further, given that a pathologists' display will essentially become their new surrogate "microscope," it becomes exceedingly important that all pathologists have a basic understanding of fundamental display properties and their functional consequences. This review seeks to: (a) define and summarize the current and emerging display technology, terminology, features, and regulation as they pertain to pathologists and review the current literature on the impact of different display types (e.g. MG vs. consumer off the shelf vs. professional grade) on pathologists' diagnostic performance and (b) discuss the impact of the recent digital pathology device componentization and the coronavirus disease 2019 public emergency on the pixel pathway and display use for remote digital pathology. Display technology has changed dramatically over the past 20 years and continues to change at a rapid rate. There is a paucity of published studies to date that investigate how display type affects pathologist performance, with more research necessary in order to develop standards and minimum specifications for displays in digital pathology. Given the complexity of modern displays, pathologists must become better informed regarding display technology if they wish to have more choice over their future "microscopes."

Digital pathology and computational image analysis in nephropathology.

The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

The growing need for microservices in bioinformatics.

OBJECTIVE: Within the information technology (IT) industry, best practices and standards are constantly evolving and being refined. In contrast, computer technology utilized within the healthcare industry often evolves at a glacial pace, with reduced opportunities for justified innovation. Although the use of timely technology refreshes within an enterprise's overall technology stack can be costly, thoughtful adoption of select technologies with a demonstrated return on investment can be very effective in increasing productivity and at the same time, reducing the burden of maintenance often associated with older and legacy systems. In this brief technical communication, we introduce the concept of microservices as applied to the ecosystem of data analysis pipelines. Microservice architecture is a framework for dividing complex systems into easily managed parts. Each individual service is limited in functional scope, thereby conferring a higher measure of functional isolation and reliability to the collective solution. Moreover, maintenance challenges are greatly simplified by virtue of the reduced architectural complexity of each constitutive module. This fact notwithstanding, rendered overall solutions utilizing a microservices-based approach provide equal or greater levels of functionality as compared to conventional programming approaches. Bioinformatics, with its ever-increasing demand for performance and new testing algorithms, is the perfect use-case for such a solution. Moreover, if promulgated within the greater development community as an open-source solution, such an approach holds potential to be transformative to current bioinformatics software development. CONTEXT: Bioinformatics relies on nimble IT framework which can adapt to changing requirements. AIMS: To present a well-established software design and deployment strategy as a solution for current challenges within bioinformatics. CONCLUSIONS: Use of the microservices framework is an effective methodology for the fabrication and implementation of reliable and innovative software, made possible in a highly collaborative setting.

Suggestions for a web based universal exchange and inference language for medicine. Continuity of patient care with PCAST disaggregation.

We describe here the applications of our recently proposed Q-UEL language to continuity of patient care between physicians, specialists and institutions as mediated via the Internet, giving examples derived from HL7 CDA and VistA of particular interest to workflow. Particular attention is given to the Universal Exchange Language for healthcare as requested by the US President׳s Council of Advisors on Science and Technology (PCAST) released in December 2010, especially in regard to disaggregation of the patient record on the Internet. To illustrate many features and options, one of our most elaborate configurations combining them, for disaggregation and reaggregation, is described. The Q-UEL tags used do not physically join, but query each other from a random mix via the application. Despite the computationally demanding complexity of the configuration with two joining tags for each data tag and four independently evolving keys, plus a valuable but rate limiting isomorphism test, packets of essential clinical data for patient could be recovered and displayed every 2 s for a "club" of 30,000-50,000 patients in the mix. All computation here is on a standard laptop, but for practical use of the Internet to display downloaded data, the above is adequate, so focus is primarily on increasing club size. In practice, it is not necessary that a club comprise an entire nation. Assuming that one does not use purely random assignments of patients to arbitrary clubs, there could for example be a club comprising all schoolchildren in Scotland, or a club comprising all military veterans in Illinois. In such cases, one is typically dealing with clubs each of the order of a mere million patients. Using such club sizes efficiently, and in principle even a club the size of a whole country, appears to be possible.

Suggestions for a Web based universal exchange and inference language for medicine.

Mining biomedical and pharmaceutical data generates huge numbers of interacting probabilistic statements for inference, which can be supported by mining Web text sources. This latter can also be probabilistic, in a sense described in this report. However, the diversity of tools for probabilistic inference is troublesome, suggesting a need for a unifying best practice. Physicists often claim that quantum mechanics is the universal best practice for probabilistic reasoning. We discuss how the Dirac notation and algebra suggest the form and algebraic and semantic meaning of XML-like Web tags for a clinical and biomedical universal exchange language formulated to make sense directly to the eye of the physician and biomedical researcher.

Image microarrays derived from tissue microarrays (IMA-TMA): New resource for computer-aided diagnostic algorithm development.

BACKGROUND: Conventional tissue microarrays (TMAs) consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD) algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms. A recently developed digital tool, digital core (dCORE), and image microarray maker (iMAM) enables the capture of uniformly sized and resolution-matched images, with these representing key morphologic features and fields of view, aggregated into a single monolithic digital image file in an array format, which we define as an image microarray (IMA). We further define the TMA-IMA construct as IMA-based images derived from whole slide images of TMAs themselves. METHODS: Here we describe the first combined use of the previously described dCORE and iMAM tools, toward the goal of generating a higher-order image construct, with multiple TMA cores from multiple distinct conventional TMAs assembled as a single digital image montage. This image construct served as the basis of the carrying out of a massively parallel image analysis exercise, based on the use of the previously described spatially invariant vector quantization (SIVQ) algorithm. RESULTS: Multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia were separately rendered, using the aforementioned tools. Each of these two IMAs contained a distinct spectrum of morphologic heterogeneity with respect to both tingible body macrophage (TBM) appearance and apoptotic body morphology. SIVQ-based pattern matching, with ring vectors selected to screen for either tingible body macrophages or apoptotic bodies, was subsequently carried out on the differing TMA-IMAs, with attainment of excellent discriminant classification between the two diagnostic classes. CONCLUSION: The TMA-IMA construct enables and accelerates high-throughput multicase, multifield based image feature discovery and classification, thus simplifying the development, validation, and comparison of CAD algorithms in settings where the heterogeneity of diagnostic feature morphologic is a significant factor.

Application of a rules-based natural language parser to critical value reporting in anatomic pathology.

Critical values in anatomic pathology are rare occurrences and difficult to define with precision. Nevertheless, accrediting institutions require effective and timely communication of all critical values generated by clinical and anatomic laboratories. Provisional gating criteria for potentially critical anatomic diagnoses have been proposed, with some success in their implementation reported in the literature. Ensuring effective communication is challenging, however, making the case for programmatic implementation of a turnkey-style integrated information technology solution. To address this need, we developed a generically deployable laboratory information system-based tool, using a tiered natural language processing predicate calculus inference engine to identify qualifying cases that meet criteria for critical diagnoses but lack an indication in the electronic medical record for an appropriate clinical discussion with the ordering physician of record. Using this tool, we identified an initial cohort of 13,790 cases over a 49-month period, which were further explored by reviewing the available electronic medical record for each patient. Of these cases, 35 (0.3%) were judged to require intervention in the form of direct communication between the attending pathologist and the clinical physician of record. In 8 of the 35 cases, this intervention resulted in the conveyance of new information to the requesting physician and/or a change in the patient's clinical plan. The very low percentage of such cases (0.058%) illustrates their rarity in daily practice, making it unlikely that manual identification/notification approaches alone can reliably manage them. The automated turnkey system was useful in avoiding missed handoffs of significant, clinically actionable diagnoses.

Analysis of factorial time-course microarrays with application to a clinical study of burn injury.

Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body's response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package "TANOVA" and submitted to the Comprehensive R Archive Network at http://www.r-project.org/. It is also available for download at http://gluegrant1.stanford.edu/TANOVA/.