Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks.

The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.

Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

A community-based approach to image analysis of cells, tissues and tumors.

Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on "Computational Challenges Shared by Diverse Imaging Platforms" to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).

Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification.

INTRODUCTION: Digital pathology improves the standardization and reproducibility of kidney biopsy specimen assessment. We developed a pipeline allowing the analysis of many images without requiring human preprocessing and illustrate its use with a simple algorithm for quantification of interstitial fibrosis on a large dataset of kidney allograft biopsy specimens. METHODS: Masson trichrome-stained images from kidney allograft biopsy specimens were used to train and validate a glomeruli detection algorithm using a VGG19 convolutional neural network and an automatic cortical region of interest (ROI) selection algorithm including cortical regions containing all predicted glomeruli. A positive-pixel count algorithm was used to quantify interstitial fibrosis on the ROIs and the association between automatic fibrosis and pathologist evaluation, estimated glomerular filtration rate (GFR) and allograft survival was assessed. RESULTS: The glomeruli detection (F1 score of 0.87) and ROIs selection (F1 score 0.83 [SD 0.13]) algorithms displayed high accuracy. The correlation between the automatic fibrosis quantification on manually and automatically selected ROIs was high (r = 1.00 [0.99-1.00]). Automatic fibrosis quantification was only moderately correlated with pathologists' assessment and was not significantly associated with eGFR or allograft survival. CONCLUSION: This pipeline can automatically and accurately detect glomeruli and select cortical ROIs that can easily be used to develop, validate, and apply image analysis algorithms.

Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma.

MRI in combination with genomic markers are critical in the management of gliomas. Radiomics and radiogenomics analysis facilitate the quantitative assessment of tumor properties which can be used to model both molecular subtype and predict disease progression. In this work, we report on the Drosophila gene capicua (CIC) mutation biomarker effects alongside radiomics features on the predictive ability of CIC mutation status in lower-grade gliomas (LGG). Genomic data of lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) (n = 509) and corresponding MR images from TCIA (n = 120) were utilized. Following tumor segmentation, radiomics features were extracted from T1, T2, T2 Flair, and T1 contrast enhanced (CE) images. Lasso feature reduction was used to obtain the most important MR image features and then logistic regression used to predict CIC mutation status. In our study, CIC mutation rarely occurred in Astrocytoma but has a high probability of occurrence in Oligodendroglioma. The presence of CIC mutation was found to be associated with better survival of glioma patients (p < 1e-4, HR: 0.2445), even with co-occurrence of IDH mutation and 1p/19q co-deletion (p = 0.0362, HR: 0.3674). An eleven-feature model achieved glioma prediction accuracy of 94.2% (95% CI, 94.03-94.38%), a six-feature model achieved oligodendroglioma prediction accuracy of 92.3% (95% CI, 91.70-92.92%). MR imaging and its derived image of gliomas with CIC mutation appears more complex and non-uniform but are associated with lower malignancy. Our study identified CIC as a potential prognostic factor in glioma which has close associations with survival. MRI radiomic features could predict CIC mutation, and reflect less malignant manifestations such as milder necrosis and larger tumor volume in MRI and its derived images that could help clinical judgment.

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aß pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice.

Digital imaging applications and informatics in dermatology.

In this chapter, we present the use of whole slide imaging (WSI) and dermoscopy in the field of dermatology. Image digitization has allowed for increasing computer-assisted clinical decision-making. An introduction to common digital imaging data sources such as WSI and dermoscopy is provided. We also review some commonly used image quantification methods and their potential applications in dermatology. Finally, we review how machine learning approaches utilize novel large dermatology image datasets.

Fusion in Breast Cancer Histology Classification.

Breast cancer is a deadly disease that affects millions of women worldwide. The International Conference on Image Analysis and Recognition in 2018 presents the BreAst Cancer Histology (ICIAR2018 BACH) image data challenge that calls for computer tools to assist pathologists and doctors in the clinical diagnosis of breast cancer subtypes. Using the BACH dataset, we have developed an image classification pipeline that combines both a shallow learner (support vector machine) and a deep learner (convolutional neural network). The shallow learner and deep learners achieved moderate accuracies of 79% and 81% individually. When being integrated by fusion algorithms, the system outperformed any individual learner with the highest accuracy as 92%. The fusion presents big potential for improving clinical design support.

TDP-43 cytoplasmic inclusion formation is disrupted in C9orf72-associated amyotrophic lateral sclerosis/frontotemporal lobar degeneration.

The G4C2 hexanucleotide repeat expansion mutation in the C9orf72 gene is the most common genetic cause underlying both amyotrophic lateral sclerosis and frontotemporal dementia. Pathologically, these two neurodegenerative disorders are linked by the common presence of abnormal phosphorylated TDP-43 neuronal cytoplasmic inclusions. We compared the number and size of phosphorylated TDP-43 inclusions and their morphology in hippocampi from patients dying with sporadic versus C9orf72-related amyotrophic lateral sclerosis with pathologically defined frontotemporal lobar degeneration with phosphorylated TDP-43 inclusions, the pathological substrate of clinical frontotemporal dementia in patients with amyotrophic lateral sclerosis. In sporadic cases, there were numerous consolidated phosphorylated TDP-43 inclusions that were variable in size, whereas inclusions in C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration were quantitatively smaller than those in sporadic cases. Also, C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration homogenized brain contained soluble cytoplasmic TDP-43 that was largely absent in sporadic cases. To better understand these pathological differences, we modelled TDP-43 inclusion formation in fibroblasts derived from sporadic or C9orf72-related amyotrophic lateral sclerosis/frontotemporal dementia patients. We found that both sporadic and C9orf72 amyotrophic lateral sclerosis/frontotemporal dementia patient fibroblasts showed impairment in TDP-43 degradation by the proteasome, which may explain increased TDP-43 protein levels found in both sporadic and C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration frontal cortex and hippocampus. Fibroblasts derived from sporadic patients, but not C9orf72 patients, demonstrated the ability to sequester cytoplasmic TDP-43 into aggresomes via microtubule-dependent mechanisms. TDP-43 aggresomes in vitro and TDP-43 neuronal inclusions in vivo were both tightly localized with autophagy markers and, therefore, were likely to function similarly as sites for autophagic degradation. The inability for C9orf72 fibroblasts to form TDP-43 aggresomes, together with the observations that TDP-43 protein was soluble in the cytoplasm and formed smaller inclusions in the C9orf72 brain compared with sporadic disease, suggests a loss of protein quality control response to sequester and degrade TDP-43 in C9orf72-related diseases.

A multiplexed magnetic tweezer with precision particle tracking and bi-directional force control.

BACKGROUND: In the past two decades, methods have been developed to measure the mechanical properties of single biomolecules. One of these methods, Magnetic tweezers, is amenable to aquisition of data on many single molecules simultaneously, but to take full advantage of this "multiplexing" ability, it is necessary to simultaneously incorprorate many capabilities that ahve been only demonstrated separately. METHODS: Our custom built magnetic tweezer combines high multiplexing, precision bead tracking, and bi-directional force control into a flexible and stable platform for examining single molecule behavior. This was accomplished using electromagnets, which provide high temporal control of force while achieving force levels similar to permanent magnets via large paramagnetic beads. RESULTS: Here we describe the instrument and its ability to apply 2-260 pN of force on up to 120 beads simultaneously, with a maximum spatial precision of 12 nm using a variety of bead sizes and experimental techniques. We also demonstrate a novel method for increasing the precision of force estimations on heterogeneous paramagnetic beads using a combination of density separation and bi-directional force correlation which reduces the coefficient of variation of force from 27% to 6%. We then use the instrument to examine the force dependence of uncoiling and recoiling velocity of type 1 fimbriae from Eschericia coli (E. coli) bacteria, and see similar results to previous studies. CONCLUSION: This platform provides a simple, effective, and flexible method for efficiently gathering single molecule force spectroscopy measurements.

Bioseguridad durante el Internado de Medicina en Hospitales de Trujillo ûLa Libertad 2010: a propósito de la muerte de un estudiante de medicina / Biosecurity for the boarding of Medicine Hospital from Trujillo - La Libertad 2010: report of the death of a medical students

Objetivo: Conocer las características de bioseguridad en el internado de Medicina de Trujillo û La Libertad, 2010. Material y método: Se realizó un estudio descriptivo, transversal, en una muestra de 80 internos de medicina de Hospitales de Trujillo - La Libertad durante los meses de julio û agosto 2010, utilizando como instrumento la encuesta del estudio Características del Internado de Medicina en el Perú, 2010. Se realizó estadística descriptiva con frecuencias absolutas y relativas. Resultados: Se encuestó a 80 internos de medicina, sexo masculino (61,2 %) y femenino (38,8%), con edad entre 23 y 27 años. El 40% recibió capacitación en bioseguridad, brindada en un 32,5% por la sede hospitalaria y 7,5% por la universidad, ninguno había recibido material de protección personal al momento del cuestionario, y 13,7% mencionan contar con un seguro contra accidentes laborales. Conclusiones: Los Internos de medicina de los hospitales de la Libertad û Trujillo en su mayoría no cuentan con capacitación oportuna en bioseguridad, ni se les entrega materiales para su protección personal en sus prácticas hospitalarias, considerando además que la gran mayoría están desprovistos de un seguro de protección contra accidentes laborales.

Objectives: To know the features of medicine internship in the main hospitals directed by the Ministry of Health (MINSA) and Peruvian Social Security (EsSalud) in La Libertad, Trujillo. Material and Method: Descriptive, analytical, and cross-sectional study. Results: Eighty medicine interns were interviewed, 61,2% (49) were male and 38.8 (31) were female, and their ages were between 23 and 27 years. Forty percent had training about biosafety; however, none received any technical documents regarding this topic. Some interns declared they had an insurance policy for working place-related accidents and adequate places for rest (13,7% and 17,5%, respectively). Conclusion: Interns lack appropriate and timely training in biosafety issues, and these persons are exposed to a great deal of risk during their last year of medical training in the hospital, and the great majority of them do not have any insurance policy that may protect them from working place-related accidents.

Actualización para el médico no especialista en el tratamiento del VIH / Update for the General Physician in the treatment of the HIV

Despues de dos décadas de disponibilidad de medicamentos antiretrovirales el pronóstico de los pacientes infectados con el virus de la immunodeficiencias adquirida (VIH) ha cambiado dramáticamente. El curso de esta infección ha pasado de ser una infección casi indefectiblemente fatal a una infección crónica con una esperanza de vida potencial casi normal. Eso se ha traducido en dramáticas reducciones en la tasas de mortalidad en países donde existe amplia disponibilidad a estos medicamentos. Con tasas de incidencias relativamente estables o en aumento y disminución de la mortalidad, la prevalenica de esta infección continua aumentando. Eso hace cada vez más probable que el médico no especialista encuentre en su práctica pacientes infectados con VIH. Es por eso que esta revisión esta dirijida principalmente al médico no especialista y tiene como objetivo su actualización dada la creciente complejidad del tratamiento de esta infección. Esta es una revisión superficial pero amplia del estado actual del tratamiento de la infección por el VIH, e incluye los esquemas principales, alternativos y una descripción de las dos nuevas clases de medicamentos recientemente disponibles.

After two decades of availability of antiretroviral therapy, the prognosis of HIV-infected patients has changed dramatically. The curse of this infection has changed from a rapidly fatal infection to a more manageable, chronic infection with an almost normal life span. This has led to a dramatic reduction in the mortality rate related to HIV in countries where antiretroviral therapy is widely available. With incidence rates stable or increasing and lower mortality, the prevalence of this infection is increasing. This fact increases the chances of non-specialist physicians to encounter HIV infected patients in their practice. That is why this revision is directed to the non-specialist physician and its objective is to update their knowledge in the complex area of the management of HIV therapy. This is a broad but superficial revision of the current status of the management of HIV infection therapy, and includes the use of first line and alternative options of therapy as well as the description of two newly approved classes of antiretrovirals.