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T cells reactive against immunodominant regions of inducible heat shock proteins (HSPs) have been identified in the chronic inflammatory lesions of several experimental autoimmune diseases. Since HSPs are known to be induced by a number of renal tubular epithelial cell toxins associated with chronic interstitial nephritis, we investigated the relevance of HSP expression and T cell reactivity to HSP70 in a model of progressive inflammatory interstitial nephritis. Chronic administration of cadmium chloride (CdCl2) to SJL/J mice induces HSP70 expression in renal tubular cells 4-5 wk before the development of interstitial mononuclear cell infiltrates. CdCl2 also induces HSP70 expression in cultured tubular epithelial cells from SJL/J mice. CD4+, TCR-alpha/beta+ T cell lines specific for an immunodominant HSP peptide are cytotoxic to heat stressed or CdCl2-treated renal tubular cells. Such HSP-reactive T cells mediate an inflammatory interstitial nephritis after adoptive transfer to CdCl2-treated mice at a time when immunoreactive HSP70 is detectable in the kidneys, but before the development of interstitial mononuclear cell infiltrates. T cells isolated from the nephritic kidneys of mice treated with CdCl2 for 13 wk are also cytotoxic to heat shocked or cadmium-treated tubular cells. These kidney-derived T cells additionally induced interstitial nephritis after passive transfer, indicating their pathogenic significance. Our studies strongly support a role for HSP-reactive T cells in CdCl2-induced interstitial nephritis and suggest that the induction of HSPs in the kidney by a multitude of "non-immune" events may initiate or facilitate inflammatory damage by HSP-reactive lymphocytes.

2-Amino-O4-benzylpteridine derivatives inactivate the human DNA repair protein O6-alkylguanine-DNA alkyltransferase and have been tested as modulators of alkylating agent chemotherapy. Recently, the therapeutic potential of O4-benzylfolate (O4BF) in modulating bis-chloroethylnitrosourea (BCNU) toxicity was demonstrated in vitro using human HT29 and KB tumor lines. The current studies replicated the previous findings in HT29 and KB cells using ATP as an endpoint. However, the effective treatment conditions were severely toxic to human neutrophil progenitors called CFU-granulocyte/macrophage (CFU-GM), which could not tolerate > or =40 microM BCNU at any O4BF concentration. A lower BCNU concentration (10 microM) in combination with O4BF (2-100 microM) was only moderately tumoricidal. To screen for conditions tolerated by CFU-GM, bone marrow (BM) cells were pre-incubated (5 h) with O4BF, co-treated with O4BF and BCNU (42 h), washed, and plated to quantify CFU-GM survival. O4BF at 2 or 5 microM progressively lowered the inhibitory concentrations (ICs) for BCNU, but further increases in O4BF concentrations did not. Increasing O4BF concentrations with constant BCNU (10 microM) under the same prolonged exposure as in the human marrow study achieved only modest tumoricidal effects. In summary, the unexpected finding that normal BM cells are impacted by an agent developed to target malignant tissue refutes speculation that normal beta-folate receptor expressing hematopoietic cells will be spared. Further, the validated IC90 endpoint from the huCFU-GM assay has provided a reference point for judging the potential therapeutic effectiveness of this investigational combination in man using in vitro assays. [ABSTRACT FROM AUTHOR]

Fibrous pseudotumors of the testicular tunics and paratesticular soft tissue are uncommon lesions. They typically arise as painless scrotal masses that may be associated with a hydrocele or history of trauma or infection. Although these lesions are clinically worrisome for a malignant neoplasm, they are thought to be reactive in nature, since they are composed of dense fibrous tissue with interspersed bland fibroblasts and myofibroblasts and mixed inflammatory cells. Once excised, these lesions behave in a benign fashion. Typically, these masses are multinodular, but in rare cases they are diffuse, bandlike myofibroblastic proliferations that encase the testis and are termed fibromatous periorchitis. Herein, we describe a 57-year-old man with a diffuse fibrous pseudotumor/fibromatous periorchitis that encased the left testis and adnexa and arose in close apposition to an inflamed hydrocele. [ABSTRACT FROM AUTHOR]

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION ( F unctional U nit S tate I dentificati ON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.

HistoLens is an open-source graphical user interface developed using MATLAB AppDesigner for visual and quantitative analysis of histological datasets. HistoLens enables users to interrogate sets of digitally annotated whole slide images to efficiently characterize histological differences between disease and experimental groups. Users can dynamically visualize the distribution of 448 hand-engineered features quantifying color, texture, morphology, and distribution across microanatomic sub-compartments. Additionally, users can map differentially detected image features within the images by highlighting affected regions. We demonstrate the utility of HistoLens to identify hand-engineered features that correlate with pathognomonic renal glomerular characteristics distinguishing diabetic nephropathy and amyloid nephropathy from the histologically unremarkable glomeruli in minimal change disease. Additionally, we examine the use of HistoLens for glomerular feature discovery in the Tg26 mouse model of HIV-associated nephropathy. We identify numerous quantitative glomerular features distinguishing Tg26 transgenic mice from wild-type mice, corresponding to a progressive renal disease phenotype. Thus, we demonstrate an off-the-shelf and ready-to-use toolkit for quantitative renal pathology applications., (© 2024. The Author(s).)

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., Competing Interests: JvL has been a member of the advisory boards of Philips, Netherlands and ContextVision, Sweden, and received research funding from Philips, Netherlands, ContextVision, Sweden, and Sectra, Sweden in the last 5 years. He is chief scientific officer (CSO) and shareholder of Aiosyn BV, Netherlands. JeK is a consultant for Aiosyn BV and Novartis AG Switzerland and received speaker fees from Chiesi Pharmaceuticals, Netherlands. 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 Farris, Alexander, Balis, Barisoni, Boor, Bülow, Cornell, Demetris, Farkash, Hermsen, Hogan, Kain, Kers, Kong, Levenson, Loupy, Naesens, Sarder, Tomaszewski, van der Laak, van Midden, Yagi and Solez.)

The incorporation of automated computational tools has a great amount of potential to positively influence the field of pathology. However, pathologists and regulatory agencies are reluctant to trust the output of complex models such as Convolutional Neural Networks (CNNs) due to their usual implementation as black-box tools. Increasing the interpretability of quantitative analyses is a critical line of research in order to increase the adoption of modern Machine Learning (ML) pipelines in clinical environments. Towards that goal, we present HistoLens , a Graphical User Interface (GUI) designed to facilitate quantitative assessments of datasets of annotated histological compartments. Additionally, we introduce the use of hand-engineered feature visualizations to highlight regions within each structure that contribute to particular feature values. These feature visualizations can then be paired with feature hierarchy determinations in order to view which regions within an image are significant to a particular sub-group within the dataset. As a use case, we analyzed a dataset of old and young mouse kidney sections with glomeruli annotated. We highlight some of the functional components within HistoLens that allow non-computational experts to efficiently navigate a new dataset as well as allowing for easier transition to downstream computational analyses.

Podocyte injury plays a crucial role in the progression of diabetic kidney disease (DKD). Injured podocytes demonstrate variations in nuclear shape and chromatin distribution. These morphometric changes have not yet been quantified in podocytes. Furthermore, the molecular mechanisms underlying these variations are poorly understood. Recent advances in omics have shed new lights into the biological mechanisms behind podocyte injury. However, there currently exists no study analyzing the biological mechanisms underlying podocyte morphometric variations during DKD. First, to study the importance of nuclear morphometrics, we performed morphometric quantification of podocyte nuclei from whole slide images of renal tissue sections obtained from murine models of DKD. Our results indicated that podocyte nuclear textural features demonstrate statistically significant difference in diabetic podocytes when compared to control. Additionally, the morphometric features demonstrated the existence of multiple subpopulations of podocytes suggesting a potential cause for their varying response to injury. Second, to study the underlying pathophysiology, we employed single cell RNA sequencing data from the murine models. Our results again indicated five subpopulations of podocytes in control and diabetic mouse models, validating the morphometrics-based results. Additionally, gene set enrichment analysis revealed epithelial to mesenchymal transition and apoptotic pathways in a subgroup of podocytes exclusive to diabetic mice, suggesting the molecular mechanism behind injury. Lastly, our results highlighted two distinct lineages of podocytes in control and diabetic cases suggesting a phenotypical change in podocytes during DKD. These results suggest that textural variations in podocyte nuclei may be key to understanding the pathophysiology behind podocyte injury.

Background: Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise., Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate the generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic GN, and dose-dependent direct podocyte toxicity and depletion, and in human biopsies from steroid-resistant nephrotic syndrome and from human autopsy tissues., Results: The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic acid-Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end users., Conclusions: Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically stained WSIs, facilitating podocyte research, and enabling possible future clinical applications., (Copyright © 2021 by the American Society of Nephrology.)

Rationale: Lupus podocytopathy (LP) is an entity that is increasingly being reported in the literature on systemic lupus erythematosus (SLE). LP is characterized by nephrotic syndrome in SLE patients with diffuse glomerular podocyte foot process effacement and no immune complex deposits along the capillary loops. Histologically, LP typically mimics minimal change disease or primary focal segmental glomerulosclerosis (FSGS) on a background of ISN/RPS class I or II lupus nephritis. In situations where there are coexistent glomerular diseases, however, LP may be easily masked by background lesions and overlapping clinical symptoms., Patient Concerns: We report the case of a 24-year-old woman with type I diabetes, hypertension, psoriasis/rash, and intermittent arthritis who presented with abrupt onset of severe nephrotic proteinuria and renal insufficiency. Renal biopsy revealed nodular glomerulosclerosis and FSGS. Immune deposits were not identified by immunofluorescence or electron microscopy. Ultrastructurally, there was diffuse glomerular basement membrane thickening and over 90% podocyte foot process effacement. With no prior established diagnosis of SLE, the patient was initially diagnosed with diabetic nephropathy with coexistent FSGS, and the patient was started on angiotensin-converting enzyme inhibitors (ACEI) and diuretics. However, nephrotic proteinuria persisted and renal function deteriorated. The patient concurrently developed hemolytic anemia with pancytopenia., Diagnoses: Subsequent to the biopsy, serologic results showed positive autoantibodies against double strand DNA (dsDNA), Smith antigen, ribonucleoprotein (RNP), and Histone. A renal biopsy was repeated, revealing essentially similar findings to those of the previous biopsy. Integrating serology and clinical presentation, SLE was favored. The pathology findings were re-evaluated and considered to be most consistent with LP and coexistent diabetic nephropathy, with superimposed FSGS either as a component of LP or as a lesion secondary to diabetes or hypertension., Interventions: The patient was started on high-dose prednisone at 60 mg/day, with subsequent addition of mycophenolate mofetil and ACEI, while prednisone was gradually tapered., Outcomes: The patient's proteinuria, serum creatinine, complete blood counts, skin rash, and arthritis were all significantly improved., Conclusion: The diagnosis of LP when confounded by other glomerular diseases that may cause nephrotic syndrome can be challenging. Sufficient awareness of this condition is necessary for the appropriate diagnosis and treatment., Competing Interests: The authors have no conflicts of interests to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2, created an unprecedented need for comprehensive laboratory testing of populations, in order to meet the needs of medical practice and to guide the management and functioning of our society. With the greater New York metropolitan area as an epicenter of this pandemic beginning in March 2020, a consortium of laboratory leaders from the assembled New York academic medical institutions was formed to help identify and solve the challenges of deploying testing. This report brings forward the experience of this consortium, based on the real-world challenges which we encountered in testing patients and in supporting the recovery effort to reestablish the health care workplace. In coordination with the Greater New York Hospital Association and with the public health laboratory of New York State, this consortium communicated with state leadership to help inform public decision-making addressing the crisis. Through the length of the pandemic, the consortium has been a critical mechanism for sharing experience and best practices in dealing with issues including the following: instrument platforms, sample sources, test performance, pre- and post-analytical issues, supply chain, institutional testing capacity, pooled testing, biospecimen science, and research. The consortium also has been a mechanism for staying abreast of state and municipal policies and initiatives, and their impact on institutional and laboratory operations. The experience of this consortium may be of value to current and future laboratory professionals and policy-makers alike, in dealing with major events that impact regional laboratory services., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Histologic examination of interstitial fibrosis and tubular atrophy (IFTA) is critical to determine the extent of irreversible kidney injury in renal disease. The current clinical standard involves pathologist's visual assessment of IFTA, which is prone to inter-observer variability. To address this diagnostic variability, we designed two case studies (CSs), including seven pathologists, using HistomicsTK- a distributed system developed by Kitware Inc. (Clifton Park, NY). Twenty-five whole slide images (WSIs) were classified into a training set of 21 and a validation set of four. The training set was composed of seven unique subsets, each provided to an individual pathologist along with four common WSIs from the validation set. In CS 1, all pathologists individually annotated IFTA in their respective slides. These annotations were then used to train a deep learning algorithm to computationally segment IFTA. In CS 2, manual and computational annotations from CS 1 were first reviewed by the annotators to improve concordance of IFTA annotation. Both the manual and computational annotation processes were then repeated as in CS1. The inter-observer concordance in the validation set was measured by Krippendorff's alpha (KA). The KA for the seven pathologists in CS1 was 0.62 with CI [0.57, 0.67], and after reviewing each other's annotations in CS2, 0.66 with CI [0.60, 0.72]. The respective CS1 and CS2 KA were 0.58 with CI [0.52, 0.64] and 0.63 with CI [0.56, 0.69] when including the deep learner as an eighth annotator. These results suggest that our designed annotation framework refines agreement of spatial annotation of IFTA and demonstrates a human-AI approach to significantly improve the development of computational models.

The primary purpose of the kidney, specifically the glomerulus, is filtration. Filtration is accomplished through the glomerular filtration barrier, which consists of the fenestrated endothelium, glomerular basement membrane, and specialized epithelial cells called podocytes. In pathologic states, such as Diabetes Mellitus (DM) and diabetic kidney disease (DKD), variable glomerular conditions result in podocyte injury and depletion, followed by progressive glomerular injury and DKD progression. In this work we quantified glomerulus and podocyte structural changes in histopathology image data derived from a murine model of DM. Using a variety of image processing techniques, we studied changes in podocyte morphology and intra-glomerular distribution across healthy, mild DM, and DM glomeruli. Our feature analysis provided feature trends which we believe are reflective of DKD pathology; while glomerular area peaked in mild DM, average podocyte number and distance from the urinary pole continued to decrease and increase, respectively, throughout DM. Ultimately, this study aims to augment the set of quantifiable image biomarkers used for evaluation of DKD progression in digital pathology, as well as underscore the importance of engineering biologically-inspired image features.

In the age of modern medicine and artificial intelligence, image analysis and machine learning have revolutionized diagnostic pathology, facilitating the development of computer aided diagnostics (CADs) which circumvent prevalent diagnostic challenges. Although CADs will expedite and improve the precision of clinical workflow, their prognostic potential, when paired with clinical outcome data, remains indeterminate. In high impact renal diseases, such as diabetic nephropathy and lupus nephritis (LN), progression often occurs rapidly and without immediate detection, due to the subtlety of structural changes in transient disease states. In such states, exploration of quantifiable image biomarkers, such as Neutrophil Extracellular Traps (NETs), may reveal alternative progression measures which correlate with clinical data. NETs have been implicated in LN as immunogenic cellular structures, whose occurrence and dysregulation results in excessive tissue damage and lesion manifestation. We propose that renal biopsy NET distribution will function as a discriminate, predictive biomarker in LN, and will supplement existing classification schemes. We have developed a computational pipeline for segmenting NET-like structures in LN biopsies. NET-like structures segmented from our biopsies warrant further study as they appear pathologically distinct, and resemble non-lytic, vital NETs. Examination of corresponding H&E regions predominantly placed NET-like structures in glomeruli, including globally and segmentally sclerosed glomeruli, and tubule lumina. Our work continues to explore NET-like structures in LN biopsies by: 1.) revising detection and analytical methods based on evolving NETs definitions, and 2.) cataloguing NET morphology in order to implement supervised classification of NET-like structures in histopathology images.

Generative adversarial networks (GANs) have received immense attention in the field of machine learning for their potential to learn high-dimensional and real data distribution. These methods do not rely on any assumptions about the data distribution of the input sample and can generate real-like samples from latent vector space based on unsupervised learning. In the medical field, particularly, in digital pathology expert annotation and availability of a large set of training data is costly and the study of manifestations of various diseases is based on visual examination of stained slides. In clinical practice, various staining information is required to improve the pathological diagnosis process. But when the sampled tissue to be examined is limited, the final diagnosis made by the pathologist is based on limited stain styles. These limitations can be overcome by studying the usability and reliability of generative models in the field of digital pathology. To understand the usability of the generative models, we synthesize in an unsupervised manner, high resolution renal microanatomical structures like renal glomerulus in thin tissue histology images using state-of-art architectures like Deep Convolutional Generative Adversarial Network (DCGAN) and Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). Successful generation of such structures will lead to obtaining a large set of labeled data for further developing supervised algorithms for disease classification and understanding progression. Our study suggests while GAN is able to attain formalin fixed and paraffin embedded tissue image quality, GAN requires further prior knowledge as input to model intrinsic micro-anatomical details, such as capillary wall, urinary pole, nuclei placement, suggesting developing semi-supervised architectures by using these above details as prior information. Also, the generative models can be used to create an artificial effect of staining without physically tampering the histopathological slide. To demonstrate this, we use a CycleGAN network to transform Hematoxylin and eosin (H&E) stain to Periodic acid-Schiff (PAS) stain and Jones methenamine silver (JMS) stain to PAS stain. In this way GAN can be employed to translate different renal pathology stain styles when the relevant staining information is not available in the clinical settings.

Background: Pathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation., Methods: We developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification., Results: Our digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen's kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ 1 = 0.68, 95% interval [0.50, 0.86] and κ 2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity., Conclusions: Computationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics., (Copyright © 2019 by the American Society of Nephrology.)

Context.—: Advancements in genomic, computing, and imaging technology have spurred new opportunities to use quantitative image analysis (QIA) for diagnostic testing., Objective.—: To develop evidence-based recommendations to improve accuracy, precision, and reproducibility in the interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) for breast cancer where QIA is used., Design.—: The College of American Pathologists (CAP) convened a panel of pathologists, histotechnologists, and computer scientists with expertise in image analysis, immunohistochemistry, quality management, and breast pathology to develop recommendations for QIA of HER2 IHC in breast cancer. A systematic review of the literature was conducted to address 5 key questions. Final recommendations were derived from strength of evidence, open comment feedback, expert panel consensus, and advisory panel review., Results.—: Eleven recommendations were drafted: 7 based on CAP laboratory accreditation requirements and 4 based on expert consensus opinions. A 3-week open comment period received 180 comments from more than 150 participants., Conclusions.—: To improve accurate, precise, and reproducible interpretation of HER2 IHC results for breast cancer, QIA and procedures must be validated before implementation, followed by regular maintenance and ongoing evaluation of quality control and quality assurance. HER2 QIA performance, interpretation, and reporting should be supervised by pathologists with expertise in QIA.

In diabetic nephropathy (DN), hyperglycemia drives a progressive thickening of glomerular filtration surfaces, increased cell proliferation as well as mesangial expansion and a constriction of capillary lumens. This leads to progressive structural changes inside the Glomeruli. In this work, we make a study of structural glomerular changes in DN from a graph-theoretic standpoint, using features extracted from Minimal Spanning Trees (MSTs) constructed over intercellular distances in order to classify the "packing signatures" of different DN stages. We further investigate the significance of the competing effects of Volume change measured here in 2Dimensional Pixel span area (Area) on one hand and increased cell proliferation on the other in determining the packing patterns. Towards that we formulate the problem as Dynamic Bayesian Network (DBN). From our preliminary results we do postulate that volume expansion caused by internal pressure as capillary lumens constriction has perhaps has a greater effect in the early stages.

Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data., Competing Interests: Competing interests The authors declare no competing interests.

The presence of cancer stem cells (CSCs) and the induction of epithelial-to-mesenchymal transition (EMT) in tumors are associated with tumor aggressiveness, metastasis, drug resistance, and poor prognosis, necessitating the development of reagents for unambiguous detection of CSC- and EMT-associated proteins in tumor specimens. To this end, we generated novel antibodies to EMT- and CSC-associated proteins, including Goosecoid, Sox9, Slug, Snail, and CD133. Importantly, unlike several widely used antibodies to CD133, the anti-CD133 antibodies we generated recognize epitopes distal to known glycosylation sites, enabling analyses that are not confounded by differences in CD133 glycosylation. For all target proteins, we selected antibodies that yielded the expected target protein molecular weights by Western analysis and the correct subcellular localization patterns by immunofluorescence microscopy assay (IFA); binding selectivity was verified by immunoprecipitation-mass spectrometry and by immunohistochemistry and IFA peptide blocking experiments. Finally, we applied these reagents to assess modulation of the respective markers of EMT and CSCs in xenograft tumor models by IFA. We observed that the constitutive presence of human hepatocyte growth factor (hHGF) in the tumor microenvironment of H596 non-small cell lung cancer tumors implanted in homozygous hHGF knock-in transgenic mice induced a more mesenchymal-like tumor state (relative to the epithelial-like state when implanted in control SCID mice), as evidenced by the elevated expression of EMT-associated transcription factors detected by our novel antibodies. Similarly, our new anti-CD133 antibody enabled detection and quantitation of drug-induced reductions in CD133-positive tumor cells following treatment of SUM149PT triple-negative breast cancer xenograft models with the CSC/focal adhesion kinase (FAK) inhibitor VS-6063. Thus, our novel antibodies to CSC- and EMT-associated factors exhibit sufficient sensitivity and selectivity for immunofluorescence microscopy studies of these processes in preclinical xenograft tumor specimens and the potential for application with clinical samples., Competing Interests: T.N., T.D.P, S.C., R.G.S., J.K., G.W., A.A., L.D., R.J.K., and R.E.P. are affiliated with and received support in the form of salaries from The Frederick National Laboratory for Cancer Research, which is funded by the federal government and operated by Leidos Biomedical Research, Inc. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

American hospitals are increasingly turning to service outsourcing to reduce costs, including laboratory services. Studies of this practice have largely focused on nonacademic medical centers. In contrast, academic medical centers have unique practice environments and unique mission considerations. We sought to elucidate and analyze clinical laboratory outsourcing experiences in US academic medical centers. Seventeen chairs of pathology with relevant experience were willing to participate in in-depth interviews about their experiences. Anticipated financial benefits from joint venture arrangements often eroded after the initial years of the agreement, due to increased test pricing, management fees, duplication of services in support of inpatients, and lack of incentive for utilization control on the part of the for-profit partner. Outsourcing can preclude development of lucrative outreach programs; such programs were successfully launched in several cases after joint ventures were either avoided or terminated. Common complaints included poor test turnaround time and problems with test quality (especially in molecular pathology, microbiology, and flow cytometry), leading to clinician dissatisfaction. Joint ventures adversely affected retention of academically oriented clinical pathology faculty, with adverse effects on research and education, which further exacerbated clinician dissatisfaction due to lack of available consultative expertise. Resident education in pathology and in other disciplines (especially infectious disease) suffered both from lack of on-site laboratory capabilities and from lack of teaching faculty. Most joint ventures were initiated with little or no input from pathology leadership, and input from pathology leadership was seen to have been critical in those cases where such arrangements were declined or terminated., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Blood smear is a crucial diagnostic aid. Quantification of both solitary and overlapping erythrocytes within these smears, directly from their whole slide images (WSIs), remains a challenge. Existing software designed to accomplish the computationally extensive task of hematological WSI analysis is too expensive and is widely unavailable. We have thereby developed a fully automated software targeted for erythrocyte detection and quantification from WSIs. We define an optimal region within the smear, which contains cells that are neither too scarce/damaged nor too crowded. We detect the optimal regions within the smear and subsequently extract all the cells from these regions, both solitary and overlapped, the latter of which undergoes a clump splitting before extraction. The performance was systematically tested on 28 WSIs of blood smears obtained from 13 different species from three classes of the subphylum vertebrata including birds, mammals, and reptiles. These data pose as an immensely variant erythrocyte database with diversity in size, shape, intensity, and textural features. Our method detected [Formula: see text] more cells than that detected from the traditional monolayer and resulted in a testing accuracy of 99.14% for the classification into their respective class (bird, mammal, or reptile) and a testing accuracy of 84.73% for the classification into their respective species. The results suggest the potential employment of this software for the diagnosis of hematological disorders, such as sickle cell anemia.

We demonstrate a simple and effective automated method for the localization of glomeruli in large (~1 gigapixel) histopathological whole-slide images (WSIs) of thin renal tissue sections and biopsies, using an adaptation of the well-known local binary patterns (LBP) image feature vector to train a support vector machine (SVM) model. Our method offers high precision (>90%) and reasonable recall (>70%) for glomeruli from WSIs, is readily adaptable to glomeruli from multiple species, including mouse, rat, and human, and is robust to diverse slide staining methods. Using 5 Intel(R) Core(TM) i7-4790 CPUs with 40 GB RAM, our method typically requires ~15 sec for training and ~2 min to extract glomeruli reproducibly from a WSI. Deploying a deep convolutional neural network trained for glomerular recognition in tandem with the SVM suffices to reduce false positives to below 3%. We also apply our LBP-based descriptor to successfully detect pathologic changes in a mouse model of diabetic nephropathy. We envision potential clinical and laboratory applications for this approach in the study and diagnosis of glomerular disease, and as a means of greatly accelerating the construction of feature sets to fuel deep learning studies into tissue structure and pathology.

Circulating tumor cells (CTCs) are increasingly employed for research and clinical monitoring of cancer, though most current methods do not permit the isolation of non-epithelial tumor cells. Furthermore, CTCs isolated with antibody-dependent methods are not suitable for downstream experimental uses, including in vitro culturing and implantation in vivo. In the present study, we describe the development, validation, and transfer across laboratories of a new antibody-independent device for the enrichment of CTCs from blood samples of patients with various cancer diagnoses. The ApoStream® device uses dielectrophoresis (DEP) field-flow assist to separate non-hematopoietic cells from the peripheral blood mononuclear fraction by exposing cells in a laminar flow stream to a critical alternating current frequency. The ApoStream® device was calibrated and validated in a formal cross-laboratory protocol using 3 different cancer cell lines spanning a range of distinct phenotypes (A549, MDA-MB-231, and ASPS-1). In spike-recovery experiments, cancer cell recovery efficiencies appeared independent of spiking level and averaged between 68% and 55%, depending on the cell line. No inter-run carryover was detected in control samples. Moreover, the clinical-readiness of the device in the context of non-epithelial cancers was evaluated with blood specimens from fifteen patients with metastatic sarcoma. The ApoStream® device successfully isolated CTCs from all patients with sarcomas examined, and the phenotypic heterogeneity of the enriched cells was demonstrated by fluorescence in situ hybridization or with multiplex immunophenotyping panels. Therefore, the ApoStream® technology expands the clinical utility of CTC evaluation to mesenchymal cancers.

The glomerulus is the blood filtering unit of the kidney. Each human kidney contains [Formula: see text] glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features. Computational quantification of equivalent features promises to improve the precision of glomerular structural analysis. One large obstacle to the computational quantification of renal tissue is the identification of complex glomerular boundaries automatically. To mitigate this issue, we developed a computational pipeline capable of extracting and exactly defining glomerular boundaries. Our method, composed of Gabor filtering, Gaussian blurring, statistical [Formula: see text]-testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of [Formula: see text] and accuracy of 0.92, on [Formula: see text] glomeruli images stained with standard renal histological stains. Our method will simplify computational partitioning of glomerular microcompartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic and can pioneer real-time diagnoses and interventions for renal care.

Phosphorylated H2AX (γ-H2AX) is a sensitive marker for DNA double-strand breaks (DSBs), but the variability of H2AX expression in different cell and tissue types makes it difficult to interpret the meaning of the γ-H2AX level. Furthermore, the assays commonly used for γ-H2AX detection utilize laborious and low-throughput microscopy-based methods. We describe here an ELISA assay that measures both phosphorylated H2AX and total H2AX absolute amounts to determine the percentage of γ-H2AX, providing a normalized value representative of the amount of DNA damage. We demonstrate the utility of the assay to measure DSBs introduced by either ionizing radiation or DNA-damaging agents in cultured cells and in xenograft models. Furthermore, utilizing the NCI-60 cancer cell line panel, we show a correlation between the basal fraction of γ-H2AX and cellular mutation levels. This additional application highlights the ability of the assay to measure γ-H2AX levels in many extracts at once, making it possible to correlate findings with other cellular characteristics. Overall, the γ-H2AX ELISA represents a novel approach to quantifying DNA damage, which may lead to a better understanding of mutagenic pathways in cancer and provide a useful biomarker for monitoring the effectiveness of DNA-damaging anticancer agents., Competing Interests: J.J., Y.Z., and R.E.P. are affiliated with and received support in the form of salaries from The Frederick National Laboratory for Cancer Research, which is funded by the federal government and operated by Leidos Biomedical Research, Inc. L.K.F. received support in the form of a salary from Kelly Government Solutions, a division of Kelly Services. A U.S. patent has been filed on the γ-H2AX assay by the National Cancer Institute (PCT Application No. PCT/US2016/016000). This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

The tumor suppressor p53 plays a critical role in suppressing cancer growth and progression and is an attractive target for the development of new targeted therapies. We synthesized several indolo-pyrido-isoquinolin based alkaloids to activate p53 function and examined their therapeutic efficacy using NCI-60 screening. Here, we provide molecular evidence that one of these compounds, 11-methoxy-2,3,4,13-tetrahydro-1H-indolo[2',3':3,4]pyrido[1,2-b]isoquinolin-6-ylium-bromide (termed P18 or NSC-768219) inhibits growth and clonogenic potential of cancer cells. P18 treatment results in downregulation of mesenchymal markers and concurrent upregulation of epithelial markers as well as inhibition of migration and invasion. Experimental epithelial-mesenchymal-transition (EMT) induced by exposure to TGFβ/TNFα is also completely reversed by P18. Importantly, P18 also inhibits mammosphere-formation along with a reduction in the expression of stemness factors, Oct4, Nanog and Sox2. We show that P18 induces expression, phosphorylation and accumulation of p53 in cancer cells. P18-mediated induction of p53 leads to increased nuclear localization and elevated expression of p53 target genes. Using isogenic cancer cells differing only in p53 status, we show that p53 plays an important role in P18-mediated alteration of mesenchymal and epithelial genes, inhibition of migration and invasion of cancer cells. Furthermore, P18 increases miR-34a expression in p53-dependent manner and miR-34a is integral for P18-mediated inhibition of growth, invasion and mammosphere-formation. miR-34a mimics potentiate P18 efficacy while miR-34a antagomirs antagonize P18. Collectively, these data provide evidence that P18 may represent a promising therapeutic strategy for the inhibition of growth and progression of breast cancer and p53-miR-34a axis is important for P18 function., (Copyright © 2016 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.)

Purpose: Rational development of targeted MET inhibitors for cancer treatment requires a quantitative understanding of target pharmacodynamics, including molecular target engagement, mechanism of action, and duration of effect., Experimental Design: Sandwich immunoassays and specimen handling procedures were developed and validated for quantifying full-length MET and its key phosphospecies (pMET) in core tumor biopsies. MET was captured using an antibody to the extracellular domain and then probed using antibodies to its C-terminus (full-length) and epitopes containing pY1234/1235, pY1235, and pY1356. Using pMET:MET ratios as assay endpoints, MET inhibitor pharmacodynamics were characterized in MET-amplified and -compensated (VEGFR blockade) models., Results: By limiting cold ischemia time to less than two minutes, the pharmacodynamic effects of the MET inhibitors PHA665752 and PF02341066 (crizotinib) were quantifiable using core needle biopsies of human gastric carcinoma xenografts (GTL-16 and SNU5). One dose decreased pY1234/1235 MET:MET, pY1235-MET:MET, and pY1356-MET:MET ratios by 60% to 80% within 4 hours, but this effect was not fully sustained despite continued daily dosing. VEGFR blockade by pazopanib increased pY1235-MET:MET and pY1356-MET:MET ratios, which was reversed by tivantinib. Full-length MET was quantifiable in 5 of 5 core needle samples obtained from a resected hereditary papillary renal carcinoma, but the levels of pMET species were near the assay lower limit of quantitation., Conclusions: These validated immunoassays for pharmacodynamic biomarkers of MET signaling are suitable for studying MET responses in amplified cancers as well as compensatory responses to VEGFR blockade. Incorporating pharmacodynamic biomarker studies into clinical trials of MET inhibitors could provide critical proof of mechanism and proof of concept for the field. Clin Cancer Res; 22(14); 3683-94. ©2016 AACR., (©2016 American Association for Cancer Research.)

Purpose: To support clinical pharmacodynamic evaluation of the Smac mimetic TL32711 (birinapant) and other apoptosis-targeting drugs, we describe the development, validation, and application of novel immunoassays for 15 cytosolic and membrane-associated proteins indicative of the induction, onset, and commitment to apoptosis in human tumors., Experimental Design: The multiplex immunoassays were constructed on the Luminex platform with apoptosis biomarkers grouped into three panels. Panel 1 contains Bak, Bax, total caspase-3, total lamin-B (intact and 45 kDa fragment), and Smac; panel 2 contains Bad, Bax-Bcl-2 heterodimer, Bcl-xL, Bim, and Mcl1; and panel 3 contains active (cleaved) caspase-3, Bcl-xL-Bak heterodimer, Mcl1-Bak heterodimer, pS99-Bad, and survivin. Antibody specificity was confirmed by immunoprecipitation and Western blot analysis., Results: Two laboratories analytically validated the multiplex immunoassays for application with core-needle biopsy samples processed to control preanalytical variables; the biologic variability for each biomarker was estimated from xenograft measurements. Studies of TL32711 in xenograft models confirmed a dose-dependent increase in activated caspase-3 6 hours after dosing and provided assay fit-for-purpose confirmation. Coincident changes in cytosolic lamin-B and subsequent changes in Bcl-xL provided correlative evidence of caspase-3 activation. The validated assay is suitable for use with clinical specimens; 14 of 15 biomarkers were quantifiable in patient core-needle biopsies., Conclusions: The validated multiplex immunoassays developed for this study provided proof of mechanism data for TL32711 and are suitable for quantifying apoptotic biomarkers in clinical trials., (©2015 American Association for Cancer Research.)

Context: We define the scope and needs within the new discipline of computational pathology, a discipline critical to the future of both the practice of pathology and, more broadly, medical practice in general., Objective: To define the scope and needs of computational pathology., Data Sources: A meeting was convened in Boston, Massachusetts, in July 2014 prior to the annual Association of Pathology Chairs meeting, and it was attended by a variety of pathologists, including individuals highly invested in pathology informatics as well as chairs of pathology departments., Conclusions: The meeting made recommendations to promote computational pathology, including clearly defining the field and articulating its value propositions; asserting that the value propositions for health care systems must include means to incorporate robust computational approaches to implement data-driven methods that aid in guiding individual and population health care; leveraging computational pathology as a center for data interpretation in modern health care systems; stating that realizing the value proposition will require working with institutional administrations, other departments, and pathology colleagues; declaring that a robust pipeline should be fostered that trains and develops future computational pathologists, for those with both pathology and nonpathology backgrounds; and deciding that computational pathology should serve as a hub for data-related research in health care systems. The dissemination of these recommendations to pathology and bioinformatics departments should help facilitate the development of computational pathology.

Proteasome inhibitors have a 20 year history in cancer therapy. The first proteasome inhibitor, bortezomib (Velcade, PS-341), a break-through multiple myeloma treatment, moved rapidly through development from bench in 1994 to first approval in 2003. Bortezomib is a reversible boronic acid inhibitor of the chymotrypsin-like activity of the proteasome. Next generation proteasome inhibitors include carfilzomib and oprozomib which are irreversible epoxyketone proteasome inhibitors; and ixazomib and delanzomib which are reversible boronic acid proteasome inhibitors. Two proteasome inhibitors, bortezomib and carfilzomib are FDA approved drugs and ixazomib and oprozomib are in late stage clinical trials. All of the agents are potent cytotoxics. The disease focus for all the proteasome inhibitors is multiple myeloma. This focus arose from clinical observations made in bortezomib early clinical trials. Later preclinical studies confirmed that multiple myeloma cells were indeed more sensitive to proteasome inhibitors than other tumor cell types. The discovery and development of the proteasome inhibitor class of anticancer agents has progressed through a classic route of serendipity and scientific investigation. These agents are continuing to have a major impact in their treatment of hematologic malignancies and are beginning to be explored as potential treatment agent for non-cancer indications., (Published by Elsevier Inc.)

Background: Notch1 transmembrane receptor is activated through ligand-binding- triggered proteolytic cleavages and, upon release, the intracellular domain (N1-ICD) translocates into the nucleus and modulates target gene transcriptions. Notch activation has been implicated in tumorigenesis in an increasing number of human malignancies including non-small cell lung cancer (NSCLC). However, Notch1 in distinct expression patterns and activation status with tumor progression remains to be defined in NSCLC., Methods: Notch1 and activated Notch1, N1-ICD, were examined by immunohistochemistry in 58 cases of stage I to IV NSCLC tumors. Association between Notch1 or N1-ICD expression and clinicopathological factors was assessed via correlation coefficient r statistics. P-values are two-sided., Results: Detectable tumor Notch1, predominantly localized to the membrane and cytoplasm, was observed in 29 cases (50%, 95% Blyth-Still-Casella confidence interval 37 - 63%). It was negatively associated with stage (r=- 0.43, P<0.001) and nodal status (r=- 0.33, P = 0.01), but not tumor size. In contrast, nuclear N1-ICD expression level was low and found in 12% of NSCLC patients, neither significantly associated with stage nor nodal status. Upon Notch1 activation in vitro, a mostly extra-nuclear staining was substantially turned into the nuclear signal in cancer cells., Conclusions: Notch1 in the largely inactivated phenotype is inversely associated with clinical stage progression in NSCLC. Notch1, rather than activated N1-ICD, may be a context-dependent restrictive factor to nodal metastasis.

In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

Hypoxia-inducible factor-1 alpha (HIF-1α) is an important marker of hypoxia in human tumors and has been implicated in tumor progression. Drugs targeting HIF-1α are being developed, but the ability to measure drug-induced changes in HIF-1α is limited by the lability of the protein in normoxia. Our goal was to devise methods for specimen collection and processing that preserve HIF-1α in solid tumor tissues and to develop and validate a two-site chemiluminescent quantitative enzyme-linked immunosorbent assay (ELISA) for HIF-1α. We tested various strategies for HIF-1α stabilization in solid tumors, including nitrogen gas-purged lysis buffer, the addition of proteasome inhibitors or the prolyl hydroxylase inhibitor 2-hydroxyglutarate, and bead homogenization. Degassing and the addition of 2-hydroxyglutarate to the collection buffer significantly increased HIF-1α recovery, whereas bead homogenization in sealed tubes improved HIF-1α recovery and reduced sample variability. Validation of the ELISA demonstrated intra- and inter-assay variability of less than 15% and accuracy of 99.8±8.3% as assessed by spike recovery. Inter-laboratory reproducibility was also demonstrated (R(2)=0.999). Careful sample handling techniques allow us to quantitatively detect HIF-1α in samples as small as 2.5μg of total protein extract, and this method is currently being applied to analyze tumor biopsy specimens in early-phase clinical trials., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images., Materials and Methods: This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (Az) was calculated for each of five lesion classification tasks involving triple-negative breast cancers., Results: For each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an Az of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non-triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma., Conclusion: Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women., (© RSNA, 2014.)

Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly ([Formula: see text]) better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms.

Context: Co-registration of ex-vivo histologic images with pre-operative imaging (e.g., magnetic resonance imaging [MRI]) can be used to align and map disease extent, and to identify quantitative imaging signatures. However, ex-vivo histology images are frequently sectioned into quarters prior to imaging., Aims: This work presents Histostitcher™, a software system designed to create a pseudo whole mount histology section (WMHS) from a stitching of four individual histology quadrant images., Materials and Methods: Histostitcher™ uses user-identified fiducials on the boundary of two quadrants to stitch such quadrants. An original prototype of Histostitcher™ was designed using the Matlab programming languages. However, clinical use was limited due to slow performance, computer memory constraints and an inefficient workflow. The latest version was created using the extensible imaging platform (XIP™) architecture in the C++ programming language. A fast, graphics processor unit renderer was designed to intelligently cache the visible parts of the histology quadrants and the workflow was significantly improved to allow modifying existing fiducials, fast transformations of the quadrants and saving/loading sessions., Results: The new stitching platform yielded significantly more efficient workflow and reconstruction than the previous prototype. It was tested on a traditional desktop computer, a Windows 8 Surface Pro table device and a 27 inch multi-touch display, with little performance difference between the different devices., Conclusions: Histostitcher™ is a fast, efficient framework for reconstructing pseudo WMHS from individually imaged quadrants. The highly modular XIP™ framework was used to develop an intuitive interface and future work will entail mapping the disease extent from the pseudo WMHS onto pre-operative MRI.

Germ-cell tumors (GCTs) are the most common malignancies in adolescent and young men. These tumors are highly treatable, even at an advanced stage; therefore, accurate diagnosis is imperative. In this study, we evaluated immunohistochemical stains for SALL4, NANOG, glypican-3 (GPC3), D2-40, and CD30 with adequate control in retroperitoneal dissection specimens under the same laboratory conditions. The study groups included 31 cases of metastatic testicular GCTs with the following components: 11 seminomas, 14 embryonal carcinoma (ECs), 12 yolk sac tumor (YSTs), 8 teratomas, 10 cases of metastatic melanomas, 14 cases of malignant lymphomas, and 11 cases of metastatic, poorly differentiated carcinoma. SALL4 showed diffuse nuclear labeling for all seminomas, ECs, and YSTs. NANOG showed diffuse nuclear positivity in all seminomas and ECs. Metastatic carcinomas, melanomas, and malignant lymphomas were negative for these 2 markers. Gypican-3, D2-40, and CD30 showed sensitive staining for YSTs, seminomas, and ECs, respectively. In conclusion, SALL4 and NANOG are sensitive and specific markers for GCTs. GPC3, D2-40, and CD30 are sensitive but not specific for individual components of GCTs and may be useful in aiding in the differential diagnosis for the individual component of GCTs when the identity of GCT is established.