Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Progression of diabetic kidney disease in T2DN rats.

Diabetic kidney disease (DKD) is one of the leading pathological causes of decreased renal function and progression to end-stage kidney failure. To explore and characterize age-related changes in DKD and associated glomerular damage, we used a rat model of type 2 diabetic nephropathy (T2DN) at 12 wk and older than 48 wk. We compared their disease progression with control nondiabetic Wistar and diabetic Goto-Kakizaki (GK) rats. During the early stages of DKD, T2DN and GK animals revealed significant increases in blood glucose and kidney-to-body weight ratio. Both diabetic groups had significantly altered renin-angiotensin-aldosterone system function. Thereafter, during the later stages of disease progression, T2DN rats demonstrated a remarkable increase in renal damage compared with GK and Wistar rats, as indicated by renal hypertrophy, polyuria accompanied by a decrease in urine osmolarity, high cholesterol, a significant prevalence of medullary protein casts, and severe forms of glomerular injury. Urinary nephrin shedding indicated loss of the glomerular slit diaphragm, which also correlates with the dramatic elevation in albuminuria and loss of podocin staining in aged T2DN rats. Furthermore, we used scanning ion microscopy topographical analyses to detect and quantify the pathological remodeling in podocyte foot projections of isolated glomeruli from T2DN animals. In summary, T2DN rats developed renal and physiological abnormalities similar to clinical observations in human patients with DKD, including progressive glomerular damage and a significant decrease in renin-angiotensin-aldosterone system plasma levels, indicating these rats are an excellent model for studying the progression of renal damage in type 2 DKD.

Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections.

Background Histologic examination of fixed renal tissue is widely used to assess morphology and the progression of disease. Commonly reported metrics include glomerular number and injury. However, characterization of renal histology is a time-consuming and user-dependent process. To accelerate and improve the process, we have developed a glomerular localization pipeline for trichrome-stained kidney sections using a machine learning image classification algorithm.Methods We prepared 4-µm slices of kidneys from rats of various genetic backgrounds that were subjected to different experimental protocols and mounted the slices on glass slides. All sections used in this analysis were trichrome stained and imaged in bright field at a minimum resolution of 0.92 µm per pixel. The training and test datasets for the algorithm comprised 74 and 13 whole renal sections, respectively, totaling over 28,000 glomeruli manually localized. Additionally, because this localizer will be ultimately used for automated assessment of glomerular injury, we assessed bias of the localizer for preferentially identifying healthy or damaged glomeruli.Results Localizer performance achieved an average precision and recall of 96.94% and 96.79%, respectively, on whole kidney sections without evidence of bias for or against glomerular injury or the need for manual preprocessing.Conclusions This study presents a novel and robust application of convolutional neural nets for the localization of glomeruli in healthy and damaged trichrome-stained whole-renal section mounts and lays the groundwork for automated glomerular injury scoring.

Homologous point transformer for multi-modality prostate image registration.

Registration is the process of transforming images so they are aligned in the same coordinate space. In the medical field, image registration is often used to align multi-modal or multi-parametric images of the same organ. A uniquely challenging subset of medical image registration is cross-modality registration-the task of aligning images captured with different scanning methodologies. In this study, we present a transformer-based deep learning pipeline for performing cross-modality, radiology-pathology image registration for human prostate samples. While existing solutions for multi-modality prostate image registration focus on the prediction of transform parameters, our pipeline predicts a set of homologous points on the two image modalities. The homologous point registration pipeline achieves better average control point deviation than the current state-of-the-art automatic registration pipeline. It reaches this accuracy without requiring masked MR images which may enable this approach to achieve similar results in other organ systems and for partial tissue samples.

Dietary protein source contributes to the risk of developing maternal syndrome in the Dahl salt-sensitive rat.

Preeclampsia (PE) is a disorder of pregnancy, which is categorized by hypertension and proteinuria or signs of end-organ damage. Though PE is the leading cause of maternal and fetal morbidity and mortality, the mechanisms leading to PE remain unclear. The present study examined the contribution of dietary protein source (casein versus wheat gluten) to the risk of developing maternal syndrome utilizing two colonies of Dahl salt-sensitive (SS/JrHsdMcwi) rats. While the only difference between the colonies is the diet, the colonies exhibit profound differences in the pregnancy phenotypes. The SS rats maintained on the wheat gluten (SSWG) chow are protected from developing maternal syndrome; however, approximately half of the SS rats fed a casein-based diet (SSC) exhibit maternal syndrome. Those SSC rats that develop pregnancy-specific increases in blood pressure and proteinuria have no observable differences in renal or placental immune profiles compared to the protected SS rats. A gene profile array of placental tissue revealed a downregulation in Nos3 and Cyp26a1 in the SSC rats that develop maternal syndrome accompanied with increases in uterine artery resistance index suggesting the source of this phenotype could be linked to inadequate remodeling within the placenta. Investigations into the effects of multiple pregnancies on maternal health replicated similar findings. The SSC colony displayed an exacerbation in proteinuria, renal hypertrophy and renal immune cell infiltration associated with an increased mortality rate while the SSWG colony were protected highlighting how dietary protein source could have beneficial effects in PE.

Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network.

Purpose: Prostate cancer primarily arises from the glandular epithelium. Histomophometric techniques have been used to assess the glandular epithelium in automated detection and classification pipelines; however, they are often rigid in their implementation, and their performance suffers on large datasets where variation in staining, imaging, and preparation is difficult to control. The purpose of this study is to quantify performance of a pixelwise segmentation algorithm that was trained using different combinations of weak and strong stroma, epithelium, and lumen labels in a prostate histology dataset. Approach: We have combined weakly labeled datasets generated using simple morphometric techniques and high-quality labeled datasets from human observers in prostate biopsy cores to train a convolutional neural network for use in whole mount prostate labeling pipelines. With trained networks, we characterize pixelwise segmentation of stromal, epithelium, and lumen (SEL) regions on both biopsy core and whole-mount H&E-stained tissue. Results: We provide evidence that by simply training a deep learning algorithm on weakly labeled data generated from rigid morphometric methods, we can improve the robustness of classification over the morphometric methods used to train the classifier. Conclusions: We show that not only does our approach of combining weak and strong labels for training the CNN improve qualitative SEL labeling within tissue but also the deep learning generated labels are superior for cancer classification in a higher-order algorithm over the morphometrically derived labels it was trained on.

Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer.

Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Approach: Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized. Data from 28 patients ( n = 33 slides) were sent to five pathologists to be annotated. Slides from the remaining 20 patients ( n = 123 slides) were annotated by one of the five pathologists. Interpathologist variability was measured using Krippendorff's alpha. Pathologist-specific radiopathomic mapping models were trained using a partial least-squares regression using MRI values to predict epithelium density, a known marker for disease severity. An analysis of variance characterized intermodel means difference in epithelium density. A consensus model was created and evaluated using a receiver operator characteristic classifying high grade versus low grade and benign, and was statistically compared to apparent diffusion coefficient (ADC). Results: Interobserver variability ranged from low to acceptable agreement (0.31 to 0.69). There was a statistically significant difference in mean predicted epithelium density values ( p < 0.001 ) between the five models. The consensus model outperformed ADC (areas under the curve = 0.80 and 0.71, respectively, p < 0.05 ). Conclusion: We demonstrate that radiopathomic maps of epithelium density are sensitive to the pathologist annotating the dataset; however, it is unclear if these differences are clinically significant. The consensus model produced the best maps, matched the performance of the best individual model, and outperformed ADC.

Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board-approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging.

Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer.

PURPOSE: This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization. METHODS AND MATERIALS: Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer. RESULTS: The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer. CONCLUSIONS: We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy.

Inhibition of Mammalian Target of Rapamycin Complex 1 Attenuates Salt-Induced Hypertension and Kidney Injury in Dahl Salt-Sensitive Rats.

The goal of the present study was to explore the protective effects of mTORC1 (mammalian target of rapamycin complex 1) inhibition by rapamycin on salt-induced hypertension and kidney injury in Dahl salt-sensitive (SS) rats. We have previously demonstrated that H2O2 is elevated in the kidneys of SS rats. The present study showed a significant upregulation of renal mTORC1 activity in the SS rats fed a 4.0% NaCl for 3 days. In addition, renal interstitial infusion of H2O2 into salt-resistant Sprague Dawley rats for 3 days was also found to stimulate mTORC1 activity independent of a rise of arterial blood pressure. Together, these data indicate that the salt-induced increases of renal H2O2 in SS rats activated the mTORC1 pathway. Daily administration of rapamycin (IP, 1.5 mg/kg per day) for 21 days reduced salt-induced hypertension from 176.0±9.0 to 153.0±12.0 mm Hg in SS rats but had no effect on blood pressure salt sensitivity in Sprague Dawley treated rats. Compared with vehicle, rapamycin reduced albumin excretion rate in SS rats from 190.0±35.0 to 37.0±5.0 mg/d and reduced the renal infiltration of T lymphocytes (CD3+) and macrophages (ED1+) in the cortex and medulla. Renal hypertrophy and cell proliferation were also reduced in rapamycin-treated SS rats. We conclude that enhancement of intrarenal H2O2 with a 4.0% NaCl diet stimulates the mTORC1 pathway that is necessary for the full development of the salt-induced hypertension and kidney injury in the SS rat.

Increased Perfusion Pressure Drives Renal T-Cell Infiltration in the Dahl Salt-Sensitive Rat.

Renal T-cell infiltration is a key component of salt-sensitive hypertension in Dahl salt-sensitive (SS) rats. Here, we use an electronic servo-control technique to determine the contribution of renal perfusion pressure to T-cell infiltration in the SS rat kidney. An aortic balloon occluder placed around the aorta between the renal arteries was used to maintain perfusion pressure to the left kidney at control levels, ≈128 mm Hg, during 7 days of salt-induced hypertension, whereas the right kidney was exposed to increased renal perfusion pressure that averaged 157±4 mm Hg by day 7 of high-salt diet. The number of infiltrating T cells was compared between the 2 kidneys. Renal T-cell infiltration was significantly blunted in the left servo-controlled kidney compared with the right uncontrolled kidney. The number of CD3+, CD3+CD4+, and CD3+CD8+ T cells were all significantly lower in the left servo-controlled kidney. This effect was not specific to T cells because CD45R+ (B cells) and CD11b/c+ (monocytes and macrophages) cell infiltrations were all exacerbated in the hypertensive kidneys. Increased renal perfusion pressure was also associated with augmented renal injury, with increased protein casts and glomerular damage in the hypertensive kidney. Levels of norepinephrine were comparable between the 2 kidneys, suggestive of equivalent sympathetic innervation. Renal infiltration of T cells was not reversed by the return of renal perfusion pressure to control levels after 7 days of salt-sensitive hypertension. We conclude that increased pressure contributes to the initiation of renal T-cell infiltration during the progression of salt-sensitive hypertension in SS rats.

Transcriptome-wide co-expression analysis identifies LRRC2 as a novel mediator of mitochondrial and cardiac function.

Mitochondrial dysfunction contributes to myriad monogenic and complex pathologies. To understand the underlying mechanisms, it is essential to define the full complement of proteins that modulate mitochondrial function. To identify such proteins, we performed a meta-analysis of publicly available gene expression data. Gene co-expression analysis of a large and heterogeneous compendium of microarray data nominated a sub-population of transcripts that whilst highly correlated with known mitochondrial protein-encoding transcripts (MPETs), are not themselves recognized as generating proteins either localized to the mitochondrion or pertinent to functions therein. To focus the analysis on a medically-important condition with a strong yet incompletely understood mitochondrial component, candidates were cross-referenced with an MPET-enriched module independently generated via genome-wide co-expression network analysis of a human heart failure gene expression dataset. The strongest uncharacterized candidate in the analysis was Leucine Rich Repeat Containing 2 (LRRC2). LRRC2 was found to be localized to the mitochondria in human cells and transcriptionally-regulated by the mitochondrial master regulator Pgc-1α. We report that Lrrc2 transcript abundance correlates with that of ß-MHC, a canonical marker of cardiac hypertrophy in humans and experimentally demonstrated an elevation in Lrrc2 transcript in in vitro and in vivo rodent models of cardiac hypertrophy as well as in patients with dilated cardiomyopathy. RNAi-mediated Lrrc2 knockdown in a rat-derived cardiomyocyte cell line resulted in enhanced expression of canonical hypertrophic biomarkers as well as increased mitochondrial mass in the context of increased Pgc-1α expression. In conclusion, our meta-analysis represents a simple yet powerful springboard for the nomination of putative mitochondrially-pertinent proteins relevant to cardiac function and enabled the identification of LRRC2 as a novel mitochondrially-relevant protein and regulator of the hypertrophic response.

Breaking the Cycle: Estrous Variation Does Not Require Increased Sample Size in the Study of Female Rats.

Despite the striking differences between male and female physiology, female physiology is understudied. In response, the National Institutes of Health is promulgating new policies to increase the use of female organisms in preclinical research. Females are commonly believed to have greater variability than males because of the estrous cycle, but recent studies call this belief into question. Effects of estrous cycling on mean arterial pressure were assessed in female Dahl S rats using telemetry and vaginal cytometry and found that estrous cycling did not affect mean arterial pressure magnitude or variance. Data from the PhysGen arm of the Program for Genomic Applications was used to compare male and female variance and coefficient of variation in 142 heart, lung, vascular, kidney, and blood phenotypes, each measured in hundreds to thousands of individual rats from over 50 inbred strains. Seventy-four of 142 phenotypes from this data set demonstrated a sex difference in variance; however, 59% of these phenotypes exhibited greater variance in male rats rather than female. Remarkably, a retrospective power analysis demonstrated that only 16 of 74 differentially variable phenotypes would be detected when using an experimental cohort large enough to detect a difference in magnitude. No overall difference in coefficient of variation between male and female rats was detected when analyzing these 142 phenotypes. We conclude that variability of 142 traits in male and female rats is similar, suggesting that differential treatment of males and females for the purposes of experimental design is unnecessary until proven otherwise, rather than the other way around.

Automated quantification reveals hyperglycemia inhibits endothelial angiogenic function.

OBJECTIVE: Diabetes Mellitus (DM) has reached epidemic levels globally. A contributing factor to the development of DM is high blood glucose (hyperglycemia). One complication associated with DM is a decreased angiogenesis. The Matrigel tube formation assay (TFA) is the most widely utilized in vitro assay designed to assess angiogenic factors and conditions. In spite of the widespread use of Matrigel TFAs, quantification is labor-intensive and subjective, often limiting experiential design and interpretation of results. This study describes the development and validation of an open source software tool for high throughput, morphometric analysis of TFA images and the validation of an in vitro hyperglycemic model of DM. APPROACH AND RESULTS: Endothelial cells mimic angiogenesis when placed onto a Matrigel coated surface by forming tube-like structures. The goal of this study was to develop an open-source software algorithm requiring minimal user input (Pipeline v1.3) to automatically quantify tubular metrics from TFA images. Using Pipeline, the ability of endothelial cells to form tubes was assessed after culture in normal or high glucose for 1 or 2 weeks. A significant decrease in the total tube length and number of branch points was found when comparing groups treated with high glucose for 2 weeks versus normal glucose or 1 week of high glucose. CONCLUSIONS: Using Pipeline, it was determined that hyperglycemia inhibits formation of endothelial tubes in vitro. Analysis using Pipeline was more accurate and significantly faster than manual analysis. The Pipeline algorithm was shown to have additional applications, such as detection of retinal vasculature.

Changes in glutamate receptor subunits within the medulla in goats after section of the carotid sinus nerves.

The mechanisms which contribute to the time-dependent recovery of resting ventilation and the ventilatory CO2 chemoreflex after carotid body denervation (CBD) are poorly understood. Herein we tested the hypothesis that there are time-dependent changes in the expression of specific AMPA, NMDA, and/or neurokinin-1 (NK1R) receptors within respiratory-related brain stem nuclei acutely or chronically after CBD in adult goats. Brain stem tissues were collected acutely (5 days) or chronically (30 days) after sham or bilateral CBD, immunostained with antibodies targeting AMPA (GluA1 or GluA2), NMDA (GluN1), or NK-1 receptors, and optical density (OD) compared. Physiological measurement confirmed categorization of each group and showed ventilatory effects consistent with bilateral CBD (Miller et al. J Appl Physiol 115: 1088-1098, 2013). Acutely after CBD, GluA1 OD was unchanged or slightly increased, but GluA2 and GluN1 OD were reduced 15-30% within the nucleus tractus solitarius (NTS) and in other medullary respiratory nuclei. Chronically after CBD, GluA1 was reduced (P < 0.05) within the caudal NTS and in other nuclei, but there was significant recovery of GluA2 and GluN1 OD. NK1 OD was not significantly different from control after CBD. We conclude that the initial decrease in GluA2 and GluN1 after CBD likely contributes to hypoventilation and the reduced CO2 chemoreflex. The partial recovery of ventilation and the CO2 chemoreflex after CBD parallel a time-dependent return of these receptors to near control levels but likely depend upon additional initiating and maintenance factors for neuroplasticity.