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. We prepared 4-m 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 m 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. 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. 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.

Renal T-cell infiltration is a key component of salt-sensitive hypertension in Dahl salt-sensitive rats. Here we use an electronic servo-control technique to determine the contribution of renal perfusion pressure to T-cell infiltration in the Dahl salt-sensitive 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, approximately 128 mmHg, during 7-days of salt-induced hypertension while the right kidney was exposed to increased renal perfusion pressure which averaged 157 ± 4 mmHg by high salt day-7. The number of infiltrating T-cells was compared between the two kidneys. Renal T-cell infiltration was significantly blunted in the left servo-controlled kidney compared to 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 since 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 two 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 Dahl salt-sensitive rats.

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.

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. © 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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