Toward confident prostate cancer detection using ultrasound: a multi-center study.

PURPOSE: Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue. METHODS: Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods. RESULTS: PCa detection models achieve performance scores up to 76 % average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as 2 % , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue. CONCLUSION: Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations.

PURPOSE: The standard of care for prostate cancer (PCa) diagnosis is the histopathological analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy. Models built with deep neural networks (DNNs) hold the potential for direct PCa detection from TRUS, which allows targeted biopsy and subsequently enhances outcomes. Yet, there are ongoing challenges with training robust models, stemming from issues such as noisy labels, out-of-distribution (OOD) data, and limited labeled data. METHODS: This study presents LensePro, a unified method that not only excels in label efficiency but also demonstrates robustness against label noise and OOD data. LensePro comprises two key stages: first, self-supervised learning to extract high-quality feature representations from abundant unlabeled TRUS data and, second, label noise-tolerant prototype-based learning to classify the extracted features. RESULTS: Using data from 124 patients who underwent systematic prostate biopsy, LensePro achieves an AUROC, sensitivity, and specificity of 77.9%, 85.9%, and 57.5%, respectively, for detecting PCa in ultrasound. Our model shows it is effective for detecting OOD data in test time, critical for clinical deployment. Ablation studies demonstrate that each component of our method improves PCa detection by addressing one of the three challenges, reinforcing the benefits of a unified approach. CONCLUSION: Through comprehensive experiments, LensePro demonstrates its state-of-the-art performance for TRUS-based PCa detection. Although further research is necessary to confirm its clinical applicability, LensePro marks a notable advancement in enhancing automated computer-aided systems for detecting prostate cancer in ultrasound.

Self-Supervised Learning With Limited Labeled Data for Prostate Cancer Detection in High-Frequency Ultrasound.

Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer (PCa) detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning (SL) paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of SL methods. However, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centers, we demonstrate that feature representations learned with this method can be used to classify cancer from noncancer tissue, obtaining an AUROC score of 91% on an independent test set. To the best of our knowledge, this is the first successful end-to-end self-SL (SSL) approach for PCa detection using ultrasound data. Our method outperforms baseline SL approaches, generalizes well between different data centers, and scales well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data. Our code is publicly available at https://www.github.com/MahdiGilany/SSL_micro_ultrasound.

TRUSformer: improving prostate cancer detection from micro-ultrasound using attention and self-supervision.

PURPOSE: A large body of previous machine learning methods for ultrasound-based prostate cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within a larger needle trace corresponding to a prostate tissue biopsy (called biopsy core). These ROI-scale models suffer from weak labeling as histopathology results available for biopsy cores only approximate the distribution of cancer in the ROIs. ROI-scale models do not take advantage of contextual information that are normally considered by pathologists, i.e., they do not consider information about surrounding tissue and larger-scale trends when identifying cancer. We aim to improve cancer detection by taking a multi-scale, i.e., ROI-scale and biopsy core-scale, approach. METHODS: Our multi-scale approach combines (i) an "ROI-scale" model trained using self-supervised learning to extract features from small ROIs and (ii) a "core-scale" transformer model that processes a collection of extracted features from multiple ROIs in the needle trace region to predict the tissue type of the corresponding core. Attention maps, as a by-product, allow us to localize cancer at the ROI scale. RESULTS: We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature. Our model shows consistent and substantial performance improvements compared to ROI-scale-only models. It achieves [Formula: see text] AUROC, a statistically significant improvement over ROI-scale classification. We also compare our method to large studies on prostate cancer detection, using other imaging modalities. CONCLUSIONS: Taking a multi-scale approach that leverages contextual information improves prostate cancer detection compared to ROI-scale-only models. The proposed model achieves a statistically significant improvement in performance and outperforms other large-scale studies in the literature. Our code is publicly available at www.github.com/med-i-lab/TRUSFormer .

Biparametric MR signal characteristics can predict histopathological measures of prostate cancer.

OBJECTIVES: The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer. METHODS: This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses. RESULTS: MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001). CONCLUSIONS: MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression. KEY POINTS: • Semi-supervised deep learning enables non-invasive and quantitative histopathology in the prostate from biparametric MRI. • Tissue density derived from biparametric MRI demonstrates similar characteristics to the direct estimation of tissue density from histopathology images. • The analysis of MR-driven tissue density reveals significantly different tissue compositions among different cancer grades as well as between MR-positive and MR-negative benign.

Coarse label refinement for improving prostate cancer detection in ultrasound imaging.

PURPOSE: Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise. METHODS: Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement. RESULTS: We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively. CONCLUSIONS: Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement.

Intracavernous delivery of Dickkopf3 gene or peptide rescues erectile function through enhanced cavernous angiogenesis in the diabetic mouse.

BACKGROUND: Severe peripheral angiopathy in patients with diabetes is a major contributing factor for low response rate to phosphodiesterase-5 inhibitors. OBJECTIVES: To examine whether and how Dickkopf3 (DKK3), a secreted modulator of the Wnt pathway that known to be involved in endothelial cell repair and vascular progenitor cell migration, restores erectile function in diabetic mice. METHODS: Eight-week-old C57BL/6 mice received intraperitoneal injections of streptozotocin (50 mg/kg for 5 days). Eight weeks after the diabetes was induced, the efficacy of DKK3 was determined by three independent experiments: experiment 1 (DKK3 peptide [5 µg in 20 µL PBS]); experiment 2 (DKK3 plasmid DNA with electroporation [10, 40, or 100 µg in 20 µL PBS, respectively]); and experiment 3 (DKK3 adenovirus [1 × 107 , 1 × 108 , 1 × 109 virus particles per 20 µL, respectively]). Erectile function was measured by electrical stimulation of the cavernous nerve one week (for peptide) or two weeks (for genes) after treatment. The angiogenic activity of DKK3 was determined in diabetic penis in vivo and in primary cultured mouse cavernous endothelial cells (MCECs) in vitro. RESULTS: The cavernous expression of DKK3 protein was significantly lower in the diabetic mice than in controls. DKK3 peptide or adenovirus significantly improved erectile function in diabetic mice (70% of the control values). DKK3 adenovirus profoundly restored cavernous endothelial cell and pericyte contents and increased endothelial junction proteins in diabetic mice in vivo. DKK3 peptide induced upregulation of angiogenic factors (angiopoietin-1, vascular endothelial growth factor, and basic fibroblast growth factor) and accelerated tube formation in MCECs cultivated under the high-glucose condition in vitro. CONCLUSION: DKK3 restored cavernous vascular integrity and improved erectile function in diabetic mice. Therapeutic cavernous angiogenesis by the use of DKK3 will be a promising therapeutic strategy to treat diabetic erectile dysfunction.

A Method to Isolate Pericytes From the Mouse Urinary Bladder for the Study of Diabetic Bladder Dysfunction.

PURPOSE: Pericytes surround the endothelial cells in microvessels and play a distinct role in controlling vascular permeability and maturation. The loss of pericyte function is known to be associated with diabetic retinopathy and erectile dysfunction. This study aimed to establish a technique for the isolation of pericytes from the mouse urinary bladder and an in vitro model that mimics in vivo diabetic bladder dysfunction. METHODS: To avoid contamination with epithelial cells, the urothelial layer was meticulously removed from the underlying submucosa and detrusor muscle layer. The tissues were cut into multiple pieces, and the fragmented tissues were settled by gravity into collagen I-coated culture plates. The cells were cultured under normal-glucose (5 mmol/L) or high-glucose (30 mmol/L) conditions, and tube formation, cell proliferation, and TUNEL assays were performed. We also performed hydroethidine staining to measure superoxide anion production. RESULTS: We successfully isolated high-purity pericytes from the mouse urinary bladder. The cells were positively stained for platelet-derived growth factor receptor-ß and NG2 and negatively stained for smooth muscle cell markers (desmin and myosin) and an endothelial cell marker (CD31). The number of tubes formed and the number of proliferating cells were significantly lower when the pericytes were exposed to high-glucose conditions compared with normal-glucose conditions. In addition, there were significant increases in superoxide anion production and the number of apoptotic cells when the pericytes were cultured under high-glucose conditions. CONCLUSION: To the best of our knowledge, this is the first study to isolate and culture pericytes from the mouse urinary bladder. Our model would be a useful tool for screening the efficacy of therapeutic candidates targeting pericyte function in diabetic bladder dysfunction and exploring the functional role of specific targets at the cellular level.

Vactosertib, a Novel, Orally Bioavailable Activin Receptor-Like Kinase 5 Inhibitor, Promotes Regression of Fibrotic Plaques in a Rat Model of Peyronie's Disease.

PURPOSE: To examine the therapeutic effect of Vactosertib, a small molecule inhibitor of transforming growth factor-ß (TGF-ß) type I receptor (activin receptor-like kinase-5, ALK5), in an experimental model of Peyronie's disease (PD) and determining anti-fibrotic mechanisms of Vactosertib in primary fibroblasts derived from human PD plaques. MATERIALS AND METHODS: Male rats were randomly divided into three groups (n=6 per group); control rats without treatment; PD rats receiving vehicle; and PD rats receiving Vactosertib (10 mg/kg). PD-like plaques were induced by administering 100 µL of each of human fibrin and thrombin solutions into the tunica albuginea on days 0 and 5. Vactosertib was given orally five times a week for 2 weeks. On day 30, we performed electrical stimulation of the cavernous nerve to measure erectile function, and the penis was obtained for histological examination. Fibroblasts isolated from human PD plaques were used to determine the anti-fibrotic effects of Vactosertib in vitro. RESULTS: Vactosertib induced significant regression of fibrotic plaques in PD rats in vivo through reduced infiltration of inflammatory cells and reduced expression of phospho-Smad2, which recovered erectile function. Vactosertib also abrogated TGF-ß1-induced enhancement of extracellular matrix protein production and hydroxyproline content in PD fibroblasts in vitro by hindering the TGF-ß1-induced Smad2/3 phosphorylation and nuclear translocation, and fibroblast-to-myofibroblast transdifferentiation. CONCLUSIONS: In view of the critical role of TGF-ß and the Smad pathway in the pathogenesis of PD, inhibition of this pathway with an ALK5 inhibitor may represent a novel, targeted therapy for PD.

A Simple and Nonenzymatic Method to Isolate Human Corpus Cavernosum Endothelial Cells and Pericytes for the Study of Erectile Dysfunction.

PURPOSE: To establish a simple and nonenzymatic technique to isolate endothelial cells (ECs) and pericytes from human corpus cavernosum tissue and to evaluate the angiogenic ability of the human cavernous EC or pericytes for the study of high glucose-induced angiopathy. MATERIALS AND METHODS: For primary human cavernous EC culture, cavernous tissues were implanted into Matrigel in dishes. For primary human cavernous pericyte culture, cavernous tissues were settled by gravity into dishes. We performed immunocytochemistry and Western blot to determine phenotype and morphologic changes from passage 1 to 5. The primary cultured cells were exposed to a normal-glucose (5 mmol/L) or a high-glucose (30 mmol/L) condition, and then tube formation assay was done. RESULTS: We successfully isolated high-purity EC and pericytes from human corpus cavernosum tissue. Primary cultured EC showed highly positive staining for von Willebrand factor, and pericyte revealed positive staining for NG2 and platelet-derived growth factor receptor-ß. Primary cultured EC and pericytes maintained their cellular characteristics up to passage 2 or 3. However, we observed significant changes in their typical phenotype from the passage 4 and morphological characteristics from the passage 3. Human cavernous EC or pericytes formed well-organized capillary-like structures in normal-glucose condition, whereas severely impaired tube formation was detected in high-glucose condition. CONCLUSIONS: This study provides a simple and nonenzymatic method for primary culture of human cavernous EC and pericytes. Our study will aid us to understand the pathophysiology of diabetic erectile dysfunction, and also be a valuable tool for determining the efficacy of candidate therapeutic targets.

Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke.

PURPOSE: Acute ischemic stroke is one of the primary causes of death worldwide. Recent studies have shown that the assessment of collateral status could aid in improving the treatment for patients with acute ischemic stroke. We present a 3D deep regression neural network to automatically generate the collateral images from dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP) in acute ischemic stroke. METHODS: This retrospective study includes 144 subjects with acute ischemic stroke (stroke cases) and 201 subjects without acute ischemic stroke (controls). DSC-MRP images of these subjects were manually inspected for collateral assessment in arterial, capillary, early and late venous, and delay phases. The proposed network was trained on 205 subjects, and the optimal model was chosen using the validation set of 64 subjects. The predictive power of the network was assessed on the test set of 76 subjects using the squared correlation coefficient (R-squared), mean absolute error (MAE), Tanimoto measure (TM), and structural similarity index (SSIM). RESULTS: The proposed network was able to predict the five phase maps with high accuracy. On average, 0.897 R-squared, 0.581 × 10-1 MAE, 0.946 TM, and 0.846 SSIM were achieved for the five phase maps. No statistically significant difference was, in general, found between controls and stroke cases. The performance of the proposed network was lower in the arterial and venous phases than the other three phases. CONCLUSION: The results suggested that the proposed network performs equally well for both control and acute ischemic stroke groups. The proposed network could help automate the assessment of collateral status in an efficient and effective manner and improve the quality and yield of diagnosis of acute ischemic stroke. The follow-up study will entail the clinical evaluation of the collateral images that are generated by the proposed network.

BACH: Grand challenge on breast cancer histology images.

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Methods for Segmentation and Classification of Digital Microscopy Tissue Images.

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.

PURPOSE: We propose an approach of 3D convolutional neural network to segment the prostate in MR images. METHODS: A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder-decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches. RESULTS: Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts. CONCLUSIONS: The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.

Silencing Histone Deacetylase 7 Alleviates Transforming Growth Factor-ß1-Induced Profibrotic Responses in Fibroblasts Derived from Peyronie's Plaque.

PURPOSE: Epigenetic modifications, such as histone acetylation/deacetylation and DNA methylation, play a crucial role in the pathogenesis of inflammatory disorders and fibrotic diseases. The aim of this study was to study the differential gene expression of histone deacetylases (HDACs) in fibroblasts isolated from plaque tissue of Peyronie's disease (PD) or normal tunica albuginea (TA) and to examine the anti-fibrotic effect of small interfering RNA (siRNA)-mediated silencing of HDAC7 in fibroblasts derived from human PD plaque. MATERIALS AND METHODS: For differential gene expression study, we performed reverse-transcriptase polymerase chain reaction for HDAC isoforms (1-11) in fibroblasts isolated from PD plaque or normal TA. Fibroblasts isolated from PD plaque were pretreated with HDAC7 siRNA (100 pmol) and then stimulated with transforming growth factor-ß1 (TGF-ß1, 10 ng/mL). Protein was extracted from treated fibroblasts for Western blotting. We also performed immunocytochemistry to detect the expression of extracellular matrix proteins and to examine the effect of HDAC2 siRNA on the TGF-ß1-induced nuclear translocation of Smad2/3 and myofibroblastic differentiation. RESULTS: The mRNA expression of HDAC2, 3, 4, 5, 7, 8, 10, and 11 was higher in fibroblasts isolated from PD plaque than in fibroblasts isolated from normal TA tissue. Knockdown of HDAC7 in PD fibroblasts inhibited TGF-ß1-induced nuclear shuttle of Smad2 and Smad3, transdifferentiation of fibroblasts into myofibroblasts, and abrogated TGF-ß1-induced production of extracellular matrix protein. CONCLUSIONS: These findings suggest that specific inhibition of HDAC7 with RNA interference may represent a promising epigenetic therapy for PD.

Pericyte-Derived Dickkopf2 Regenerates Damaged Penile Neurovasculature Through an Angiopoietin-1-Tie2 Pathway.

Penile erection requires well-coordinated interactions between vascular and nervous systems. Penile neurovascular dysfunction is a major cause of erectile dysfunction (ED) in patients with diabetes, which causes poor response to oral phosphodiesterase-5 inhibitors. Dickkopf2 (DKK2), a Wnt antagonist, is known to promote angiogenesis. Here, using DKK2-Tg mice or DKK2 protein administration, we demonstrate that the overexpression of DKK2 in diabetic mice enhances penile angiogenesis and neural regeneration and restores erectile function. Transcriptome analysis revealed that angiopoietin-1 and angiopoietin-2 are target genes for DKK2. Using an endothelial cell-pericyte coculture system and ex vivo neurite sprouting assay, we found that DKK2-mediated juxtacrine signaling in pericyte-endothelial cell interactions promotes angiogenesis and neural regeneration through an angiopoietin-1-Tie2 pathway, rescuing erectile function in diabetic mice. The dual angiogenic and neurotrophic effects of DKK2, especially as a therapeutic protein, will open new avenues to treating diabetic ED.

Dickkopf2 rescues erectile function by enhancing penile neurovascular regeneration in a mouse model of cavernous nerve injury.

Penile erection is a neurovascular event and neurologic or vascular disturbances are major causes of erectile dysfunction (ED). Radical prostatectomy for prostate cancer not only induces cavernous nerve injury (CNI) but also results in cavernous angiopathy, which is responsible for poor responsiveness to oral phosphodiesterase-5 inhibitors. Dickkopf2 (DKK2) is known as a Wnt signaling antagonist and is reported to promote mature and stable blood vessel formation. Here, we demonstrated in CNI mice that overexpression of DKK2 by administering DKK2 protein or by using DKK2-Tg mice successfully restored erectile function: this recovery was accompanied by enhanced neural regeneration through the secretion of neurotrophic factors, and restoration of cavernous endothelial cell and pericyte content. DKK2 protein also promoted neurite outgrowth in an ex vivo major pelvic ganglion culture experiment and enhanced tube formation in primary cultured mouse cavernous endothelial cells and pericytes co-culture system in vitro. In light of critical role of neuropathy and angiopathy in the pathogenesis of radical prostatectomy-induced ED, reprogramming of damaged erectile tissue toward neurovascular repair by use of a DKK2 therapeutic protein may represent viable treatment option for this condition.

Effectiveness of Intracavernous Delivery of Recombinant Human Hepatocyte Growth Factor on Erectile Function in the Streptozotocin-Induced Diabetic Mouse.

INTRODUCTION: Diabetic erectile dysfunction is a disease mostly of vascular origin and men with diabetic erectile dysfunction respond poorly to oral phosphodiesterase-5 inhibitors. Hepatocyte growth factor (HGF) is a pleiotropic factor that plays an essential role in the regulation of cell proliferation, survival, and angiogenesis. AIM: To determine the effectiveness of recombinant human (rh)-HGF in restoring erectile function in diabetic mice. METHODS: Four groups of mice were used: control non-diabetic mice and streptozotocin-induced diabetic mice receiving two successive intracavernous injections of phosphate buffered saline (days -3 and 0), a single intracavernous injection of rh-HGF (day 0), or two successive intracavernous injections of rh-HGF (days -3 and 0). We also examined the effect of rh-HGF in primary cultured mouse cavernous endothelial cells and in major pelvic ganglion culture in vitro, which was incubated under a normal-glucose (5 mmol/L) or a high-glucose (30 mmol/L) condition. MAIN OUTCOME MEASURES: Two weeks after treatment, we measured erectile function by electrical stimulation of the cavernous nerve and the penis was harvested for histologic studies. RESULTS: Repeated intracavernous injections of rh-HGF protein induced significant restoration of erectile function in diabetic mice (89-100% of control values), whereas a single intracavernous injection of rh-HGF protein elicited modest improvement. Rh-HGF significantly induced phosphorylation of its receptor c-Met, increased the content of endothelial cells and smooth muscle cells, and decreased the generation of reactive oxygen species (superoxide anion and peroxynitrite) and extravasation of oxidized low-density lipoprotein in diabetic mice. Under the high-glucose condition, rh-HGF protein also promoted tube formation in mouse cavernous endothelial cells and enhanced neurite sprouting in major pelvic ganglion culture in vitro. CONCLUSION: The dual angiogenic and neurotrophic effects of HGF could open a new avenue through which diabetic erectile dysfunction can be treated.