Prostate Region Research Articles

Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review

World-wide incidence rate of prostate cancer has progressively increased with time especially with the increased proportion of elderly population. Early detection of prostate cancer when it is confined to the prostate gland has the best chance of successful treatment and increase in surviving rate. Prostate cancer occurrence rate varies over the three prostate regions, peripheral zone (PZ), transitional zone (TZ), and central zone (CZ) and this characteristic is one of the important considerations is development of segmentation algorithm. In fact, the occurrence rate of cancer PZ, TZ and CZ regions is respectively. at 70-80%, 10-20%, 5% or less. In general application of medical imaging, segmentation tasks can be time consuming for the expert to delineate the region of interest, especially when involving large numbers of images. In addition, the manual segmentation is subjective depending on the expert's experience. Hence, the need to develop automatic segmentation algorithms has rapidly increased along with the increased need of diagnostic tools for assisting medical practitioners, especially in the absence of radiologists. The prostate gland segmentation is challenging due to its shape variability in each zone from patient to patient and different tumor levels in each zone. This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. The review coverage includes the initial screening and imaging techniques, image pre-processing, segmentation techniques based on machine learning and deep learning techniques. Particular attention is given to different loss functions used for training segmentation based on deep learning techniques. Besides, a summary of publicly available prostate MRI image datasets is also provided. Finally, the future challenges and limitations of current deep learning-based approaches and suggestions of potential future research are also discussed.

A prostate MRI segmentation method based on prior-shape-information combined with gradient and regional information

With the increasing and younger incidence of prostate cancer, the extraction of prostate contour from magnetic resonance imaging (MRI) plays an increasingly important role in clinical diagnosis and related medical research. In order to accurately segment the prostate region in MR images, we propose a level set segmentation model, which integrates the prior shape information of the prostate and the gradient information into the region-scalable fitting (RSF) model proposed by Li et al. to achieve high-precision segmentation of the prostate. In our model, we use the Gaussian probability model to establish the statistical learning of the prior shape, expressed as the external energy term of the evolution curve, and use the local grey information and the improved gradient information as the internal energy term to construct the final level set segmentation model. This method uses the information of the image itself to modify the evolution curve under the constraints of the prior shape, which can better deal with the small difference between the background and the target area in the medical image, and improve the accuracy and robustness of the segmentation. The final experimental results and analysis illustrate the feasibility of the method used in prostate MRI segmentation.

Effects of resveratrol against scattered radiation-induced testicular damage in rats

Abstract Objectives To investigate the possible protective effects of resveratrol against oxidative testicular damage due to scattered radiation during pelvic ionizing radiation exposure in rats. Methods Rats were divided into 5 groups; control, radiation, and radiation + resveratrol therapy in early and late periods. Under anesthesia, 20 Gy ionizing radiation was applied to prostatic region. Resveratrol was administered (10 mg/kg/day) orally before ionizing radiation exposure. Animals were decapitated at the end of 1st and 10th weeks. Biochemical markers of oxidative stress; caspase-3 and sirtuin-1 protein expressions; testosterone levels were evaluated, histological examinations were performed. Results Significant increases in malondialdehyde, 8-hydroxy-deoxyguanosine levels, myeloperoxidase, and caspase-3 activities were observed after ionizing radiation exposure, also superoxide dismutase and glutathione activities were significantly decreased. Radiotherapy increased caspase-3 and decreased sirtuin-1 protein expressions. Resveratrol treatment significantly reversed these parameters and also reversed the decrease in testosterone levels back to control levels in late period. Conclusion Resveratrol showed antioxidant and sirtuin-activating properties against oxidative damage caused by scattered radiation to testis and provided hormonal protection. These results suggest that resveratrol may be an alternative protective agent on testicular tissues against the effects of scattered pelvic radiation.

Convergent network effects along the axis of gene expression during prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high-throughput genomic measurements. It remains unclear how complex genome alterations affect molecular networks through changing protein levels and consequently biochemical states of tumor tissues.ResultsHere, we investigate the propagation of genomic effects along the axis of gene expression during prostate cancer progression. We quantify genomic, transcriptomic, and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis reveals the convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devise a network-based approach that integrates perturbations across different molecular layers, which identifies a sub-network consisting of nine genes whose joint activity positively correlates with increasingly aggressive tumor phenotypes and is predictive of recurrence-free survival. Further, our data reveal a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers.ConclusionsThis study uncovers molecular networks with considerable convergent alterations across tumor sites and patients. It also exposes a diversity of network effects: we could not identify a single sub-network that is perturbed in all high-grade tumor regions.

Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks.

Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and dose monitoring in prostate radiotherapy. We proposed a novel registration framework which considers biomechanical constraint when deforming the MR to CBCT. The registration framework consists of two segmentation convolutional neural networks (CNN) for MR and CBCT prostate segmentation, and a three-dimensional (3D) point cloud (PC) matching network. Image intensity-based rigid registration was first performed to initialize the alignment between MR and CBCT prostate. The aligned prostates were then meshed into tetrahedron elements to generate volumetric PC representation of the prostate shapes. The 3D PC matching network was developed to predict a PC motion vector field which can deform the MRI prostate PC to match the CBCT prostate PC. To regularize the network's motion prediction with biomechanical constraints, finite element (FE) modeling-generated motion fields were used to train the network. MRI and CBCT images of 50 patients with intraprostatic fiducial markers were used in this study. Registration results were evaluated using three metrics including dice similarity coefficient (DSC), mean surface distance (MSD), and target registration error (TRE). In addition to spatial registration accuracy, Jacobian determinant and strain tensors were calculated to assess the physical fidelity of the deformation field. The mean and standard deviation of our method were 0.93±0.01, 1.66±0.10mm, and 2.68±1.91mm for DSC, MSD, and TRE, respectively. The mean TRE of the proposed method was reduced by 29.1%, 14.3%, and 11.6% as compared to image intensity-based rigid registration, coherent point drifting (CPD) nonrigid surface registration, and modality-independent neighborhood descriptor (MIND) registration, respectively. We developed a new framework to accurately register the prostate on MRI to CBCT images for external beam radiotherapy. The proposed method could be used to aid DIL delineation on CBCT, treatment planning, dose escalation to DIL, and dose monitoring.

A Systematic Review of the Zinc Content of the Normal Human Prostate Gland.

The prostate gland is subject to various disorders. The etiology and pathogenesis of these diseases are not well understood. Moreover, despite technological advancements, the differential diagnosis of prostate disorders has become progressively more complex and controversial. It was suggested that the Zn level in prostatic tissue plays an important role in prostatic carcinogenesis and its measurement may be useful as a cancer biomarker. These suggestions promoted more detailed studies of the Zn content in the prostatic tissue of healthy subjects. The present study evaluated by systematic analysis presents the published data for Zn content analyzed in prostatic tissue of "normal" glands. This evaluation reviewed 1885 studies, all of which were published in the years from 1921 to 2020 and were located by searching the databases PubMed, MEDLINE, Scopus, Web of Science, Elsevier-Embase, and Cochrane Library. In addition, the personal archive of the author collected from 1966 to 2020 was also used. The articles were analyzed and "median of means" and "range of means" were used to examine heterogeneity of the measured Zn content in prostates of apparently healthy men. The objective analysis was performed on data from the 105 studies, which included 3735 subjects. It was found that the range of means of prostatic Zn content reported in the literature for "normal" gland varies widely from 17 to 547mg/kg with median of means 109mg/kg on a wet mass basis. The Zn content depends on many factors such as analytical method, age, level of androgens, dietary Zn intake, and the prostatic region and fraction of prostate tissue being studied. Finally, because of small sample size and high data heterogeneity, we recommend other primary studies be performed.

Magnetic resonance imaging and transrectal ultrasound prostate image segmentation based on improved level set for robotic prostate biopsy navigation.

Transrectal ultrasound (TRUS) guided prostate biopsy is a typical early prostate examination. However, the ultrasound imaging suffers from blurred contour, intensity inhomogeneity and small surrounding soft tissue differentiation. To take advantage of clear magnetic resonance imaging (MRI) into robotic prostate biopsy navigation, the prostate regions in the MRI and TRUS images need to be segmented separately. This paper proposes an improved level set segmentation model based on prior shape, which aims to provide a better solution to the prostate segmentation problems in TRUS and MRI. In our segmentation model, the Gaussian probability model is used to establish the statistical learning of the prior shape, and the cosine function is used to represent the energy term fitting of the traditional prior shape and the local intensity information. The experiment results show that the model can adapt to different forms of prostate in MRI and TRUS more accurately, and the prostate biopsy accuracy in our biopsy system can reach 2.24 ± 1.44 mm. This segmentation model has high accuracy, meets the clinical needs in robotic prostate biopsy navigation.

Image quality evaluation of intra-irradiation cone-beam computed tomography acquired during one- and two-arc prostate volumetric-modulated arc therapy delivery: A phantom study.

PurposeTo evaluate (a) the effects of megavoltage (MV)‐scatter on concurrent kilovoltage (kV) projections (P MVkV) acquired during rotational delivery, and (b) the image quality of intra‐irradiation cone‐beam computed tomography (ii‐CBCT) images acquired during prostate volumetric‐modulated arc therapy (VMAT) delivery.MethodsExperiment (1): P MVkVs were acquired with various MV beam parameters using a cylindrical phantom: field size (FS), MV energy (6 or 15 MV), dose rate (DR), and gantry speed. The average pixel values were calculated in a region on each P MVkV which were extracted at eight equally spaced gantry angles. Experiment (2): 11 one‐arc and seven two‐arc 15 MV prostate VMAT plans were used along with a pelvis phantom. One plan was selected from each of arc plans and its MV energy was changed to 6 MV. After P MVkVs were acquired, projections consisting of MV‐scatter only (P MVS) were acquired with closing kV blades and subtracted from P MVkV (P MVScorr). Projections by kV beams only were acquired (P kV). The corresponding CBCT images were reconstructed (CBCTMVkV, CBCTMVScorr, and CBCTkV). The root‐mean‐square errors (RMSEs) were calculated in prostate region and 3D gamma analysis was conducted, in which the CBCT‐number was used instead of doses between ii‐CBCT images and CBCTkV (30 HU/1 mm).ResultsExperiment (1): The MV‐scatters were dependent on the FSs, MV energies, and DRs. Experiment (2): The median RMSEs for CBCTMVScorr were decreased by 107.5 HU (1‐arc) and 42.9 HU (2‐arc) compared to those for CBCTMVkV. The median GPRs for CBCTMVScorr were 94.7% (1‐arc) and 93.4% (2‐arc), while those for CBCTMVkV were 61.1% and 79.9%, respectively. GPRs for 6 MV plans were smaller than those for 15 MV plans.ConclusionsThe number of MV‐scatters increased with larger FSs and DRs, and smaller MV energy. The MV‐scatters were corrected on the CBCTMVScorr regardless of the number of arcs.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. Retrospective. Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. 3T multishell diffusion-weighted sequence. Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps. Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models. The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order. The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Numerical study of blood perfusion and nanoparticle transport in prostate and muscle tumours during intravenous magnetic hyperthermia

In the present work, a two-phase mathematical model of intravenous magnetic (Fe3O4,Fe2O3 or FePt) nanoparticle hyperthermia is considered for the treatment of muscle and prostate tumours. The blood-nanoparticle suspension is described as a non-Newtonian (Quemada) nanofluid, and the effects of thermophoresis and Brownian motion are considered. The blood vessel region is surrounded by a cancerous (muscle or prostate) tumour region, a non-cancerous (muscle or prostate) region and a fat layer. Heat is generated within these tissue regions using an external alternating magnetic field. A numerical solution of the problem is obtained using the mixed finite element method with P2-P1 Taylor-Hood elements. Using this numerical solution, the influence of tumour size, blood vessel radius and haematocrit on blood flow, convective heat transfer, nanoparticle mass transport and blood pressure is examined.

Molecular Mechanism and Role of Translational Values of Heat Shock Protein (HSP27) in Various Disease

HSP27, also known as HSPB1, was first discovered with a molecular weight 27kDa belonging to the four member gene family. Elevated levels of HSP27 are seen when different unfavorable conditions prevail such as increase in temperature and oxidative stress or exposure to heavy metals or organic solvents. They possess ATP-independent chaperone like activity which helps in maintaining protein homeostasis. It can also form large oligomers (300-600 kDa) containing different numbers of subunits. It is composed of total 205 amino acids. HSP27 undergoes post-translational modifications i.e. phosphorylation thereby converting large oligomers into dimers. It can act as an anti-apoptotic and antioxidant molecule during oxidative stress. The elevated form of HSP27 is also seen in some cancer belongs to breast, ovary, prostate, brain, colorectal, hepatocellular carcinoma, lung, liver, and cervical regions. Keeping in view of molecular roles of HSP27 signaling in various pathways, we have proposed their translational values in different diseases. In addition, we have also reported the existing scientific data on the HSP27 as the potential cancer biomarker and their therapeutic targets for improved prognosis and treatment in different diseases.

Biomechanically constrained non-rigid MR-TRUS prostate registration using deep learning based 3D point cloud matching.

A non-rigid MR-TRUS image registration framework is proposed for prostate interventions. The registration framework consists of a convolutional neural networks (CNN) for MR prostate segmentation, a CNN for TRUS prostate segmentation and a point-cloud based network for rapid 3D point cloud matching. Volumetric prostate point clouds were generated from the segmented prostate masks using tetrahedron meshing. The point cloud matching network was trained using deformation field that was generated by finite element analysis. Therefore, the network implicitly models the underlying biomechanical constraint when performing point cloud matching. A total of 50 patients' datasets were used for the network training and testing. Alignment of prostate shapes after registration was evaluated using three metrics including Dice similarity coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD). Internal point-to-point registration accuracy was assessed using target registration error (TRE). Jacobian determinant and strain tensors of the predicted deformation field were calculated to analyze the physical fidelity of the deformation field. On average, the mean and standard deviation were 0.94±0.02, 0.90±0.23 mm, 2.96±1.00 mm and 1.57±0.77 mm for DSC, MSD, HD and TRE, respectively. Robustness of our method to point cloud noise was evaluated by adding different levels of noise to the query point clouds. Our results demonstrated that the proposed method could rapidly perform MR-TRUS image registration with good registration accuracy and robustness.

Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such as that of the prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. To address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools—DenseNet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-weighted images in a four-fold cross-validation fashion. The networks were trained and tested on weakly and accurately annotated masks separately to test the hypothesis that the network can learn even when the labels are not accurate. The network successfully detects the prostate region and segments the gland and its zones. Compared with U-net, the second version of our algorithm, Dense-2 U-net, achieved an average Dice score for the whole prostate of 92.1± 0.8% vs. 90.7 ± 2%, for the central zone of 89.5 pm 2% vs. 89.1 pm 2.2 %, and for the peripheral zone of 78.1± 2.5% vs. 75 pm 3%. Our initial results show Dense-2 U-net to be more accurate than state-of-the-art U-net for automatic segmentation of the prostate and prostate zones.

Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer

The computer-aided diagnosis of prostate ultrasound images can aid in the detection and treatment of prostate cancer. However, the ultrasound images of the prostate sometimes come with serious speckle noise, low signal-to-noise ratio, and poor detection accuracy. To overcome this shortcoming, we proposed a deep learning model that integrates S-Mask R-CNN and Inception-v3 in the ultrasound image-aided diagnosis of prostate cancer in this paper. The improved S-Mask R-CNN was used to realize the accurate segmentation of prostate ultrasound images and generate candidate regions. The region of interest align algorithm was used to realize the pixel-level feature point positioning. The corresponding binary mask of prostate images was generated by the convolution network to segment the prostate region and the background. Then, the background information was shielded, and a data set of segmented ultrasound images of the prostate was constructed for the Inception-v3 network for lesion detection. A new network model was added to replace the original classification module, which is composed of forward and back propagation. Forward propagation mainly transfers the characteristics extracted from the convolution layer pooling layer below the pool_3 layer through the transfer learning strategy to the input layer and then calculates the loss value between the classified and label values to identify the ultrasound lesion of the prostate. The experimental results showed that the proposed method can accurately detect the ultrasound image of the prostate and segment prostate information at the pixel-level simultaneously. The proposed method has higher accuracy than that of the doctor’s manual diagnosis and other detection methods. Our simple and effective approach will serve as a solid baseline and help ease future research in the computer-aided diagnosis of prostate ultrasound images. Furthermore, this work will promote the development of prostate cancer ultrasound diagnostic technology.

Abstract 1316: Molecular features of complete response to neoadjuvant anti-androgen therapy in high risk localized prostate cancer

Abstract Though often curable, high-risk localized prostate cancer has a significant chance of recurring either biochemically, locoregionally, or as distant metastatic disease. Efforts are ongoing to evaluate neoadjuvant strategies to reduce this risk of recurrence. Our recent clinical trial showed that intense androgen deprivation with enzalutamide, leuprolide, and abiraterone acetate with prednisone (ELAP) induced pathologic complete response or minimal residual disease (&amp;lt; 5mm) at prostatectomy in 30% of trial patients (exceptional responders). Though previous studies have identified the pathological and genomic heterogeneity of localized prostate cancer, the impact of this heterogeneity on treatment response is unknown. To address this, we performed multi-region whole-exome and whole transcriptome sequencing on tumor and normal prostate regions from 15 exceptional responders and 15 non-responders treated with intensive androgen deprivation prior to prostatectomy. Overall, we found heterogeneous somatic mutation patterns with strong evidence of intra-tumoral heterogeneity and no association between mutation frequency and response. However, when subsetting patients by treatment response, SPOP mutation and SPOPL copy number loss were only observed in exceptional responders, while TP53 mutations were only observed in the non-responders. We observed expression signature differences between exceptional responders and non-responders, with exceptional responders having an increase in Androgen Receptor related gene signatures. Though underpowered to identify statistically significant genomic correlates of response to neoadjuvant ADT, our findings may inform future studies to identify clinically relevant features of high-risk localized prostate cancers to stratify therapies to reduce the risk of disease recurrence. Citation Format: Alok Tewari, Alexander T. Cheung, Jake Conway, Jihye Park, Stephanie Wankowicz, Rosina T. Lis, Zhenwei Zhang, Saud AlDubayan, Rana R. McKay, Mary-Ellen Taplin, Eliezer M. Van Allen. Molecular features of complete response to neoadjuvant anti-androgen therapy in high risk localized prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1316.

Coverage of the external prostatic region by the hybrid method compared with the conventional method of prostate low-dose-rate brachytherapy: A randomized controlled study.

To compare the dose evaluation parameters between conventional (using loose seed alone) and hybrid (using loose seeds in combination with stranded seeds) low-dose rate brachytherapy for prostate cancer. Between 2014 and July 2016, a total of 219 patients who underwent low-dose rate brachytherapy were enrolled in a randomized controlled trial (trial number: UMIN 000012780). Patients were randomized and allocated to two groups (conventional method vs hybrid method). Post-dosimetric parameters (%D90, minimal percentage of the dose received by 90% of the prostate gland; V100, percentage of the prostate volume receiving 100% of the prescribed minimal peripheral dose; V150, percentage of the prostate volume receiving 150% of the prescribed minimal peripheral dose; %UD30, minimal percentage of the dose received by 30% of the urethra; R100, rectal volume [mL] receiving 100% of the prescribed dose) calculated at 1month after seed implantation by computed tomography scan were compared between the two groups, as well as the post-dosimetric parameters using the planning target volume of the prostate+5-mm margin. Regarding dose evaluation parameters, the prostate dose (%D90, V100, V150) and the urethral dose (%UD30) were not significantly different between the two groups, whereas V100 (+5-mm margin) and %D90 (+5-mm margin) were significantly higher in the hybrid method group compared with the conventional method group (P<0.001). The present randomized study shows that the hybrid method of low-dose rate brachytherapy can achieve a higher coverage of the periprostatic region compared with the conventional method while maintaining an acceptable level of urethral and rectal doses.

Clinical outcomes of definitive whole pelvic radiotherapy for clinical lymph node metastatic prostate cancer.

BackgroundIn this study, we aim to present the clinical outcomes of radiotherapy (RT) in clinical pelvic lymph node‐positive prostate cancer (cN1) patients. We also analyze the prognostic factors with focus on RT dose escalation to metastatic lymph nodes (LN).MethodsWe retrospectively analyzed the data from cN1 patients who were treated with definitive RT and androgen deprivation therapy (ADT) between June 2004 and February 2016. All patients received localized irradiation to the prostate region and whole pelvis irradiation. Some patients received intensity‐modulated radiation therapy with RT dose escalation to metastatic LN. Univariate analyses using log‐rank test were performed to find prognostic factors between patient subgroups.ResultsFifty‐one consecutive patients were identified. The median follow‐up period for all patients was 88 (range 20‐157) months. Primary Gleason pattern and LN RT dose were statistically significant prognostic factors for relapse‐free survival (RFS) and distant metastasis‐free survival (DMFS). Especially, RT dose escalation (60 Gy or more) to metastatic LN significantly improved RFS and DMFS compared with standard dose RT (4‐year RFS 90.6% vs 82.1%, 7‐year RFS 90.6% vs 58.0%, P = .015; 4‐year DMFS 90.6% vs 82.1%, 7‐year DMFS 90.6% vs 62.8%, P = .023). The following factors were all statistically significant for biochemical relapse‐free survival (BRFS): T stage, LN RT dose, local RT dose, and ADT duration period. Any significantly different toxicity was not seen for each LN or local RT dose except for the incident rate of grade 2 or more acute urinary retention, which was significantly higher in the higher LN RT dose (60 Gy or more) group by the Chi‐square test.ConclusionsRT dose escalation to metastatic LN in cN1 patients improves BRFS, RFS, and DMFS at 4 and 7 years, without increasing severe adverse events.

TO ASSESS THE DOSIMETRIC IMPACT ON PROSTATE, BLADDER AND RECTUM IN CARCINOMA PROSTATE DUE TO INTERFRACTIONALANATOMICAL VARIATION IN IMAGE GUIDED RADIATION THERAPY.

ABTRACT- Prostate cancer is second most frequent cancer in men and fifth leading cause of death worldwide. Most radiotherapy patients with prostate cancer are treated with intensity modulated radiotherapy. IMRT requires high spatial accuracy in localizing the target volume and high precision in treatment delivery, because even a small geographic miss can have a large dosimetric effect. However, the patient setup position and anatomy changes daily, particularly in the prostate region, due to rectum and bladder filling. Materials Methods: We analyzed 30 patients with prostate cancer patient treated with IMRT kV CBCT was performed for each patient twice per week during the entire course of treatment. The bladder, rectum, prostate and PTV were contoured on each CBCT scan. Results: Delivered dose of prostate D95% and prostate D100% was 99.15% and 93.66% respectively and mean value of V70 for bladder was 16% and delivered value was 16.45%. Whereas the mean value of V70 of plan for rectum was 18.12% and delivered value was 27.58%. Conclusion: Interfractional variation during prostate radiotherapy can results in substantial difference between planned and delivered doses, particularly in critical structure. IG-IMRT is useful tool in assessing dosimetric changes in critical structure due to interfractional anatomical interfractional anatomical variation in prostate radiotherapy.

An immunohistochemical prostate cell identification key indicates that aging shifts procollagen 1A1 production from myofibroblasts to fibroblasts in dogs prone to prostate-related urinary dysfunction.

The identity and spatial distribution of prostatic cell types has been determined in humans but not in dogs, even though aging- and prostate-related voiding disorders are common in both species and mechanistic factors, such as prostatic collagen accumulation, appear to be shared between species. In this publication we characterize the regional distribution of prostatic cell types in the young intact dog to enable comparisons with human and mice and we examine how the cellular source of procollagen 1A1 changes with age in intact male dogs. A multichotomous decision tree involving sequential immunohistochemical stains was validated for use in dog and used to identify specific prostatic cell types and determine their distribution in the capsule, peripheral, periurethral and urethral regions of the young intact canine prostate. Prostatic cells identified using this technique include perivascular smooth muscle cells, pericytes, endothelial cells, luminal, intermediate, and basal epithelial cells, neuroendocrine cells, myofibroblasts, fibroblasts, fibrocytes, and other hematolymphoid cells. To enhance rigor and transparency, all high resolution images (representative images shown in the figures and biological replicates) are available through the GUDMAP database at https://doi.org/10.25548/16-WMM4. The prostatic peripheral region harbors the largest proportion of epithelial cells. Aging does not change the density of hematolymphoid cells, fibroblasts, and myofibroblasts in the peripheral region or in the fibromuscular capsule, regions where we previously observed aging- and androgen-mediated increases in prostatic collagen abundance Instead, we observed aging-related changes the procollagen 1A1 positive prostatic cell identity from a myofibroblast to a fibroblast. Hematolymphoid cells and myofibroblasts are often identified as sources of collagen in tissues prone to aging-related fibrosis. We show that these are not the likely sources of pathological collagen synthesis in older intact male dogs. Instead, we identify an aging-related shift in the prostatic cell type producing procollagen 1A1 that will help direct development of cell type and prostate appropriate therapeutics for collagen accumulation.

Improving Prostate Cancer (PCa) Classification Performance by Using Three-Player Minimax Game to Reduce Data Source Heterogeneity.

PCa is a disease with a wide range of tissue patterns and this adds to its classification difficulty. Moreover, the data source heterogeneity, i.e. inconsistent data collected using different machines, under different conditions, by different operators, from patients of different ethnic groups, etc., further hinders the effectiveness of training a generalized PCa classifier. In this paper, for the first time, a Generative Adversarial Network (GAN)-based three-player minimax game framework is used to tackle data source heterogeneity and to improve PCa classification performance, where a proposed modified U-Net is used as the encoder. Our dataset consists of novel high-frequency ExactVu ultrasound (US) data collected from 693 patients at five data centers. Gleason Scores (GSs) are assigned to the 12 prostatic regions of each patient. Two classification tasks: benign vs. malignant and low- vs. high-grade, are conducted and the classification results of different prostatic regions are compared. For benign vs. malignant classification, the three-player minimax game framework achieves an Area Under the Receiver Operating Characteristic (AUC) of 93.4%, a sensitivity of 95.1% and a specificity of 87.7%, respectively, representing significant improvements of 5.0%, 3.9%, and 6.0% compared to those of using heterogeneous data, which confirms its effectiveness in terms of PCa classification.