Prostate Region Research Articles

Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.

To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach. An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model's performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fisher's exact test. Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846-0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05). The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND. • The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features. • With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region.

Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted. A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS scoreand clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA,and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ2 test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions. A total of 320 of 688 included patients were diagnosed withcsPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitionaland apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheraland transitional zones. We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parametersshowed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Effects of obesity in rats on prostate histology and expression of leptin receptor, prolactin receptor, IL-6, and NF-κB

Hypercaloric intake can lead to obesity, which is a major risk factor associated with chronic subclinical inflammation and many types of cancer. It can increase the serum levels of leptin, prolactin, nuclear factor kappa B (NF-кB) and interleukin (IL)-6, implicated in cell proliferation, differentiation and survival. To explore the effects of obesity induced by chronic hypercaloric diet in rats on the long-term expression of leptin receptor (OB-R), prolactin receptor, NF-кB, and IL-6, and the changes of histology in rat prostate. From postnatal day 21, experimental males were fed with normal chow or chow plus enriched hypercaloric liquid diet. On the postnatal day 90 (13week old), the animals were euthanized for prostate histology (hematoxylin and eosin staining) and hormone receptors analysis by Western blot. Hypercaloric diet resulted in obesity (32% higher body weight). The prostates of the obese males showed epithelium anisocytosis and compressed interstice. There was also greater volume of lipidic content, anisokaryosis, alterations of the nucleus-cytoplasm ratio, and apparent proplasia. Measures in the ventral prostate (VP) showed that alveoli area increased, but epithelium height and nucleus area were reduced. In the dorsolateral prostate, there was only reduction of nucleus area and presence of mononuclear cells in the lumen. Hypercaloric males also expressed a trend for more OB-R 130kD in the VP, but no changes were observed with regard to prolactin receptor, NF-кB and IL-6. The obesity due to chronic consumption of hypercaloric diet affects both prostatic regions, but VP is possibly more sensitive via OB-R. We suggest that longer periods of obesity are needed to alter other receptors or the molecular markers of inflammation.

Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net

Focal boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. In this paper, we develop a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network, is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated on the PET/CT images by the trained model. For evaluation, we retrospectively investigated 49 prostate cancer patients with PET/CT images acquired. The prostate and DILs of each patient were contoured by radiation oncologists and set as the ground truths and targets. We used five-fold cross-validation and a hold-out test to train and evaluate our method. The mean surface distance and DSC values were 0.666 ± 0.696 mm and 0.932 ± 0.059 for the prostate and 0.814 ± 1.002 mm and 0.801 ± 0.178 for the DILs among all 49 patients. The proposed method has shown promise for facilitating prostate and DIL delineation for DIL focal boost prostate radiation therapy.

Evaluation of PSMA-PET Biology-Guided Radiotherapy Sequential Boost to the PSMA-avid Subvolume in the Prostate Region in Low-Volume Advanced Prostate Cancer

Biology-guided radiation therapy (BgRT) is a new technology that aims to deliver radiation based on detecting positron emissions in real time. BgRT combined with prostate specific membrane antigen (PSMA) PET/CT imaging may be an attractive option for prostate cancer patients with low-volume pelvic nodes and distant metastases since all lesions may be treated during the same session. This study investigates the feasibility of BgRT sequential boost to the PSMA-avid subvolume (PAS) in the prostate region. The ratio of standard uptake value (SUV) to background SUV is quantified, with specific focus on adjacent bladder uptake.Patients enrolled in the ProPSMA prospective single-institution clinical trial underwent Ga-68-PSMA-11 PET scan at the time of prostate cancer diagnosis. Those with pelvic node or distant metastases were selected for analysis. PAS gross target volume (GTV) was delineated on the PET component of a PET/CT acquisition by using a PET edge tool in commercially available software. Three dimensional shells of thickness 5 mm, 10 mm and 20 mm were created by expanding the GTV to characterize the uptake background and to determine suitability of BgRT based on these tracking zones. Normalized SUV (nSUV) was calculated as the ratio of SUVmax in the GTV to SUVmean of the adjacent shell. Lesions with nSUV larger than 3 were defined to be suitable for BgRT. An increase in nSUV with increasing shell thickness indicates absence of uptake surrounding the GTV.From the ProPSMA patient cohort of 89 patients, 24 patients had at least one pelvic nodal or distant metastases. PAS GTV volume ranged from 1 cc to 147 cc (median = 25 cc, IQR = 12 cc - 62 cc). GTV SUVmax varied from 6.4 to 80 (median = 21.5; IQR = 14 - 33). nSUV ranged from 3.4 to 21.2 (median = 6.4; IQR = 4.9 - 8.5), from 4.1 to 20.9 (median = 8.6; IQR = 6 - 10.5), and from 2.8 to 31.4 (median = 8.2; IQR = 5.4 - 13.8) in 5 mm, 10 mm, and 20 mm shell, respectively. Moreover, 100%, 100%, and 96% of lesions had nSUV greater than 3 in a shell thickness of 5 mm, 10 mm, and 20 mm, respectively. nSUV increased from the 5 mm shell to the 10 mm shell in 75% of patients and from the 10 mm shell to the 20 mm in 46% patients, indicating the absence of bladder uptake.An nSUV greater than 3 was achieved in more than 95% of all GTVs for all shell thicknesses investigated in this study, which suggests that PSMA-guided BgRT is a feasible modality for sequential boost directed at the dPSMA-avid subvolume in the prostate region. If bladder proximity impacts geometric specificity of BgRT delivery or if greater tumor-to-background contrast is desired, additional refinements may be required. These may include modifications to the BgRT algorithm that enable masking of the bladder. Clinical trial number ACTRN12617000005358.

Towards real-time PGS range monitoring in proton therapy of prostate cancer

Proton therapy of prostate cancer (PCPT) was linked with increased levels of gastrointestinal toxicity in its early use compared to intensity-modulated radiation therapy (IMRT). The higher radiation dose to the rectum by proton beams is mainly due to anatomical variations. Here, we demonstrate an approach to monitor rectal radiation exposure in PCPT based on prompt gamma spectroscopy (PGS). Endorectal balloons (ERBs) are used to stabilize prostate movement during radiotherapy. These ERBs are usually filled with water. However, other water solutions containing elements with higher atomic numbers, such as silicon, may enable the use of PGS to monitor the radiation exposure of the rectum. Protons hitting silicon atoms emit prompt gamma rays with a specific energy of 1.78 MeV, which can be used to monitor whether the ERB is being hit. In a binary approach, we search the silicon energy peaks for every irradiated prostate region. We demonstrate this technique for both single-spot irradiation and real treatment plans. Real-time feedback based on the ERB being hit column-wise is feasible and would allow clinicians to decide whether to adapt or continue treatment. This technique may be extended to other cancer types and organs at risk, such as the oesophagus.

Biparametric Magnetic Resonance Imaging-Derived Nomogram to Detect Clinically Significant Prostate Cancer by Targeted Biopsy for Index Lesion.

Currently, it is necessary to investigate how to combine biparametric magnetic resonance imaging (bpMRI) with various clinical parameters for the detection of clinically significant prostate cancer (csPCa). To develop a multivariate prebiopsy nomogram using clinical and bpMRI parameters for estimating the probability of csPCa. Retrospective, single-center study. Two hundred and twenty-six patients who underwent targeted biopsy (TBx) for the MRI-suspected index lesion because of clinical suspicions of PCa. A 3 T MRI including turbo spin-echo T2 -weighted and diffusion-weighted single-shot echo-planar imaging sequences. Prebiopsy clinical and bpMRI parameters were patient age, biopsy history (biopsy-naïve or repeated biopsy status), prostate-specific antigen density (PSAD), Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1), and apparent diffusion coefficient ratio (ADCR). ADCR was defined as mean ADC of the index lesion divided by mean ADC of the contralateral prostatic region. A multivariate prebiopsy nomogram for csPCa (i.e. Gleason sum ≥7) was developed. Area under the curve (AUC) of each parameter and prebiopsy nomogram was assessed. Five-fold cross-validation was performed for robust estimation of performance of the prebiopsy nomogram. Logistic regression, receiver-operating curve, and 5-fold cross-validation. P-value < 0.05 was considered statistically significant. Proportion of csPCa was 31.9% (72/226). The AUCs of age, biopsy-naïve status, PSAD, PI-RADSv2.1, ADCR, and prebiopsy nomogram were 0.657 (95% confidence interval [CI], 0.580-0.733), 0.593 (95% CI, 0.525-0.660), 0.762 (95% CI, 0.697-0.826), 0.824 (95% CI, 0.770-0.878), 0.829 (95% CI, 0.769-0.888), and 0.906 (95% CI, 0.863-0.948), respectively: AUC of nomogram was significantly different than that of individual parameter. In the 5-fold cross-validation, the mean AUC of the prebiopsy nomogram for csPCa was 0.888 (95% CI, 0.786-0.983). This multivariate prebiopsy nomogram using clinical and bpMRI parameters may help estimate the probability of csPCa in patients undergoing TBx. ADCR seems to enhance the role of bpMRI in detecting csPCa. 3 TECHNICAL EFFICACY: Stage 2.

Radiotherapy planning parameters correlate with changes in the peripheral immune status of patients undergoing curative radiotherapy for localized prostate cancer

PurposeThe influence of radiotherapy on patient immune cell subsets has been established by several groups. Following a previously published analysis of immune changes during and after curative radiotherapy for prostate cancer, this analysis focused on describing correlations of changes of immune cell subsets with radiation treatment parameters.Patients and methodsFor 13 patients treated in a prospective trial with radiotherapy to the prostate region (primary analysis) and five patients treated with radiotherapy to prostate and pelvic nodal regions (exploratory analysis), already published immune monitoring data were correlated with clinical data as well as radiation planning parameters such as clinical target volume (CTV) and volumes receiving 20 Gy (V20) for newly contoured volumes of pelvic blood vessels and bone marrow.ResultsMost significant changes among immune cell subsets were observed at the end of radiotherapy. In contrast, correlations of age and CD8+ subsets (effector and memory cells) were observed early during and 3 months after radiotherapy. Ratios of T cells and T cell proliferation compared to baseline correlated with CTV. Early changes in regulatory T cells (Treg cells) and CD8+ effector T cells correlated with V20 of blood vessels and bone volumes.ConclusionsPatient age as well as radiotherapy planning parameters correlated with immune changes during radiotherapy. Larger irradiated volumes seem to correlate with early suppression of anti-cancer immunity. For immune cell analysis during normofractionated radiotherapy and correlations with treatment planning parameters, different time points should be looked at in future projects.Trial registration number:NCT01376674, 20.06.2011

Abstract 2702: Prostate stromal transcriptome as biomarker of aggressive prostate cancer

Abstract Background: Prostate cancer has a heterogeneous prognosis. Discovery of molecular biomarkers that improve identification of potentially lethal prostate cancer could enhance the ability to determine which patients would benefit most from immediate treatment. Most prognostic biomarkers have focused on those in prostate tumors. However, it is increasingly recognized that the tumor microenvironment contributes to cancer aggressiveness and progression. We therefore examined whole transcriptome expression of the prostate stroma and associations with clinically significant, high-grade prostate cancer. Methods: We performed RNA-sequencing (Illumina TruSeq RNA Exome library preparation) of macrodissected stromal samples (adjacent to and distant from tumor) from 363 radical prostatectomy specimens from Health Professionals Follow-up Study and Physicians' Health Study participants. We performed a differential expression analysis comparing the gene expression and pathways of stroma adjacent to and distant from tumor. We also performed a cluster analysis and computed a single sample gene set enrichment analysis (ssGSEA) score to assess the association of our previously reported 25 stromal signature genes (Tyekucheva et al. 2017) to determine the association with Gleason score. Results: Eighty-eight genes as well as eight Gene Ontology pathways and fourteen Canonical pathways were differentially expressed (FDR&amp;lt;0.05) between tumor adjacent to and distant from tumor. Pathways included cell-substrate adhesion, muscle development, and cardiomyopathy. 30% of men had high-grade (Gleason score 8-10) tumors. We observed higher expression of the individual signature genes in the cluster enriched for high Gleason score tumors, and a higher stromal signature ssGSEA score was associated with high Gleason score (p=0.04). Conclusions: Pathways differentially expressed between stroma adjacent to and distant from tumor suggest known differences in cell types by location or a potential field effect. We observed that the direction of the association of the stromal signature with Gleason score agreed with our previous report. We are currently analyzing the full expression dataset to identify additional stromal genes associated with Gleason score and lethal prostate cancer, and performing multi-plex immunohistochemistry to assess stromal cell-type heterogeneity in regions of tumor and normal prostate across tissue microarrays. Citation Format: Yinglu Zhou, Chaoran Ma, Giuseppe Nicoló Fanelli, Lavinia Stefanizzi, Elizaveta Gazeeva, Lorelei A. Mucci, Massimo Loda, Svitlana Tyekucheva, Kathryn L. Penney. Prostate stromal transcriptome as biomarker of aggressive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2702.

Precise Identification of Prostate Cancer from DWI Using Transfer Learning.

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was with average sensitivity and specificity of and . These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was with sensitivity and specificity of and . Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

Differential Impact of COVID-19 on Cancer Diagnostic Services Based on Body Regions: A Public Facility-Based Study in Hong Kong.

PurposeA reduction in cancer services during the coronavirus disease of 2019 pandemic has affected cancer diagnoses. The purpose of this study is to quantitatively determine the impact on cancer diagnostic service in public facilities across Hong Kong. Quantifying the temporal changes in the number of cancer diagnoses before, during, and after the outbreak is useful to establish the scale of the problem and assess if there has been an adequate level of response.Methods and MaterialsThis is a retrospective cohort study using a territory-wide database in Hong Kong from 2017 to 2020, and consecutive specimens received for pathologic diagnosis in public laboratories in 41 hospitals were retrieved.ResultsIn 2020, a total of 455,453 pathologic specimens were received, which amounted to a 15.5% reduction compared with the prior 3-year average (P < .001). An analysis of confirmed malignant pathologic diagnoses revealed a statistically significant reduction in colorectal (–10.0%; P < .001) and prostate (–19.7%; P < .001), nonsignificant reduction in lung (–3.0%; P = .0526), and a marginal but nonsignificant increase for breast (0.7%; P = .7592) regions. Based on time series projection data, the estimated missed cancers for the 3 regions with reduced investigations were colorectal (10.0%), lung (3.0%), and prostate (19.7%).ConclusionsVariable impact on actual malignant pathologic diagnoses based on 4 body regions was observed, with a statistically significant reduction in colorectal, lung, and prostate regions, and marginal but insignificant increase in breast regions. The findings could help public health policy with future planning and intervention.

Development of a transperineal prostate biopsy robot guided by MRI-TRUS image.

In the transrectal ultrasound (TRUS)-guided transperineal prostate biopsy, doctors determine the biopsy target by observing the prostate region in TRUS images. However, ultrasound images with low imaging quality make doctors easy to be interfered when determining the biopsy route, which reduces the biopsy success rate. This paper introduces the guidance method of magnetic resonance image (MRI) registration to ultrasound image and develops a 5-degrees of freedom robot for prostate biopsy guided by MRI-TRUS image. The robot uses a structure attached to the ultrasound probe to reduce the space occupied. By registering the posture relationship between MRI, TRUS image, ultrasonic probe and the robot base, the accurate localization of the suspected lesion area can be achieved with the preoperative MRIs. The prostate phantom biopsy based on the robotic biopsy system in this paper, the average biopsy error is 1.44 mm, and the maximum biopsy error is 2.23mm. We build a robotic biopsy platform with prostate phantom, and evaluate the biopsy accuracy of MRI-TRUS guided prostate biopsy robot, the results meet clinical prostate biopsy requirements.

The prostate of the bat Artibeus lituratus: Seasonal variations, abiotic regulation, and hormonal control.

The prostate is an important gland that contributes to the male reproductive process, producing secretions that are essential for maintaining ideal conditions for the survival of sperm. Studies indicate a wide variation in the occurrence, morphology, and physiology of this gland in mammals, especially in bats, with this variation being related not only to the number of regions and their degree of compaction/lobulation but also to fluctuations in their functioning throughout the year. Thus, the aim of this study was to evaluate the annual morphological and physiological variations of the male prostate of Artibeus lituratus and analyze their responses to annual abiotic variations and hormonal control. Sixty sexually adult males of A. lituratus were analyzed in this study, with five specimens collected monthly. Blood samples were submitted to serum hormone measurements and the prostates were morphologically, morphometrically, and immunohistochemically analyzed. The results indicated that the two prostatic regions (ventral and dorsal) of A. lituratus had different morphology, as well as different physiology and regulation. Annual fluctuations in abiotic factors seemed to influence the dorsal region more than the ventral region. Conversely, variations on testicular factors, such as testosterone and estradiol, influenced the ventral region more than the dorsal region. Despite these differences, both prostatic regions were strongly synchronized to the main reproductive peak of the species in September. The holocrine pattern of the ventral prostate was not directly affected by abiotic factors or by factors released by the testes.

Prostate Cancer Detection Using 3-D Shear Wave Elasticity Imaging

Transrectal ultrasound (TRUS) B-mode imaging provides insufficient sensitivity and specificity for prostate cancer (PCa) targeting when used for biopsy guidance. Shear wave elasticity imaging (SWEI) is an elasticity imaging technique that has been commercially implemented and is sensitive and specific for PCa. We have developed a SWEI system capable of 3-D data acquisition using a dense acoustic radiation force (ARF) push approach that leads to enhanced shear wave signal-to-noise ratio compared with that of the commercially available SWEI systems and facilitates screening of the entire gland before biopsy. Additionally, we imaged and assessed 36 patients undergoing radical prostatectomy using 3-D SWEI and determined a shear wave speed threshold separating PCa from healthy prostate tissue with sensitivities and specificities akin to those for multiparametric magnetic resonance imaging fusion biopsy. The approach measured the mean shear wave speed in each prostate region to be 4.8 m/s (Young's modulus E = 69.1 kPa) in the peripheral zone, 5.3 m/s (E = 84.3 kPa) in the central gland and 6.0 m/s (E = 108.0 kPa) for PCa with statistically significant (p < 0.0001) differences among all regions. Three-dimensional SWEI receiver operating characteristic analyses identified a threshold of 5.6 m/s (E = 94.1 kPa) to separate PCa from healthy tissue with a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of 81%, 82%, 69%, 89% and 0.84, respectively. Additionally, a shear wave speed ratio was assessed to normalize for tissue compression and patient variability, which yielded a threshold of 1.11 to separate PCa from healthy prostate tissue and was accompanied by a substantial increase in specificity, PPV and AUC, where the sensitivity, specificity, PPV, NPV and AUC were 75%, 90%, 79%, 88% and 0.90, respectively. This work illustrates the feasibility of using 3-D SWEI data to detect and localize PCa and demonstrates the benefits of normalizing for applied compression during data acquisition for use in biopsy targeting studies.

Treatment results of radiotherapy to both the prostate and metastatic sites in patients with bone metastatic prostate cancer.

Although systemic therapy is the standard treatment for metastatic prostate cancer, a randomized controlled trial showed radiotherapy to the prostate improved overall survival of metastatic prostate cancer patients with the low metastatic burden. Additionally, a randomized phase II trial showed that metastasis-directed therapy for oligo-recurrent prostate cancer improved androgen-deprivation therapy (ADT)-free survival. Therefore, administering radiotherapy to both prostate and metastatic regions might result in better outcomes. Thus, we report the treatment results of radiotherapy to both prostate and metastatic regions. Our institutional database was searched for patients who received radiotherapy to the prostate and metastatic regions. We summarized patient characteristics and treatment efficacy and performed statistical analysis to find possible prognostic factors. A total of 35 patients were included in this study. The median age was 66 years, and the median initial prostate-specific antigen (PSA) level was 32 ng/ml. The Gleason score was 7 in 10 patients, 8 in 13 patients, and 9 in 12 patients. The median radiotherapy dose was 72 Gy to the prostate and 50 Gy to the metastatic bone region. The 8-year overall survival, cause-specific survival, progression-free survival, and freedom from biochemical failure rate were 81, 85, 53, and 57%. Among the 35 patients, 12 were disease-free even after ADT was discontinued. In selected patients with metastatic prostate cancer, ADT and radiotherapy to the prostate and metastatic sites were effective. Patients with good response to ADT may benefit from radiotherapy to both prostate and metastatic regions.

MetricUNet: Synergistic image- and voxel-level learning for precise prostate segmentation via online sampling.

Fully convolutional networks (FCNs), including UNet and VNet, are widely-used network architectures for semantic segmentation in recent studies. However, conventional FCN is typically trained by the cross-entropy or Dice loss, which only calculates the error between predictions and ground-truth labels for pixels individually. This often results in non-smooth neighborhoods in the predicted segmentation. This problem becomes more serious in CT prostate segmentation as CT images are usually of low tissue contrast. To address this problem, we propose a two-stage framework, with the first stage to quickly localize the prostate region, and the second stage to precisely segment the prostate by a multi-task UNet architecture. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Therefore, the proposed network has a dual-branch architecture that tackles two tasks: (1) a segmentation sub-network aiming to generate the prostate segmentation, and (2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss. Specifically, the voxel-metric learning sub-network samples tuples (including triplets and pairs) in voxel-level through the intermediate feature maps. Unlike conventional deep metric learning methods that generate triplets or pairs in image-level before the training phase, our proposed voxel-wise tuples are sampled in an online manner and operated in an end-to-end fashion via multi-task learning. To evaluate the proposed method, we implement extensive experiments on a real CT image dataset consisting 339 patients. The ablation studies show that our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss. And the comparisons show that the proposed method outperforms the state-of-the-art methods by a reasonable margin.

DFT study of ligand binding in the β1 adrenergic receptor

The β2-adrenergic receptor, located in the prostate region, binds noradrenaline and can influence the growth of prostate tumors. The removal of Adrb2, the gene for this receptor, can halt tumor growth and thus can serve as an alternative to chemotherapy for cancer treatment. Inhibition of the receptor may have similar effects. Comparison of β2- (PDB ID: 5X7D) and β1-adrenergic receptor (PDB ID: 2Y04) structures showed a conserved binding region on Chain A offset by approximately eight amino acids between the two receptors. The structure of the β1-adrenergic receptor with the bound partial agonist salbutamol was used to create a model of the active site of the β2-adrenergic receptor. Potential inhibitors were optimized in the receptor binding site using M062X/6-31G with relaxed amino acid sidechains. Interaction energies between the ligands and the receptor were calculated using M062X/6-311+G*. Positively charged inhibitors show greater interaction energies as compared to negatively charged inhibitors.

Localized activation of the metastatic phenotype within the perineural region in prostate cancer.

253 Background: Perineural Invasion (PNI) is defined as malignant epithelial cell invasion of the perineural space and nerves. Despite widespread acknowledgement of the clinical significance of PNI as a PCa pathological finding associated with recurrence, increased risk of bone metastasis and poor survival, the molecular mechanism underlying this pathology is relatively unknown. The malignant epithelial cells within the PNI potentially provides a spatially defined “snapshot” of disease progression, as the cells switch to a more migrational phenotype associated with metastatic progression. Here we present the initial spatial PNI phenotypic characterisation in PCa. Methods: Archival FFPE blocks, with associated full clinical history, from patients who underwent a radical prostatectomy for prostate cancer were retrieved under research ethics REC#07/H1003/161+5 10_NOCL_02. Biomarkers EphA2, pEphA2s897, pMLC2, E-Cadherin, Vimentin, TOMM20, MTC01, NDUFB8, PTEN were assessed on 4µm serial sections stained using a multiplex TSA protocol, with S100, pan-cytokeratin and DAPI acting as landmarks, on a Ventana Discovery platform prior to scanning on a Versa 3 platform with Halo image analysis. Prostate zones were defined at 500µm intervals either side of the prostate capsule. Univariate and multivariate (hierarchical clustering, UMap clustering) expression analysis and correlation with clinic-pathological features was conducted within R. Results: The PNI epithelial cells within each spatial zone of the prostate are significantly different to each other (Kruskal-Wallis test p &lt; 2.2x10−16 except for MTC01 p = 5.3x10−10). In comparison with the local tumour lesion, PNI epithelial cells localised within 1000µm of the prostate edge and outside the tumour lesion, have undergone a migrational switch, gaining features associated with an activated metastatic phenotype, with increased expression of amoeboid signalling (EphA2, pEphA2s897, pMLC2) and mitochondrial defects (loss of Complex I and IV, gain of mitochondrial mass (TOMM20)). Patients clustering by multivariate expression trends across the prostate regions showed 4 distinct patient groups, with PNI epithelial cells in patient group 1 &amp; 2 displaying a more epithelial to mesenchymal (EMT) phenotype, especially in the first 1000µm inside the prostate organ. Conclusions: Cells within PNI close to the edge of the prostate have features consistent with a switch to migrational/metastatic activation in contrast to the more indolent cell type found deeper within the tumour. Further characterisation of this localised migrational upregulation will help in understanding the transition from a localised to a metastatic phenotype.

Global guidance network for breast lesion segmentation in ultrasound images.

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks.

The distribution pattern of periprostatic neurovascular bundles examined with successive celloidin slices

BackgroundAlthough several distribution patterns of periprostatic neurovascular bundles have been proposed, variant dissection technique based on these patterns still confused surgeons. The aim of this study was to describe the periprostatic neurovascular bundles and their relationship with the fascicles around prostate and provide the accurate morphologic knowledge of periprostatic tissue for prostate operation.MethodsThe pelvic viscera were obtained from 26 adult male cadavers. They were embedded in celloidin and cut into successive slices. The slices were explored with anatomic microscopy. 3-Dimensional reconstruction was achieved with celloidin sections and series software.ResultsThe prostatic capsule which surrounded the dorsal, bilateral aspect of the prostate was attached ventrally to anterior fibrous muscular stroma (AFMS). The lower part of the striated sphincter completely embraced the urethral; the upper part of this muscle covered the lower ventral surface of prostate. The upper ventral surface of prostate is covered by the circular muscle of detrusor. The levator fascia and the capsule adhered on the most convex region of the lateral prostate, but separated on the other region. The pelvic neurovascular bundles (PNVB) divided into the anterior and posterior divisions. The anterior division continued as dorsal vascular complex (DVC). The distal part of DVC entered into penile hilum. The posterior division continued as neurovascular bundles, and then as the cavernous supply (CS). The distal part of CS joined into pudendal neurovascular bundles.ConclusionsThe capsule and AFMS formed a pocket like complex. There were anterior and posterior neurovascular approaches from PNVB to penile hilum.