Analysis of Morphological and Morphometric Changes in a Parenchymal Tissue after the Radiofrequency Ablation Procedure.

Background and Objectives: Prostate cancer is on the rise in the European Union, and radiofrequency ablation (RFA) is one of the minimally invasive treatment options used for its treatment. Therefore, the aim of this study was to investigate and analyze the effects of RFA on prostate tissues. Materials and Methods: A standard prostate RFA procedure was performed on 13 non-purebred dogs in three sessions: no cooling (NC), cooling with a 0.1% NaCl solution (C.01), and cooling using a 0.9% NaCl solution (C.09). Microtome-cut 2-3 µm sections of prostate samples were stained with hematoxylin and eosin and further examined. Results: A histopathologic evaluation identified four zones of exposure: direct, application, necrosis, and transitional, as the damage on tissues decreased going further from the ablation site. The areas and perimeters of these zones were calculated, and geometric shapes of ablative lesions were evaluated using the quotient formula. Areas and perimeters of prostate tissue lesions in the NC and C.09 sessions were of similar size, whereas those found in C.01 were statistically significantly smaller. Lesions observed in session C.01 were of the most regular geometric shape, while the most irregular ones were found in session C.09. The shapes of lesions closest to the ablation electrode were the most irregular, becoming more regular the further away from the electrode they were. Conclusions: Prostate RFA leads to tissue damage with distinct morphological zones. Notably, the prostate lesions were the smallest and the most regular in shape after RFA procedures using the 0.1% NaCl cooling solution. It can be argued that smaller ablation sites may result in smaller scars, thus allowing for faster tissue healing if the blood flow and innervation at the ablation site are not compromised.

Histological and quantitative analyzes of the stromal and acinar components of normal human prostate zones.

BACKGROUND: McNeal divided the human prostate into three major anatomical areas: the peripheral zone (PZ), the central zone (CZ), and the transition zone (TZ). Each of these areas is biologically and histologically distinct. The PZ and TZ have clinical significance and are associated with prostate cancer (PC) and benign prostatic hyperplasia (BPH), respectively. Therefore, the objective of the present study was to quantitatively and qualitatively analyze the parenchymal and stromal components that constitute the different prostate zones. METHODS: We assessed 19 samples from each prostate zone. The samples were obtained from necropsies of young people between 18 and 32 years of age with intact urogenital tracts. The samples were fixed in 4% buffered formalin and processed for paraffin embedding. Sections with a thickness of five micrometres were obtained from each sample. The sections were stained using histochemical and immunohistochemical techniques to identify the acinar and stromal components of each zone. Photomicrographs were obtained for morphometric analysis using an algorithm based on color segmentation. Data were analyzed using one-way analysis of variance (ANOVA) with the Bonferroni post-test. Differences with P < 0.05 were regarded as statistically significant. RESULTS: Collagen fibres were more numerous in the TZ (+40.26%; P = 0.0230) than in the PZ. Muscle fibres were also more numerous in the TZ (+47.05%; P = 0.0120) than in the PZ. Elastic system fibres in the TZ significantly differed from those in the PZ (+84.61%; P = 0.0012) and the CZ (+61.66%; P = 0.0074). Similarly, nerves in the PZ (-42.86%; P = 0.0107) significantly differed from nerves in the CZ. Epithelial height was lower in the TZ than in the PZ (-30.17%; P = 0.0034) and the CZ (-25.01%; P = 0.0330). CONCLUSION: Our objective, quantitative data regarding the various elements that constitute the normal prostate stroma allowed us to reveal differences among prostate zones. This study established patterns for normal parameters and may be used for posterior comparisons in histopathological analysis.

Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer.

PURPOSE: To determine whether the morphological parameters of prostate zones and tumors on magnetic resonance imaging (MRI) can predict the tumor-stage (T-stage) of prostate cancer (PCa) and establish an optimal T-stage diagnosis protocol based on three-dimensional reconstruction and quantization after image segmentation. METHODS: A dataset of the prostate MRI scans and clinical data of 175 patients who underwent biopsy and had pathologically proven PCa from January 2018 to November 2020 was retrospectively analyzed. The authors manually segmented and measured the volume, major axis, and cross-sectional area of the peripheral zone (PZ), transition zone, central zone (CZ), anterior fibromuscular stroma, and tumor. The differences were evaluated by the One-Way analysis of variance, Pearson's chi-squared test, or independent samples t-test. Spearman's correlation coefficient and receiver operating characteristic curve analyses were also performed. The cut-off values of the T-stage diagnosis were generated using Youden's J index. RESULTS: The prostate volume (PV), PZ volume (PZV), CZ volume, tumor's major axis (TA), tumor volume (TV), and volume ratio of the TV and PV were significantly different among stages T1 to T4. The cut-off values of the PV, PZV, CZV, TA, TV, and the ratio of TV/PV for the discrimination of the T1 and T2 stages were 53.63 cm3, 11.60 cm3, 1.97 cm3, 2.30 mm, 0.90 cm3, and 0.03 [area under the curves (AUCs): 0.628, 0.658, 0.610, 0.689, 0.724, and 0.764], respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T2 and T3 stages were 2.80 mm, 8.29 cm3, and 0.12 (AUCs: 0.769, 0.702, and 0.688), respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T3 and T4 stages were 4.17 mm, 18.71 cm3, and 0.22 (AUCs: 0.674, 0.709, and 0.729), respectively. CONCLUSION: The morphological parameters of the prostate zones and tumors on the MRIs are simple and valuable diagnostic factors for predicting the T-stage of patients with PCa, which can help make accurate diagnoses and lateral treatment decisions.

Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond.

Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentation of TZ, PZ, distal prostatic urethra (DPU) and the anterior fibromuscular stroma (AFS), the task becomes more challenging and has not yet been solved at the level of human performance. One reason might be the insufficient amount of labeled data for supervised training. Therefore, we propose to apply a semi-supervised learning (SSL) technique named uncertainty-aware temporal self-learning (UATS) to overcome the expensive and time-consuming manual ground truth labeling. We combine the SSL techniques temporal ensembling and uncertainty-guided self-learning to benefit from unlabeled images, which are often readily available. Our method significantly outperforms the supervised baseline and obtained a Dice coefficient (DC) of up to 78.9%, 87.3%, 75.3%, 50.6% for TZ, PZ, DPU and AFS, respectively. The obtained results are in the range of human inter-rater performance for all structures. Moreover, we investigate the method's robustness against noise and demonstrate the generalization capability for varying ratios of labeled data and on other challenging tasks, namely the hippocampus and skin lesion segmentation. UATS achieved superiority segmentation quality compared to the supervised baseline, particularly for minimal amounts of labeled data.

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation.

Automatic segmentation of prostatic zones on multiparametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

MR Imaging of Prostate Zonal Anatomy.

McNeal first described the zonal anatomy of the prostate about 40 years ago, outlining 4 zones of the prostate and defining their relation to the urethra and the ejaculatory ducts. The zonal anatomy remains the accepted model for describing the prostate and the zones are well-depicted on MR imaging, including the central zone, which until recently was grouped with the transition zone in the radiology literature. An accurate understanding of the zonal anatomy and periprostatic anatomy is key for accurate interpretation of the prostate MR imaging.

Increased transcriptional and metabolic capacity for lipid metabolism in the peripheral zone of the prostate may underpin its increased susceptibility to cancer.

The human prostate gland comprises three distinct anatomical glandular zones, namely the peripheral, central and transitional zones. Although prostate cancer can arise throughout the prostate, it is more frequent in the peripheral zone. In contrast, hyperplasia occurs most frequently in the transitional zone. In this paper, we test the hypothesis that peripheral and transitional zones have distinct metabolic adaptations that may underlie their different inherent predispositions to cancer and hyperplasia. In order to do this, we undertook RNA sequencing and high-throughput metabolic analyses of non-cancerous tissue from the peripheral and transitional zones of patients undergoing prostatectomy. Integrated analysis of RNAseq and metabolomic data revealed that transcription of genes involved in lipid biosynthesis is higher in the peripheral zone, which was mirrored by an increase in fatty acid metabolites, such as lysolipids. The peripheral zone also exhibited increased fatty acid catabolic activity and contained higher level of neurotransmitters. Such increased capacity for de novo lipogenesis and fatty acid oxidation, which is characteristic of prostate cancer, can potentially provide a permissive growth environment within the peripheral zone for cancer growth and also transmit a metabolic growth advantage to newly emerging clones themselves. This lipo-rich priming may explain the observed susceptibility of the peripheral zone to oncogenesis.