The Association of Periprostatic Fat and Grade Group Progression in Men with Localized Prostate Cancer on Active Surveillance

Measurements of peri-prostatic adipose tissue by MRI predict bone metastasis in patients with newly diagnosed prostate cancer

Objectives

To investigate the role of MRI measurements of peri-prostatic adipose tissue (PPAT) in predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa).

Methods

We performed a retrospective study on 156 patients newly diagnosed with PCa by prostate biopsy between October 2010 and November 2022. Clinicopathologic characteristics were collected. Measurements including PPAT volume and prostate volume were calculated by MRI, and the normalized PPAT (PPAT volume/prostate volume) was computed. Independent predictors of BM were determined by univariate and multivariate logistic regression analysis, and a new nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves were used to estimate predictive performance.

Results

PPAT and normalized PPAT were associated with BM (P<0.001). Normalized PPAT positively correlated with clinical T stage(cT), clinical N stage(cN), and Grading Groups(P<0.05). The results of ROC curves indicated that PPAT and normalized PPAT had promising predictive value for BM with the AUC of 0.684 and 0.775 respectively. Univariate and multivariate analysis revealed that high normalized PPAT, cN, and alkaline phosphatase(ALP) were independently predictors of BM. The nomogram was developed and the concordance index(C-index) was 0.856.

Conclusions

Normalized PPAT is an independent predictor for BM among with cN, and ALP. Normalized PPAT may help predict BM in patients with newly diagnosed prostate cancer, thus providing adjunctive information for BM risk stratification and bone scan selection.

The association of body mass index with tumor aggression among men undergoing radical prostatectomy

OBJECTIVES

To evaluate the association of preoperative body mass index (BMI) on adverse pathology in peripheral (PZ) and transition zone (TZ) tumors at time of prostatectomy for localized prostate cancer.

METHODS

Clinical and pathologic characteristics were obtained from up to 100 consecutive prostatectomy patients from 10 prostate surgeons. BMI groups included normal (18.5-24.9), overweight (25-29.9) and obese (> 29.9). "Aggressive" pathology was defined as the presence of Grade Group (GG) 3 or higher and/or pT3a or higher. Pathologic characteristics were evaluated for association with BMI using univariate analyses. Our primary outcome was the association of BMI with adverse pathology, which was assessed using logistic regression accounting for patient age. We hypothesized that obese BMI would be associated with aggressive TZ tumor.

RESULTS

Among 923 patients, 140 (15%) were classified as "normal" BMI, 413 (45%) were "overweight", and 370 (40%) were "obese." 474 patients (51%) had aggressive PZ tumors while 102 (11%) had aggressive TZ tumors. "Obese" BMI was not associated with aggressive TZ tumor compared to normal weight. Increasing BMI group was associated with overall increased risk of aggressive PZ tumor (HR 1.56 [95CI 1.04-2.34]; P = 0.03). Among patients with GG1 or GG2, increasing BMI was associated with presence of pT3a or higher TZ tumor (P = 0.03).

CONCLUSIONS

Increased BMI is associated with adverse pathology in PZ tumors. TZ adverse pathology risk may be increased among obese men with GG1 or GG2 disease, which has implications for future studies assessing behavioral change among men whose tumors are actively monitored.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning

Objective

This study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT).

Methods

Currently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU).

Results

Cropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them.

Conclusion

This U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

Periprostatic Adipose Tissue: A New Perspective for Diagnosing and Treating Prostate Cancer

Prostate cancer (PCa) is the most common tumor of the male genitourinary system. It will eventually progress to fatal metastatic castration-resistant prostate cancer, for which treatment options are limited. Adipose tissues are distributed in various parts of the body. They have different morphological structures and functional characteristics and are associated with the development of various tumors. Periprostatic adipose tissue (PPAT) is the closest white visceral adipose tissue to the prostate and is part of the PCa tumor microenvironment. Studies have shown that PPAT is involved in PCa development, progression, invasion, and metastasis through the secretion of multiple active molecules. Factors such as obesity, diet, exercise, and organochlorine pesticides can affect the development of PCa indirectly or directly through PPAT. Based on the mechanism of PPAT's involvement in regulating PCa, this review summarized various diagnostic and therapeutic approaches for PCa with potential applications to assess the progression of patients' disease and improve clinical outcomes.

Periprostatic Adipose Tissue MRI Radiomics-Derived Features Associated with Clinically Significant Prostate Cancer

Background: Altered systemic and cellular lipid metabolism plays a pivotal role in the pathogenesis of prostate cancer (PCa). In this study, we aimed to characterize T1-magnetic resonance imaging (MRI)-derived radiomic parameters of periprostatic adipose tissue (PPAT) associated with clinically significant PCa (Gleason score ≥7 [3 + 4]) in a cohort of men who underwent robot-assisted prostatectomy. Methods: Preoperative MRI scans of 98 patients were identified. The volume of interest was defined by identifying an annular shell-like region on each MRI slice to include all surgically resectable visceral adipose tissue. An optimal biomarker method was used to identify features from 7631 intensity- and texture-based properties that maximized the classification of patients into clinically significant PCa and indolent tumors at the final pathology analysis. Results: Six highest ranked optimal features were derived, which demonstrated a sensitivity, specificity, and accuracy of association with the presence of clinically significant PCa, and area under a receiver operating characteristic curve of 0.95, 0.39 0.82, and 0.82, respectively. Conclusion: A highly independent set of PPAT features derived from MRI scans that predict patients with clinically significant PCa was developed and tested. With future external validation, these features may provide a more precise scientific basis for deciding to omit biopsies in patients with borderline prostate-specific antigen kinetics and multiparametric MRI readings and help in the decision of enrolling patients into active surveillance.

Obesity-Related Cross-Talk between Prostate Cancer and Peripheral Fat: Potential Role of Exosomes

The molecular mechanisms of obesity-induced cancer progression have been extensively explored because of the significant increase in obesity and obesity-related diseases worldwide. Studies have shown that obesity is associated with certain features of prostate cancer. In particular, bioactive factors released from periprostatic adipose tissues mediate the bidirectional communication between periprostatic adipose tissue and prostate cancer. Moreover, recent studies have shown that extracellular vesicles have a role in the relationship between tumor peripheral adipose tissue and cancer progression. Therefore, it is necessary to investigate the feedback mechanisms between prostate cancer and periglandular adipose and the role of exosomes as mediators of signal exchange to understand obesity as a risk factor for prostate cancer. This review summarizes the two-way communication between prostate cancer and periglandular adipose and discusses the potential role of exosomes as a cross-talk and the prospect of using adipose tissue as a means to obtain exosomes in vitro. Therefore, this review may provide new directions for the treatment of obesity to suppress prostate cancer.

Adiposity and cancer survival: a systematic review and meta-analysis

PURPOSE

The increasing availability of clinical imaging tests (especially CT and MRI) that directly quantify adipose tissue has led to a rapid increase in studies examining the relationship of visceral, subcutaneous, and overall adiposity to cancer survival. To summarize this emerging body of literature, we conducted a systematic review and meta-analysis of imaging-measured as well as anthropometric proxies for adipose tissue distribution and cancer survival across a wide range of cancer types.

METHODS

Using keywords related to adiposity, cancer, and survival, we conducted a systematic search of the literature in PubMed and MEDLINE, Embase, and Web of Science Core Collection databases from database inception to 30 June 2021. We used a random-effect method to calculate pooled hazard ratios (HR) and corresponding 95% confidence intervals (CI) within each cancer type and tested for heterogeneity using Cochran's Q test and the I2 test.

RESULTS

We included 203 records for this review, of which 128 records were utilized for quantitative analysis among 10 cancer types: breast, colorectal, gastroesophageal, head and neck, hepatocellular carcinoma, lung, ovarian, pancreatic, prostate, and renal cancer. We found that imaging-measured visceral, subcutaneous, and total adiposity were not significantly associated with increased risk of overall mortality, death from primary cancer, or cancer progression among patients diagnosed with these 10 cancer types; however, we found significant or high heterogeneity for many cancer types. For example, heterogeneity was similarly high when the pooled HRs (95% CI) for overall mortality associated with visceral adiposity were essentially null as in 1.03 (0.55, 1.92; I2 = 58%) for breast, 0.99 (0.81, 1.21; I2 = 71%) for colorectal, versus when they demonstrated a potential increased risk 1.17 (0.85, 1.60; I2 = 78%) for hepatocellular carcinoma and 1.62 (0.90, 2.95; I2 = 84%) for renal cancer.

CONCLUSION

Greater adiposity at diagnosis (directly measured by imaging) is not associated with worse survival among cancer survivors. However, heterogeneity and other potential limitations were noted across studies, suggesting differences in study design and adiposity measurement approaches, making interpretation of meta-analyses challenging. Future work to standardize imaging measurements and data analyses will strengthen research on the role of adiposity in cancer survival.

Automatic prostate and peri-prostatic fat segmentation based on pyramid mechanism fusion network for T2-weighted MRI

BACKGROUND AND OBJECTIVE

Automatic and accurate segmentation of prostate and peri-prostatic fat in male pelvic MRI images is a critical step in the diagnosis and prognosis of prostate cancer. The boundary of prostate tissue is not clear, which makes the task of automatic segmentation very challenging. The main issues, especially for the peri-prostatic fat, which is being offered for the first time, are hazy boundaries and a large form variation.

METHODS

We propose a pyramid mechanism fusion network (PMF-Net) to learn global features and more comprehensive context information. In the proposed PMF-Net, we devised two pyramid techniques in particular. A pyramid mechanism module made of dilated convolutions of varying rates is inserted before each down sample of the fundamental network architecture encoder. The module is intended to address the issue of information loss during the feature coding process, particularly in the case of segmentation object boundary information. In the transition stage from encoder to decoder, pyramid fusion module is designed to extract global features. The features of the decoder not only integrate the features of the previous stage after up sampling and the output features of pyramid mechanism, but also include the features of skipping connection transmission under the same scale of the encoder.

RESULTS

The segmentation results of prostate and peri-prostatic fat on numerous diverse male pelvic MRI datasets show that our proposed PMF-Net has higher performance than existing methods. The average surface distance (ASD) and Dice similarity coefficient (DSC) of prostate segmentation results reached 10.06 and 90.21%, respectively. The ASD and DSC of the peri-prostatic fat segmentation results reached 50.96 and 82.41%.

CONCLUSIONS

The results of our segmentation are substantially connected and consistent with those of expert manual segmentation. Furthermore, peri-prostatic fat segmentation is a new issue, and good automatic segmentation has substantial therapeutic implications.

Thromboinflammatory Processes at the Nexus of Metabolic Dysfunction and Prostate Cancer: The Emerging Role of Periprostatic Adipose Tissue

Simple Summary

As overweight and obesity increase among the population worldwide, a parallel increase in the number of individuals diagnosed with prostate cancer was observed. There appears to be a relationship between both diseases where the increase in the mass of fat tissue can lead to inflammation. Such a state of inflammation could produce many factors that increase the aggressiveness of prostate cancer, especially if this inflammation occurred in the fat stores adjacent to the prostate. Another important observation that links obesity, fat tissue inflammation, and prostate cancer is the increased production of blood clotting factors. In this article, we attempt to explain the role of these latter factors in the effect of increased body weight on the progression of prostate cancer and propose new ways of treatment that act by affecting how these clotting factors work.

Abstract

The increased global prevalence of metabolic disorders including obesity, insulin resistance, metabolic syndrome and diabetes is mirrored by an increased incidence of prostate cancer (PCa). Ample evidence suggests that these metabolic disorders, being characterized by adipose tissue (AT) expansion and inflammation, not only present as risk factors for the development of PCa, but also drive its increased aggressiveness, enhanced progression, and metastasis. Despite the emerging molecular mechanisms linking AT dysfunction to the various hallmarks of PCa, thromboinflammatory processes implicated in the crosstalk between these diseases have not been thoroughly investigated. This is of particular importance as both diseases present states of hypercoagulability. Accumulating evidence implicates tissue factor, thrombin, and active factor X as well as other players of the coagulation cascade in the pathophysiological processes driving cancer development and progression. In this regard, it becomes pivotal to elucidate the thromboinflammatory processes occurring in the periprostatic adipose tissue (PPAT), a fundamental microenvironmental niche of the prostate. Here, we highlight key findings linking thromboinflammation and the pleiotropic effects of coagulation factors and their inhibitors in metabolic diseases, PCa, and their crosstalk. We also propose several novel therapeutic targets and therapeutic interventions possibly modulating the interaction between these pathological states.

Adult preventive vaccines with other synergistic lifestyle options: is it time to add these ancillary benefits to the overall AS management checklist?

PURPOSE

To review the potential ancillary cardiovascular and other health impacts of compliance with general adult vaccination series in the prostate cancer active surveillance (AS) population. No previous review has been published in regard to this specific topic.

METHODS

Literature review of PubMed data up to December 2020 RESULTS: Compliance rates for adult vaccination are in the approximate anemic range of 25-50% with occasional higher rates of specific vaccines in the elderly population including annual influenza and pneumococcal prevention. Herpes zoster (HZ) and numerous other vaccine preventive illnesses are associated with an increased risk of cardiovascular events. Preliminary evidence suggests vaccine compliance could reduce overall morbidity and mortality, and adherence to heart healthy lifestyle changes and parameters could further improve vaccine efficacy and overall wellness. COVID-19 vaccine utilization and research should also continue to reinforce the direct and ancillary benefits of this entire preventive intervention category.

CONCLUSIONS

Multiple ancillary lifestyle change recommendations could be included in the AS criteria to potentially reduce morbidity and mortality in this population, and perhaps the most unsung intervention is to improve the inadequate rates of general adult vaccination compliance and other heart healthy behavioral changes that impact their efficacy. Heart health, prostate health, and immune system health are closely interlinked.