Quantification and molecular correlates of tertiary lymphoid structures in primary prostate cancer.

OBJECTIVE: To morphologically describe tertiary lymphoid structures (TLS) in prostatectomy specimens and correlate them with clinical and transcriptomic features. METHODOLOGY: A total of 72 consecutive cases of entirely submitted radical prostatectomy (RP) patients tested with the Decipher Genomic Classifier were included in the study. Images were manually annotated using QuPath tools to denote tumor regions and each cluster of TLS. Clusters of lymphocytes that were surrounded on all four sides by tumor were defined as intra-tumor TLS (IT-TLS). Clusters of lymphocytes at the leading edge of carcinoma with either the prostatic pseudocapsule or benign parenchyma at one end were defined as peri-tumor TLS (PT-TLS). A classification algorithm to distinguish lymphocytes from non-lymphocytic cells using a supervised machine learning model was used. The associations between TLS formation and 265 gene expression-based signatures were examined. RESULTS: The magnitude of total TLS correlations with primary tumor gene expression signatures was moderate (~0.35-0.5) with several HLA, T-cell and B-cell Cluster signatures, showing positive correlation with various metrics for quantification of TLS. On the other hand, immune suppressive signatures (Treg, MDSC) were negatively correlated. While signatures for macrophages, NK cells and other immune cell types were uncorrelated for the most part. PT-TLS was associated with MHC signatures while IT TLS correlated with MHC and T-cell signatures. CONCLUSIONS: Clusters of inflammatory cells in the RP specimen can be divided spatially into PT TLS and IT-TLS, each with its unique molecular correlates of tumor immune microenvironment. The presence of TLS is positively correlated with MHC signatures, T- cell and B-cell cluster signatures but, negatively correlated with immune suppressive signatures. A subset of prostate cancer demonstrate a robust inflammatory response, and warrant further characterization in larger cohorts.

MRI-based radiomic features of the urinary bladder wall identify patients with moderate-to-severe international prostate symptom score.

BACKGROUND: The International Prostate Symptom Score (IPSS) is a patient-reported measurement to assess the lower urinary tract symptoms of bladder outlet obstruction. Bladder outlet obstruction induces molecular and morphological alterations in the urothelium, suburothelium, detrusor smooth muscle cells, detrusor extracellular matrix, and nerves. We sought to analyze MRI-based radiomics features of the urinary bladder wall and their association with IPSS. METHOD: In this retrospective study, 87 patients who had pelvic MRI scans were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features of the bladder wall from MR images, including morphological, intensity-based, and texture-based features, along with clinical variables. Mathematical models were created using subsets of features and evaluated based on their ability to discriminate between low and moderate-to-severe IPSS (less than 8 vs. equal to or greater than 8). RESULTS: Of the 7,666 features per patient, four highest-ranking optimal features were derived (all texture-based features), which provided a classification accuracy of 0.80 with a sensitivity, specificity, and area under the receiver operating characteristic curve of 0.81, 0.81, and 0.87, respectively. CONCLUSION: A highly independent set of urinary bladder wall features derived from MRI scans were able to discriminate between patients with low vs. moderate-to-severe IPSS with accuracy of 80%. Such differences in MRI-based properties of the bladder wall in patients with varying IPSS's might reflect differences in underlying molecular and morphological alterations that occur in the setting of chronic bladder outlet obstruction.

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.

MRI-Based Radiomics Analysis of Levator Ani Muscle for Predicting Urine Incontinence after Robot-Assisted Radical Prostatectomy.

BACKGROUND: The exact role of the levator ani (LA) muscle in male continence remains unclear, and so this study aims to shed light on the topic by characterizing MRI-derived radiomic features of LA muscle and their association with postoperative incontinence in men undergoing prostatectomy. METHOD: In this retrospective study, 140 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer using preoperative MRI were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features from MRI images, including morphological, intensity-based, and texture-based features of the LA muscle, along with clinical variables. Mathematical models were created using subsets of features and were evaluated based on their ability to predict continence outcomes. RESULTS: Univariate analysis showed that the best discriminators between continent and incontinent patients were patients age and features related to LA muscle texture. The proposed feature selection approach found that the best classifier used six features: age, LA muscle texture properties, and the ratio between LA size descriptors. This configuration produced a classification accuracy of 0.84 with a sensitivity of 0.90, specificity of 0.75, and an area under the ROC curve of 0.89. CONCLUSION: This study found that certain patient factors, such as increased age and specific texture properties of the LA muscle, can increase the odds of incontinence after RARP. The results showed that the proposed approach was highly effective and could distinguish and predict continents from incontinent patients with high accuracy.

Oncological and Functional Outcomes of Robot-Assisted Radical Prostatectomy in Kidney Transplant Recipients.

OBJECTIVE: Management of prostate cancer in kidney transplant recipients presents a unique surgical challenge due to the risk of direct or indirect injury to the transplanted kidney. Herein, we report the largest single center study of Robot-assisted Radical prostatectomy (RARP) in kidney transplant recipients. METHODS: Between Jan 2014-2019, 14 kidney transplant recipients with prostate cancer underwent RARP. Clinical and pathological features, perioperative and postoperative complications were retrospectively evaluated. Continence was defined as by patient utilization of zero urinary pads postoperatively. RESULTS: The median (IQR) age at RARP was 60.2 (57.8-61.3) years, the interval between kidney transplant and RARP was 8.1 ± 7.5 years. The median (IQR) PSA was 6.9 (4-8.6); 10 of 14 patients had intermediate or high-risk prostate cancer. The median ASA score was 3, the mean (SD) operative time was 129.7 (26.3) minutes, and mean (SD) blood loss was 110 (44.6) ml. All cases were completed robotically, there was no graft loss or injury to transplanted ureter, and the mean length of stay was 1 (0.26) day.Final pathology demonstrated that 42.8% (6/14) of the patients had nonorgan confined disease (pT3a/T3b). 50% (7/14) of the patients were upgraded to higher risk Gleason disease on final surgical pathology. Post-RARP continence rate at 3 months, and 12 months were 45.5% (5/11) and 87.5% (7/8), respectively. CONCLUSION: RARP following kidney transplantation represents a safe and feasible operation which does not appear to compromise oncological or transplant outcomes.

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos.

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of skeletal muscle mass and adipose tissues on Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) may inform surgery risk evaluation and disease prognosis. This article describes two quantification methods originally described by Mourtzakis et al. and Avrutin et al.: tissue segmentation and linear measurement of skeletal muscle. Patients' cross-sectional image at the midpoint of the third lumbar vertebra was obtained for both measurements. For segmentation, the images were imported into Slice-O-Matic and colored for skeletal muscle, intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue. Then, surface areas of each tissue type were calculated using the tag surface area function. For linear measurements, the height and width of bilateral psoas and paraspinal muscles at the level of the third lumbar vertebra are measured and the calculation using these four values yield the estimated skeletal muscle mass. Segmentation analysis provides quantitative, comprehensive information about the patients' body composition, which can then be correlated with disease progression. However, the process is more time-consuming and requires specialized training. Linear measurements are an efficient and clinic-friendly tool for quick preoperative evaluation. However, linear measurements do not provide information on adipose tissue composition. Nonetheless, these methods have wide applications in a variety of diseases to predict surgical outcomes, risk of disease progression and inform treatment options for patients.