Amr Mahran

Objective: To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa.Methods: This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (C_S) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (C_R) was trained for BCR. An integrated classifier C_S+R was then trained using predictions from C_S and C_R. These models were trained on D₁ (N = 71, 27 BCR+) and evaluated on independent hold-out set D₂ (N = 62, 12 BCR+). C_S+R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years.Results: C_S+R resulted in a higher AUC (0.75) compared to C_R (0.70, p = 0.04) and C_S (0.69, p = 0.01) on D₂ in predicting BCR. On univariable analysis, C_S+R achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. C_S+R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). C_S+R resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07).Conclusions: Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.

Objective: To evaluate differences in perioperative clinical outcomes in men undergoing artificial urinary sphincter (AUS) implantation in primary versus replacement settings. Secondarily, we aimed to identify patient-related factors contributing to complications associated with AUS placement. Materials and Methods: A review of the American College of Surgeons-National Surgical Quality Improvement Program was performed between 2010 and 2018 identifying males undergoing AUS implantation. Subjects were further subdivided into primary implantation or removal/replacement of AUS simultaneously via current procedural terminology codes 53445 and 53447, respectively. 30-Day postoperative outcomes were compared between cohorts using t-test and Fisher’s exact test. The relationship between patient factors and complications was evaluated using logistic regression. Results: A total of 1,892 patients were identified: 1,445 primary AUS placement and 447 AUS replacement procedures. Patients undergoing AUS replacement were statistically older than those undergoing primary implantation (71.4 vs 69.7 years, P < .001). AUS replacement procedures were associated with an increased rate of superficial surgical site infection (SSI) compared to primary procedures (1.3% vs 0.4%, P ¼ .042). There were no differences identified between cohorts for deep SSI, cardiopulmonary complications, reoperation, operative time, or length of stay. Logistic regression demonstrated that higher body mass index was found to be independent risk factors for any complications, and diabetes mellitus was associated with increased risk of AUS-related readmission. Conclusion: Within the perioperative period, patients undergoing replacement AUS have an increased risk of superficial SSI compared to primary AUS implantation. These findings can assist with appropriate perioperative counseling of patients undergoing primary and replacement AUS implantations. Cite this article as: Jella T, Fernstrum A, Callegari M, et al. Perioperative outcomes between primary and replacement artificial urinary sphincter surgery: An ACS-NSQIP analysis. Turk J Urol. 2021; 47(5): 427-435

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.