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Background This study evaluated the impact of sarcopenia and psoas major muscle volume on the survival of patients with upper urinary tract urothelial carcinoma who had undergone radical nephroureterectomy. Methods We reviewed data from 110 patients treated with radical nephroureterectomy in our department between June 2007 and February 2017. Psoas major muscle volume was quantified based on computed tomography data using Synapse Vincent software. The psoas major muscle volume index was calculated as psoas major muscle volume/height squared (cm3/m2). We analysed relapse-free survival, cancer-specific survival and overall survival after radical nephroureterectomy to identify factors that predicted patient survival. Results The median psoas major muscle volume index was 121.5 cm3/m2, and the psoas major muscle volume index was <100 cm3/m2 in 34 of 110 patients (30.9%). Multivariate analysis indicated that ≥pT3-stage cancer, lymphovascular invasion and a psoas major muscle volume index of <100 cm3/m2 were independent predictors of shorter relapse-free survival, cancer-specific survival and overall survival. Using these factors, patients were stratified into three groups: low, intermediate and high risks for relapse-free survival, cancer-specific survival and overall survival. Conclusions Low psoas major muscle volume resulting from sarcopenia, high T stage and the presence of lymphovascular invasion was associated with poor survival in patients with urinary tract urothelial carcinoma who had undergone radical nephroureterectomy, supporting the use of psoas major muscle volume as a new objective prognostic marker.

This study aimed to evaluate the usefulness of the LDN-PSA (LacdiNAc-glycosylated-prostate specific antigen) in detecting clinically significant prostate cancer in patients suspected of having clinically significant prostate cancer on multiparametric magnetic resonance imaging. Materials and Methods: Patients with prostate specific antigen levels ranging between 3.0 ng/mL and 20 ng/mL and suspicious lesions with PI-RADS (Prostate ImagingeReporting and Data System) category !3 were included prospectively. The LDN-PSA was measured using an automated 2-step Wisteria floribunda agglutinin lectineantieprostate specific antigen antibody sandwich immunoassay. Results: Two hundred four patients were included. Clinically significant prostate cancer was detected in 105 patients. On multivariable logistic regression analysis, prostate specific antigen density (OR 1.61, P [ .010), LDN-PSAD (OR 1.04, P [ .012), highest PI-RADS category (3 vs 4, 5; OR 14.5, P < .0001), and location of the lesion with highest PI-RADS category (transition zone vs peripheral zone) (OR 0.34, P [ .009) were significant risk factors for detecting clinically significant prostate cancer. Among the patients with the highest PI-RADS category 3 (n[113), clinically significant prostate cancer was detected in 28 patients. On multivariable logistic regression analysis to predict the detection of clinically significant prostate cancer in patients with the highest PI-RADS category 3, age (OR 1.10, P [ .026) and LDN-PSAD (OR 1.07, P < .0001) were risk factors for detecting clinically significant prostate cancer. Conclusions: LDN-PSAD would be a biomarker for detecting clinically significant prostate cancer in patients with prostate specific antigen levels 20 ng/mL and suspicious lesions with PI-RADS category !3. The use of LDN-PSAD as an adjunct to the use of prostate specific antigen levels would avoid unnecessary biopsies in patients with the highest PI-RADS category 3. Multi-institutional studies with large population are recommended.

Background Collecting system entry in robot-assisted partial nephrectomy may occur even in cases showing a low N factor in the R.E.N.A.L nephrometry score. Therefore, we focused on the tumor contact surface area with the adjacent renal parenchyma and attempted to construct a novel predictive model for collecting system entry. Methods Among 190 patients who underwent robot-assisted partial nephrectomy at our institution from 2015 to 2021, 94 patients with a low N factor (12) were analyzed. Contact surface was measured with three-dimensional imaging software and defined as the C factor, classified as C1, < 10 cm2; C2, ≥ 10 and < 15 cm2; and C3: ≥ 15 cm2. Additionally, a modified R factor (mR) was classified as mR1, < 20 mm; mR2, ≥ 20 and < 40 mm; and mR3, ≥ 40 mm. We discussed the factors influencing collecting system entry, including the C factor, and created a novel collecting system entry predictive model. Results Collecting system entry was observed in 32 patients with a low N factor (34%). The C factor was the only independent predictive factor for collecting system entry in multivariate regression analysis (odds ratio: 4.195, 95% CI: 2.160–8.146, p < 0.0001). Models including the C factor showed better discriminative power than the models without the C factor. Conclusions The new predictive model, including the C factor in N1-2 cases, may be beneficial, considering its indication for preoperative ureteral catheter placement in patients undergoing robot-assisted partial nephrectomy.

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