Laparoscopic placement of left renal vein extravascular stenting in treatment of nutcracker syndrome: Techniques and long-term outcomes.

OBJECTIVES: We aimed to assess the feasibility and efficacy of laparoscopic extravascular stent in treatment of nutcracker syndrome by transperitoneal or retroperitoneal approach. METHODS: Seventy-six patients with nutcracker syndrome were retrospectively enrolled from a tertiary referral center, and underwent transperitoneal (63 patients) or retroperitoneal (13 patients) laparoscopic extravascular stent from March 2011 to December 2020. Surgical parameters, complications, imaging and clinical outcomes were collected and analyzed. RESULTS: All procedures were successfully carried out without open conversion. The median operation time, estimated blood loss, and postoperative hospital day were 120 (interquartile range [IQR]: 90-144) min, 20 (IQR: 10-30) ml, and 7 (IQR: 6-9) days. At a median follow-up of 52 (range: 9-127) months, 60 (79%) patients had complete symptom resolution, 14 (18%) patients had significant symptom improvement, and 2 (3%) patients reported no symptom improvement. Ninety-four percent (50/53) of hematuria, 91% (30/33) of proteinuria, and 89% (25/28) of flank/abdominal pain resolved after extravascular LRV stenting. No significant differences were detected in surgery parameters and recovery rates of clinical symptoms between two approaches (each p > 0.05). However, patients with transperitoneal approach need longer to achieve complete recovery compared with retroperitoneal approach (8.7 vs. 1.5 months, p = 0.016). CONCLUSIONS: Laparoscopic extravascular stent performed either transperitoneally or retroperitoneally is a feasible and effective option in treatment of nutcracker syndrome. Retroperitoneal laparoscopic extravascular stent required shorter time to achieve complete recovery, which should be considered whenever possible in surgical decision-making.

Evaluating Incidence, Location, and Predictors of Positive Surgical Margin Among Chinese Men Undergoing Robot-Assisted Radical Prostatectomy.

PURPOSE: To evaluate the incidence and locations of positive surgical margin (PSM) among Chinese men undergoing RARP and identify the preoperative predictors for PSM. METHODS: We retrospectively identified 393 patients who underwent RARP according to inclusion criteria by single surgeon in our hospital. PSM was defined as the presence of cancer adjacent to inked surface of the specimen and categorized into four groups based on locations: apex, posterolateral, base, and multifocal. Logistic regression analysis was performed to identify the predictors of overall and location-specific PSM. RESULTS: The overall PSM rate was 133/393 (34%). The PSM rates for pT2, pT3, and pT4 stage were 63/278 (23%), 50/89 (56%), and 20/26 (77%), respectively. The estimated rates for apical, posterolateral, basal, and multifocal PSM were 8%, 4%, 7%, and 14%, respectively. In univariate analysis, overall PSM related to tPSA, f/tPSA, percentage of positive needles, and Gleason score. Multifocal PSM correlated with smoking history, drinking history, tPSA, f/tPSA, percentage of positive needles, and Gleason score. In multivariate analysis, percentage of positive needles reminded the only independent predictor for overall (OR = 10.5, 95% CI: 2.58-44.4) and basal PSM (OR = 24.0, 95% CI: 3.22-179.4). The f/tPSA (OR = 2.59, 95% CI: 2.18-5.71) and percentage of positive needles (OR = 31.0, 95% CI: 3.17-303) were independent risk factors for multifocal PSM. CONCLUSION: The multifocal sites were the most common location of positive surgical margin, followed by apical and basal sites among Chinese patients undergoing RARP. The percentage of positive needles was an independent predictor for overall, basal, and multifocal PSM.

Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.

BACKGROUND: Machine learning has many attractive theoretic properties, specifically, the ability to handle non predefined relations. Additionally, studies have validated the clinical utility of mpMRI for the detection and localization of CSPCa (Gleason score ≥ 3 + 4). In this study, we sought to develop and compare machine-learning models incorporating mpMRI parameters with traditional logistic regression analysis for prediction of PCa (Gleason score ≥ 3 + 3) and CSPCa on initial biopsy. METHODS: A total of 688 patients with no prior prostate cancer diagnosis and tPSA ≤ 50 ng/ml, who underwent mpMRI and prostate biopsy were included between 2016 and 2020. We used four supervised machine-learning algorithms in a hypothesis-free manner to build models to predict PCa and CSPCa. The machine-learning models were compared to the logistic regression analysis using AUC, calibration plot, and decision curve analysis. RESULTS: The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) yielded similar diagnostic accuracy with logistic regression, while classification and regression tree (CART, AUC = 0.834 and 0.867) had significantly lower diagnostic accuracy than logistic regression (AUC = 0.894 and 0.917) in prediction of PCa and CSPCa (all P < 0.05). However, the CART illustrated best calibration for PCa (SSR = 0.027) and CSPCa (SSR = 0.033). The ANN, SVM, RF, and LR for PCa had higher net benefit than CART across the threshold probabilities above 5%, and the five models for CSPCa displayed similar net benefit across the threshold probabilities below 40%. The RF (53% and 57%, respectively) and SVM (52% and 55%, respectively) for PCa and CSPCa spared more unnecessary biopsies than logistic regression (35% and 47%, respectively) at 95% sensitivity for detection of CSPCa. CONCLUSION: Machine-learning models (SVM and RF) yielded similar diagnostic accuracy and net benefit, while spared more biopsies at 95% sensitivity for detection of CSPCa, compared with logistic regression. However, no method achieved desired performance. All methods should continue to be explored and used in complementary ways.

Reduced Coefficients of Linear Thermal Expansion of Colorless and Transparent Semi-Alicyclic Polyimide Films via Incorporation of Rigid-Rod Amide Moiety: Preparation and Properties.

Semi-alicyclic colorless and transparent polyimide (CPI) films usually suffer from the high linear coefficients of thermal expansion (CTEs) due to the intrinsic thermo-sensitive alicyclic segments in the polymers. A series of semi-alicyclic CPI films containing rigid-rod amide moieties were successfully prepared in the current work in order to reduce the CTEs of the CPI films while maintaining their original optical transparency and solution-processability. For this purpose, two alicyclic dianhydrides, hydrogenated pyromellitic anhydride (HPMDA, I), and hydrogenated 3,3',4,4'-biphenyltetracarboxylic dianhydride (HBPDA, II) were polymerized with two amide-bridged aromatic diamines, 2-methyl-4,4'-diaminobenzanilide (MeDABA, a) and 2-chloro-4,4'-diaminobenzanilide (ClDABA, b) respectively to afford four CPI resins. The derived CPI resins were all soluble in polar aprotic solvents, including N-methyl-2-pyrrolidone (NMP) and N,N-dimethylacetamide (DMAc). Flexible and tough CPI films were successfully prepared by casing the PI solutions onto glass substrates followed by thermally cured at elevated temperatures from 80 °C to 250 °C. The MeDABA derived PI-Ia (HPMDA-MeDABA) and PI-IIa (HBPDA-MeDABA) exhibited superior optical transparency compared to those derived from ClDABA (PI-Ib and PI-IIb). PI-Ia and PI-IIa showed the optical transmittances of 82.3% and 85.8% at the wavelength of 400 nm with a thickness around 25 µm, respectively. Introduction of rigid-rod amide moiety endowed the HPMDA-PI films good thermal stability at elevated temperatures with the CTE values of 33.4 × 10-6/K for PI-Ia and 27.7 × 10-6/K for PI-Ib in the temperature range of 50-250 °C. Comparatively, the HBPDA-PI films exhibited much higher CTE values. In addition, the HPMDA-PI films exhibited good thermal stability with the 5% weight loss temperatures (T5%) higher than 430 °C and glass transition temperatures (Tg) in the range of 349-351 °C.