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Background and objective: Although radical cystectomy (RC) with pelvic lymph node dissection (PLND) is the standard treatment of muscle invasive bladder cancer, it may cause lymphatic leakage. Recent studies describe lymphatic embolization (LE) as an option to manage post-operative lymphatic leakage. Hence, this study evaluated the outcome of LE in patients receiving RC and analyzed factors associated with outcomes. Methods: This was a retrospective analysis of patients who underwent LE after RC for bladder cancer between August 2017 and June 2023. The data was assessed for analysis at January 2024. The patients were divided into a clinical success group and a clinical failure group. Clinical failure was defined as the following: 1) those who required drainage catheter placement >7 days after LE, 2) those who needed re-intervention before catheter removal, and 3) those who experienced adverse events associated with LE. Logistic regression analysis was performed to identify the factors associated with outcomes of LE. Key findings and limitations: We analyzed 45 patients who underwent LE after RC. Twenty-eight (62.2%) patients were identified as clinically successful. Four patients required re-embolization, but none required more than two sessions of intervention. Three patients experienced lymphatic complications after LE. In multivariable analysis, maximal daily drainage volume of >1,000 mL/day (odds ratio [OR] = 4.729, 95% confidence interval [CI]: 1.018–21.974, p = 0.047) and diabetes mellitus (DM) (OR = 4.571, 95% CI: 1.128–18.510, p = 0.033) were factors associated with LE outcome. Conclusions and clinical implications: Our results suggest LE as a potentially effective procedure for controlling post-operative lymphatic leaks after RC, with few minor side effects. Patients exceeding a daily drainage of 1,000mL/day or with a medical history of DM have a higher risk for re-intervention and clinical failure after LE. [ABSTRACT FROM AUTHOR]

Background: There have been conflicting outcomes regarding the use of lidocaine to reduce pain after uterine artery embolization (UAE). Purpose: To investigate the efficacy of intra-arterial lidocaine injection for pain and inflammatory response control within 24 h of UAE for symptomatic uterine fibroids. Material and Methods: Of 1530 patients who underwent UAE for uterine fibroids in 2007–2021, 5 mL of 1% lidocaine was injected into each uterine artery immediately after UAE in 23 patients. A disease-matched control group (n = 23) who did not receive intra-arterial lidocaine was generated from the same registry. The pain score, white blood cell (WBC) count, C-reactive protein (CRP), neutrophil/lymphocyte ratio (NLR), and fentanyl consumption were compared before and after UAE. Complete infarction of the dominant fibroid was assessed using magnetic resonance imaging. Results: Significantly lower WBC count, CRP level, and NLR were noted 24 h after UAE in the lidocaine group. No statistically significant difference was noted in the pain score between groups at 0–24 h. The cumulative fentanyl dose administered during the first 24 h after UAE was not significantly different. After embolization, fibroid-related symptoms resolved in all patients. No significant difference was observed in the rate of complete infarction of the dominant fibroid. Conclusion: Lidocaine administration immediately after UAE resulted in a significant reduction in the inflammatory response. However, such a difference in the inflammatory reaction did not contribute to significant reductions in pain scores or fentanyl consumption. [ABSTRACT FROM AUTHOR]

Background A deep convolutional neural network (DCNN) model that predicts the degree of arteriovenous fistula (AVF) stenosis and 6-month primary patency (PP) based on AVF shunt sounds was developed, and was compared with various machine learning (ML) models trained on patients' clinical data. Methods Forty dysfunctional AVF patients were recruited prospectively, and AVF shunt sounds were recorded before and after percutaneous transluminal angioplasty using a wireless stethoscope. The audio files were converted to melspectrograms to predict the degree of AVF stenosis and 6-month PP. The diagnostic performance of the melspectrogram-based DCNN model (ResNet50) was compared with that of other ML models [i.e. logistic regression (LR), decision tree (DT) and support vector machine (SVM)], as well as the DCNN model (ResNet50) trained on patients' clinical data. Results Melspectrograms qualitatively reflected the degree of AVF stenosis by exhibiting a greater amplitude at mid-to-high frequency in the systolic phase with a more severe degree of stenosis, corresponding to a high-pitched bruit. The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis. In predicting the 6-month PP, the area under the receiver operating characteristic curve of the melspectrogram-based DCNN model (ResNet50) (≥0.870) outperformed that of various ML models based on clinical data (LR, 0.783; DT, 0.766; SVM, 0.733) and that of the spiral-matrix DCNN model (0.828). Conclusion The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis and outperformed ML-based clinical models in predicting 6-month PP. [ABSTRACT FROM AUTHOR]