Background: The study aimed to find a potential long non-coding RNA (lncRNA) model related to survival in bladder cancer by analyzing data in The Cancer Genome Atlas (TCGA). Methods: We downloaded the gene expression data from the TCGA and analyzed the differentially expressed lncRNAs (DELs) between tumor and normal tissues. Patients were divided into training and testing groups, and a prognostic risk score model with lncRNAs was constructed by using data in the training group using multivariate Cox and lasso regression analysis. We divided patients into high-and low-risk groups according to the median value in the lncRNA signature model. Survival and receiver operating characteristic (ROC) curves were visualized in both groups. Further, we validated the model in the testing group. Results: We screened 169 DELs for bladder cancer. The univariate Cox regression analysis showed that 13 lncRNAs were associated with prognosis with a p-value <0.01. We selected 12 of these lncRNAs to perform a multivariate Cox analysis to build the lncRNA signature. The formula with nine lncRNAs, namely, MIR497HG, LINC00968, NALCN-AS1, LINC02321, RNF144A-AS1, MNX1-AS1, FLJ22447, LINC01956, FLJ42969, was significantly related to prognosis. Patients in the high-risk group had a lower survival rate compared with the low‐risk group in the training and testing sets (both p-values < 0.05) and the area of the ROC curve was 0.737 and 0.68, respectively. Conclusions: The study illustrated a significant lncRNA signature and indicated the risk score Cox model could be an important biomarker to predict the prognosis of bladder cancer.

Background: Bipolar transurethral enucleation of the prostate (B-TUEP) is a well-established surgical treatment for benign prostatic hyperplasia (BPH); however, its efficacy may vary depending on patient characteristics. Magnetic resonance imaging (MRI) with radiomics analysis can offer comprehensive and quantitative information about prostate characteristics that may relate to surgical outcomes. This study aimed to explore the value of MRI and radiomics analysis in predicting the short-term efficacy of B-TUEP for BPH. Materials and methods: A total of 137 patients with BPH who underwent B-TUEP at 2 institutions were included. Radiological features were measured in the MRIs, and the radiomics score was developed from 1702 radiomics features extracted from the prostate and transitional zone regions of interest. Three prediction models were developed and validated based on clinical-radiological features, radiomic features, and their combinations. The models were evaluated using the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: The combination model exhibited the highest area under curve in both the training set (0.838) and the external validation set (0.802), indicating superior predictive performance and robustness. Furthermore, the combination model demonstrated good calibration (p > 0.05) and optimal clinical utility. The combination model indicated that a higher maximum urine flow rate, lower transitional zone index, and higher radiomics score were associated with an increased risk of poor efficacy. Conclusions: Magnetic resonance imaging with radiomic analysis can offer valuable insights for predicting the short-term efficacy of B-TUEP in patients with BPH. A combination model based on clinical and radiomics features can assist urologists in making more precise clinical decisions.