Purpose: To construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ). Methods: A prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model. Results: A total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy. Conclusion: The prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

Background: Prostate cancer (PCa) is the second most common cancer among men worldwide. Perineural invasion (PNI) was a prominent characteristic of PCa, which was recognized as a key factor in promoting PCa progression. As a complex and heterogeneous disease, its true condition is difficult to explain thoroughly with conventional bulk RNA sequencing. Thus, an improved understanding of PNI-PCa progression at the single-cell level is needed.Methods: In this study, we performed scRNAseq on tumor tissues of three PNI-PCa patients. Principal component analysis (PCA) and Uniform manifold approximation and projection (UMAP) were used to reduce dimensionality and visualize the cellular composition of tumor tissues. The differently expressed genes among each cluster were identified by EdgeR. GO enrichment analysis was used to understand the roles of genes within the clusters. Pseudotime cell trajectory was used to reveal the molecular pathways underlying cell fate decisions and identify genes whose expression changed as the cells underwent transition. We applied CellPhoneDB to identify cell-cell interactions among the epithelial and neural cells in PNI-PCa.Results: Analysis of the ∼17,000 single-cell transcriptomes in three PNI prostate cancer tissues, we identified 12 major cell clusters, including neural cells and two epithelial subtypes with different expression profiles. We found that basal/intermediate epithelial cell subtypes highly expressed PCa progression-related genes, including PIGR, MMP7, and AGR2. Pseudotime trajectory analysis showed that luminal epithelial cells could be the initiating cells and transition to based/intermediate cells. Gene ontology (GO) enrichment analysis showed that pathways related to cancer progressions, such as lipid catabolic and fatty acid metabolic processes, were significantly enriched in basal/intermediate cells. Our analysis also suggested that basal/intermediate cells communicate closely with neural cells played a potential role in PNI-PCa progression.Conclusion: These results provide our understanding of PNI-PCa cellular heterogeneity and characterize the potential role of basal/intermediate cells in the PNI-PCa progression.

Introduction: Retroperitoneoscopic nephroureterectomy with open bladder cuff excision (TRNU) is the traditional way to manage upper urinary tract urothelial carcinoma (UTUC). Whereas, the patient has to reposition from lateral position to supine position, resterilize, and be with a large incision in the lower abdomen, which is time consuming and pain-full. Here, we describe a new surgical procedure, single-position complete retroperitoneoscopic radical nephroureterectomy (SCRNU). Methods: We evaluated 86 patients diagnosed with UTUC at our hospital from June 2013 to June 2021. The patients underwent TRNU (n=28) or SCRNU (n=58). Demographic, peroperative, and follow-up data were collected and compared retrospectively. Results: All 86 patients underwent successful procedures of radical nephroureterectomy, without transferring into open surgery. The average follow-up time was 45.4 months for the SCRNU group and 39 months for the TRNU group. All follow-up patients were alive without bladder incision tumor. Further, The follow-up results showed that there was no significant difference in the recurrence rate of bladder tumor between the two methods.The SCRNU group was superior to the TRNU group in all the evaluation parameters, including operation time, perioperative complications, postoperative pain recurrence rate and treatment fee. Conclusion: Our modified technique SCRNU is more minimally invasive, simplified, cost-effective and with low recurrence rates for managing UTUC.

Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets. Results: The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses. Conclusion: The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.