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There is dependence among stations in tandem queueing systems.Deep analysis of the influence of the upward station on the downward station is important for studying of the performance of the tandem queueing systems.However,the departure process of the upward station is usually non-renewal process,which causes the dependence among stations is difficult to be analyzed theoretically.This paper adopts performance ratio to study the dependence among stations.By simulations,the relationship between performance ratio and system parameters are analyzed.It is found that the upward station could magnify or reduce the mean waiting time of the downward station.The performance ratio increases with the square variation coefficient of the service time at the upward station.When the performance ratio is greater than 1,it increases with the ratio of the mean service time of the upward station to the downward station.When the performance ratio is less than 1,it decreases with the ratio of the mean service time of the upward station to the downward station.The mean waiting time of the downward station could be changed by adjusting mean or square variation coefficient of the service time at the upward station.

Objective: We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa). Methods: A total of 165 patients with de novo oligometastatic PCa were included in the study (training cohort, n = 115; validating cohort, n = 50). Among them, MRI scans were conducted and T2‐weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences were collected for radiomics features along with their clinicopathological features. Radiological features were extracted from T2WI and ADC sequences for prostate tumors. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) combined with 10‐fold cross‐validation were used to select the optimal features on each sequence. Then, a weighted radiomics score (Rad‐score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). Results: Eastern Cooperative Oncology Group (ECOG) score, absolute neutrophil count (ANC) and Rad‐score were included in the nomogram as independent risk factors for OS in de novo oligometastatic PCa patients. We found that the areas under the curves (AUCs) in the training cohort were 0.734, 0.851, and 0.773 for predicting OS at 1, 2, and 3 years, respectively. In the validating cohort, the AUCs were 0.703, 0.799, and 0.833 for predicting OS at 1, 2, and 3 years, respectively. Furthermore, the clinical relevance of the predictive nomogram was confirmed through the analysis of DCA and calibration curve analysis. Conclusion: The MRI‐based nomogram incorporating Rad‐score and clinical data was developed to guide the OS assessment of oligometastatic PCa. This helps in understanding the prognosis and improves the shared decision‐making process. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To assess the predictive value of MRI-based radiomics of periprostatic fat (PPF) and tumor lesions for predicting Gleason score (GS) upgrading from biopsy to radical prostatectomy (RP) in prostate cancer (PCa)., Methods: A total of 314 patients with pathologically confirmed prostate cancer (PCa) after radical prostatectomy (RP) were included in the study. The patients were randomly assigned to the training cohort (n = 157) and the validating cohort (n = 157) in a 1:1 ratio. All had pre-surgery MRI followed by transrectal ultrasound-guided prostate biopsy. Radiological features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences for PPF and tumors. Univariate and multivariate logistic regression identified independent clinical risk factors, and a combined model was established by integrating radiomic features of PPF and PCa. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis., Results: The combined model, incorporating radiomic features of PPF, PCa, and clinical data, predicted GS upgrading from biopsy to RP excellently (AUC=0.925, 95%CI0.872-0.979) in the training cohort. The Hosmer-Lemeshow test confirmed model fit (χ 2 = 9.316, P = 0.316). The nomogram was validated in the validating cohort; it showed good accuracy (AUC= 0.937, 95% CI, 0.891-0.983) and was well calibrated (χ 2 = 12.871, P = 0.116). Decision curve analysis indicated good clinical utility of the radiomic nomogram., Conclusion: The combined model incorporating PPF, PCa, and clinical data showed excellent performance in predicting GS upgrading from biopsy to RP in PCa patients. This offers a novel and reliable noninvasive tool for GS upgrading risk stratification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)