Analysis of risk factors for Gleason score upgrading after radical prostatectomy in a Chinese cohort

Cognitive fusion-targeted biopsy versus transrectal ultrasonography-guided systematic biopsy: comparison and analysis of the risk of Gleason score upgrading

PURPOSE

The aim of this study is to assess the precision of the Gleason score (GS) obtained through cognitive fusion-targeted biopsy (COG-TB) in comparison to transrectal ultrasonography-guided systematic biopsy (TRUS-SB), and to identify factors that can predict Gleason score upgrading (GSU) in a cohort of Chinese patients.

METHODS

A final enrollment of 245 patients was recorded. Between 2020 and 2022, 132 patients underwent TRUS-SB, and 113 patients underwent COG-TB. The Chi-square test was performed to analyze the variation in downgrading, concordance, and upgrading between TRUS-SB and COG-TB. Multivariable analyses were performed to seek factors predicting Gleason score upgrading. Finally, a model which utilizes multivariable logistic regression was developed to predict the likelihood of GSU.

RESULTS

The concordance for TRUS-SB and COG-TB were 42.4% and 65.5%, respectively. TRUS-SB and COG-TB exhibited notable disparities in downgrading, concordance, and upgrading. Age, prostate volume, body mass index (BMI), and the biopsy modality were significant predictive factors.

CONCLUSION

COG-TB can significantly increase concordance with final histopathology. Age, prostate volume, BMI, and the biopsy modality were predictive factors of GSU.

Predictors of Gleason Grading Group Upgrading in Low-Risk Prostate Cancer Patients From Transperineal Biopsy After Radical Prostatectomy

RATIONALE AND OBJECTIVES

To investigate the predictors of Gleason Grading Group (GGG) upgrading in low-risk prostate cancer (Gleason score=3 + 3) from transperineal biopsy after radical prostatectomy (RP).

MATERIALS AND METHODS

The clinical data of 160 patients who underwent transperineal biopsy and RP from January 2017 to December 2022 were retrospectively analyzed. First, univariate and multivariate logistic regression analysis were used to obtain independent predictors of postoperative GGG upgrading. Then receiver operating characteristic curve was used to evaluate the diagnostic efficacy of predictors. Finally, Linear-by-Linear Association test was used to analyze the risk trends of patients in different predictor groups in the postoperative GGG.

RESULTS

In this study, there were 81 cases (50.6%) in the GGG concordance group and 79 cases (49.4%) in the GGG upgrading group. Univariate analysis showed age, free/total prostate-specific antigen (f/tPSA), proportion of positive biopsies, positive target of magnetic-resonance imaging (MRI) and positive target of contrast-enhanced ultrasound had significant effects on GGG upgrading (all P < .05). In multivariate logistic regression analysis, age (odds ratio [OR]=1.066, 95%CI=1.007-1.127, P = .027), f/tPSA (OR=0.001, 95%CI=0-0.146, P = .001) and positive target of MRI (OR=3.005, 95%CI=1.353-76.674, P = .007) were independent predictors. The prediction model (area under curve=0.751 P < .001) had higher predictive efficacy than all independent predictors. The proportion of patients in exposed group of different GGG increased with the level of GGG, but decreased in nonexposed group, and the linear trend was significantly different (all P < .001).

CONCLUSION

Age, f/tPSA, and positive target of MRI were independent predictors of postoperative GGG upgrading. The predictive model constructed had the best diagnostic efficacy.

Prediction model of gleason score upgrading after radical prostatectomy based on a bayesian network

OBJECTIVE

To explore the clinical value of the Gleason score upgrading (GSU) prediction model after radical prostatectomy (RP) based on a Bayesian network.

METHODS

The data of 356 patients who underwent prostate biopsy and RP in our hospital from January 2018 to May 2021 were retrospectively analysed. Fourteen risk factors, including age, body mass index (BMI), total prostate-specific antigen (tPSA), prostate volume, total prostate-specific antigen density (PSAD), the number and proportion of positive biopsy cores, PI-RADS score, clinical stage and postoperative pathological characteristics, were included in the analysis. Data were used to establish a prediction model for Gleason score elevation based on the tree augmented naive (TAN) Bayesian algorithm. Moreover, the Bayesia Lab validation function was used to calculate the importance of polymorphic Birnbaum according to the results of the posterior analysis and to obtain the importance of each risk factor.

RESULTS

In the overall cohort, 110 patients (30.89%) had GSU. Based on all of the risk factors that were included in this study, the AUC of the model was 81.06%, and the accuracy was 76.64%. The importance ranking results showed that lymphatic metastasis, the number of positive biopsy cores, ISUP stage and PI-RADS score were the top four influencing factors for GSU after RP.

CONCLUSIONS

The prediction model of GSU after RP based on a Bayesian network has high accuracy and can more accurately evaluate the Gleason score of prostate biopsy specimens and guide treatment decisions.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Influence of Active Surveillance on Gleason Score Upgrade and Prognosis in Low- and Favorable Intermediate-Risk Prostate Cancer

Few studies have focused on the link between active surveillance (AS) and Gleason score upgrade (GSU) and its impact on the prognosis of patients with prostate cancer (PCa). This study aimed to analyze the effect of AS duration on GSU and prognostic value based on risk stratification. All eligible patients were risk-stratified according to AUA guidelines into low-risk (LR), favorable intermediate-risk (FIR), and unfavorable intermediate-risk (UIR) PCa. Within the Surveillance, Epidemiology, and End Results Program (SEER) database, 28,368 LR, 27,243 FIR, and 12,210 UIR PCa patients were included. The relationship between AS duration and GSU was identified with univariate and multivariate logistic regression. Discrimination according to risk stratification of AS duration and GSU was tested by Kaplan-Meier analysis and competing risk regression models. The proportion of patients who chose AS was the highest among LR PCa (3434, 12.1%), while the proportion in UIR PCa was the lowest (887, 7.3%). The AS duration was only associated with GSU in LR PCa, with a high Gleason score (GS) at diagnosis being a strong predictor of GSU for FIR and UIR PCa. Kaplan-Meier analysis indicated that long-term surveillance only made a significant difference in prognosis in UIR PCa. The competing risk analysis indicated that once GS was upgraded to 8 or above, the prognosis in each group was significantly worse. AS is recommended for LR and FIR PCa until GS is upgraded to 8, but AS may not be suitable for some UIR PCa patients.

Analysis of risk factors for Gleason score upgrading after radical prostatectomy in a Chinese cohort

Background

To study the risk factors of Gleason score upgrading (GSU) after radical prostatectomy (RP) in a Chinese cohort.

Methods

The data of 637 patients who underwent prostate biopsy and RP in our hospital from January 2014 to January 2021 were retrospectively analyzed. The age, body mass index (BMI), prostate‐specific antigen (PSA) level, testosterone (TT) level, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), eosinophil‐to‐lymphocyte ratio (ELR), aspartate aminotransferase/alanine transaminase (AST/ALT) ratio, clinical stage, the biopsy method, and pathological characteristics of specimens after biopsy and RP were collected for all patients. Univariate analysis and multivariate logistic regression analysis were used to analyze the risk factors of GSU after RP. The predictive efficacy was verified with the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. We performed the analysis separately in the overall cohort and in the cohort with Gleason score (GS) = 6.

Results

In the overall cohort, 177 patients (27.79%) had GSU, and in the GS = 6 cohort, 68 patients (60.18%) had GSU. Multivariate logistic regression analysis showed that in the overall cohort, clinical stage ≥T2c (OR = 3.201, p < 0.001), the number of positive cores ≥3 (OR = 0.435, p = 0.04), and positive rate of biopsy (OR = 0.990, p = 0.016) can affect whether GS is upgraded, and the AUC of the combination of the three indicators for predicting the occurrence of GSU was 0.627. In the GS = 6 cohort, multivariate logistic regression analysis showed that clinical stage ≥T2c (OR = 4.690, p = 0.001) was a risk factor for GSU, and the AUC predicted to occur GSU is 0.675.

Conclusion

Clinical stage ≥T2c, the number of positive cores <3, and lower positive rate of biopsy are the risk factors of GSU. This study may provide some references for clinicians to judge the accuracy of biopsy pathological grading and formulate treatment strategies, but the specific effect still needs clinical practice certification.