Establishment and Validation of a Machine Learning Model Predicting Post-Radical Prostatectomy Gleason grading group upgrading Author’s information

Establishment and Validation of a Machine Learning Model Predicting Post-Radical Prostatectomy Gleason grading group upgrading Author’s information | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Establishment and Validation of a Machine Learning Model Predicting Post-Radical Prostatectomy Gleason grading group upgrading Author’s information Jinfeng Wu, Runqiang Yuan, Yangbai Lu, Jian Tan, Zhenjie Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4959347/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Based on the 2014 International Society of Urological Pathology (ISUP) grading system, the study assesses the disparities in gleason grading group between preoperative needle biopsy pathology and post-radical prostatectomy (post-RP) specimens for prostate cancer (PCa). It investigates the risk factors for post-RP gleason grading group upgrading (GGU) and develops and validates a machine learning (ML) model for predicting post-RP GGU in PCa patients. Methods A retrospective analysis is conducted on demographic and clinicopathological variables of PCa patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018. Five different ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), neural network (NNET), random forest (RF), and XGBoost (XGB), are utilized. The patients with localized PCa who underwent radical prostatectomy (RP) at Zhongshan People's Hospital from January 2018 to December 2023 were selected as the external validation group. Model performance is evaluated using receiver operating characteristic (ROC) area under the curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity (recall), and specificity. A web-based predictor is developed based on the best-performing model. Results This study included a total of 65,574 PCa patients from the SEER database and 98 patients from the external validation group. Among them, there were 11,931 in the training group, 5,112 in the internal validation group, and 24 in the external validation group who experienced post-RP GGU. Risk factors such as patient age, race, preoperative prostate-specific antigen (PSA) level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores were significantly associated with GGU (P < 0.05). Five ML algorithms demonstrated relatively stable consistency, with their AUC values exceeding 0.7. A web-based predictor was developed using the XGB model, which showed the best predictive performance. Conclusion The study introduced a ML model and an online predictor designed to assess the risk of post-RP GGU in PCa patients, aiding physicians in customizing clinical decisions and treatment strategies. machine learning post-radical prostatectomy gleason grading group upgrading risk prediction prostate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Prostate cancer (PCa) is the second most common cancer globally and ranks as the fifth leading cause of cancer-related deaths in men as of 2022, with an estimated 1.5 million new cases and 397,000 deaths worldwide. Variations in PCa diagnostic practices among countries are key factors contributing to the significant differences in PCa incidence worldwide ( 1 ). Currently, the diagnostic process for PCa heavily relies on the histological examination of biopsy tissues. The PCa grading and staging system derived from biopsies serves as a primary reference for assessing patient conditions and formulating treatment plans. Ideally, there should be complete consistency between the pathological grading from preoperative biopsies and the final pathological grading reported post-radical prostatectomy (post-RP). However, due to the multifocality and high intralesional heterogeneity of PCa, combined with potential grading errors by pathologists and sampling inaccuracies, biopsy pathology often underestimates the disease compared to post-RP pathology ( 2 , 3 ). Studies based on the PCa grading and staging system proposed by the International Society of Urological Pathology (ISUP) in 2014 have shown a high rate of inconsistency between post-RP and preoperative biopsy pathological grading, with Gleason grading group upgrading (GGU) being the most common occurrence( 4 – 7 ). Additionally, GGU has been significantly associated with post-RP biochemical recurrence and a higher likelihood of surgical margin positivity in radical specimens ( 8 – 11 ). Underestimating PCa biopsy pathological grading may lead to an overly conservative selection of initial treatment regimens for prostate cancer, while overestimating patient prognosis, posing significant challenges to clinical treatment. With advancements in statistical methods and artificial intelligence, predictive models are increasingly utilized in medical diagnostics. Predicting pathological upgrades based on preoperative and biopsy-known variables could be of crucial significance for surgeons when formulating surgical plans and determining postoperative treatment strategies for patients. Scientists have been striving to explore various methods to assess the risk of post-RP GGU more accurately. However, owing to the complexity of medical data and the intricate interplay among various factors, there are significant differences in the calculation methods of the models. Machine learning (ML) is a promising cross-disciplinary method capable of accurately predicting outcomes from multiple unrelated datasets that would otherwise be discrete and challenging to correlate ( 12 ). With the rapid development of evidence-based medicine, large and complex medical datasets require more advanced techniques for interpretation, and ML has become a promising choice for the diagnosis and prognostic prediction of many diseases. Previous approaches often relied on the construction of predictive nomograms or analyses based on multiple unfavorable factors ( 13 – 15 ). However, nomogram models are limited to independent predictive variables and risk overfitting when numerous predictors are incorporated, potentially leading to model failure. Consequently, there has been a shift towards more robust predictive classifiers that use ML methods. By processing and analyzing large, heterogeneous, and complex clinical data, practical clinical problems can be addressed, thereby making diagnostic and treatment processes more efficient ( 16 – 18 ). Relevant research has also emerged in the field of urological treatments, for instance, ML algorithms that utilize pathological data have been pivotal in developing and validating automated pathological models for diagnosing and predicting outcomes in patients with bladder cancer or clear-cell renal cell carcinoma ( 19 , 20 ). Similarly, studies on PCa have employed ML models to predict bone metastasis in patients with PCa or lymph node metastasis in intermediate-to-high-risk PCa patients, aiming for precise diagnosis and treatment guidance ( 21 , 22 ). Presently, investigations into the risk factors of post-RP GGU are constrained by small sample sizes, single-center methodologies, and predictive models lacking external data validation. In this study, we aimed to identify the risk factors of post-RP GGU, and develop and validate a new ML model to predict post-RP GGU in PCa patients from the Surveillance, Epidemiology, and End Results (SEER) database as training and internal validation groups and PCa patients from Zhongshan People's Hospital as external validation groups using five ML methods. Methods Data Collection Data from patients diagnosed with PCa from 2010 to 2018 were retrieved using SEER*Stat software (version 8.4.0.1). The International Classification of Diseases for Oncology, 3rd edition, code 8140/3, was used for data filtering. The inclusion criteria were primary PCa, age ≥ 18 years, and complete clinical information. The exclusion criteria were preoperative neoadjuvant systemic therapy or other local treatments and incomplete clinical information. The SEER database is publicly accessible and anonymizes all patient information, hence exempt from ethical review. The external validation data from this retrospective clinical study waived the requirement for informed consent from the participants, with all procedures conducted in accordance with the Helsinki Declaration. Inclusion of Analytical Variables Eight variables potentially affecting GGU in patients with PCa were included: age, race, marital status, preoperative prostate-specific antigen (PSA) level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores. Statistical Methods Data were processed using R Studio (version 4.2.3). Normally distributed continuous data were expressed as mean ± standard deviation (x ± s), while non-normally distributed continuous data were presented as median (25th percentile, 75th percentile). Count data were presented as frequency and percentage (%). Intergroup comparisons were conducted using the chi-square test or Fisher's exact test. To determine the variables included in the ML model, a univariate logistic regression analysis of GGU risk factors was first performed, followed by a multivariate logistic regression analysis to identify independent predictive factors associated with GGU, considering P-values < 0.05 as significant. Machine Learning Classifiers and Model Building and Evaluation Five ML and decision tree models were constructed based on selected independent risk factors: logistic regression (LR), gradient boosting machine (GBM), neural network (NNET), random forest (RF), and XGBoost (XGB). Initially, LR, one of the most fundamental classifiers, was used to predict the likelihood of a patient achieving a specific outcome based on relevant information or clinical features( 23 ). Next, the GBM method was applied. This ensemble technique constructs multiple decision trees sequentially, each aiming to correct the errors of its predecessors, thus effectively managing complex data and nonlinear relationships ( 24 ). NNET was also employed, characterized by its structure of multiple layers of neurons that perform nonlinear transformations, making it particularly adept at capturing intricate patterns and processing large datasets ( 25 ). Furthermore, the RF algorithm was implemented to build numerous decision trees using bootstrap samples of the training data and random subsets of features for each split. It combines the outputs of these trees through majority voting to improve prediction accuracy and prevent overfitting ( 26 ). Finally, XGB was used, a highly efficient and scalable tree-boosting system that builds robust models even with relatively small sample sizes by creating new models to predict the residuals of previous models and aggregating these for the final prediction ( 27 ). The SEER dataset was divided into a 7:3 ratio for the training and internal validation groups, with external validation serving as a separate group. Each model underwent 10-fold cross-validation during training to ensure stability, and the best hyperparameters were selected using a random search method. Receiver operating characteristic (ROC) curves were constructed for both groups. Differences in predictive performance among the ML models were assessed using the area under the ROC curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity, and specificity, with the best-performing model selected as the final model after a comprehensive comparison. Web-based Predictor Finally, the accuracy and generalizability of the best predictive model were validated using an independent external validation group. This step involved evaluating the model's performance on a separate dataset not used in the training and internal validation groups. The model's ability to accurately predict outcomes and maintain performance across different datasets and conditions was assessed, ensuring that it was not overfitted to the training data and could generalize well to new, unseen data. To enhance the usability and presentation of the data, a web-based predictor was developed using the Shiny package in R. Shiny is a powerful tool that allows the creation of interactive web applications directly from R software. Results Baseline Characteristics and Postoperative Pathological Upgrading This study included a total of 65,574 PCa patients from the SEER database. They were randomly divided into training (45,903 cases) and internal validation (19,671 cases) groups at a ratio of 7:3. Additionally, 130 patients with PCa who underwent RP at Zhongshan People's Hospital between 2018 and 2023 were included. After excluding 32 patients who did not meet the inclusion criteria, 98 patients were included in the external validation study. The process is illustrated in Fig. 1 . In Table 1 , the patient characteristics for the training, internal validation, and external validation groups were presented. Among these, 11,931 patients (25.9%) in the training group, 5,112 (25.9%) in the internal validation group, and 24 (24.4%) in the external validation group experienced post-RP GGU. Table 1 Clinical characteristics of patients. Characteristics Training group(n = 45,903) Internal validation group (n = 19,671) External validation group (n = 98) Age (%) =70 40,320 (87.8) 17,226 (87.6) 55 (56.1) Race (%) White 35,983 (78.4) 15,462 (78.6) - Black 7,075 (15.4) 2,999 (15.2) - Asian or Pacific 2,669 (5.8) 1,136 (5.8) 98 (100.0) American or Alaska 176 (0.4) 74 (0.4) - Marital (%) Married 36,483 (79.5) 15,730 (80.0) 96 (98.0) Single 4,967 (10.8) 2,085 (10.6) - Divorced or Separated 3,462 (7.5) 1,454 (7.4) 2 (2.0) Other 991 (2.2) 402 (2.0) - Gleason Patterns Clinical (%) Group1 13,245 (28.9) 5,679 (28.9) 10 (10.2) Group2 16,208 (35.3) 6,902 (35.1) 18 (18.4) Group3 7,827 (17.1) 3,389 (17.2) 20 (20.4) Group4 5,268 (11.5) 2,280 (11.6) 30 (30.6) Group5 3,355 (7.3) 1,421 (7.2) 20 (20.4) Gleason Patterns Pathological (%) Group1 8,194 (17.9) 3,503 (17.8) 5 (5.1) Group2 21,745 (47.4) 9,359 (47.6) 29 (29.6) Group3 9,489 (20.7) 4,029 (20.5) 24 (24.5) Group4 2,535 (5.5) 1,139 (5.8) 15 (15.3) Group5 3,940 (8.6) 1,641 (8.3) 25 (25.5) PSA (%) =20 2,905 (6.3) 1,272 (6.5) 39 (39.8) Positive (%) =10 5,014 (10.9) 2,153 (10.9) 20 (20.4) Examined = >=10 (%) 41,310 (90.0) 17,701 (90.0) 98 (100.0) Percent = >=33% (%) 27,789 (60.5) 11,819 (60.1) 52 (53.1) Univariable and Multivariable Logistic Regression Analysis Subsequently, univariate logistic regression analysis revealed significant associations (P < 0.05) between GGU and variables such as age, race, marital status, preoperative PSA level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores among PCa patients. Multivariate logistic regression analysis was conducted on variables with P-values < 0.05. The results indicated that, except for marital status, all other variables were independent prognostic risk factors for GGU in patients with PCa ( Table 2 ). Additionally, increasing age and preoperative PSA levels were associated with an increased likelihood of pathological upgrading, especially when age was ≥ 70 years and preoperative PSA was ≥ 20 ng/mL (OR 1.85; P < 0.001 and OR 2.70; P < 0.001/ng/mL). Table 2 Univariate Analysis and Multivariate Logistic Regression Analysis of Variables Characteristics Univariate Multivariate OR (95% CI ) P -Value OR (95% CI ) P -Value Age < 60 Reference Reference 60 ~ 69 1.20(1.06–1.35) 0.004 1.41(1.24–1.61) =70 1.12(1.02–1.23) 0.016 1.85(1.67–2.05) < 0.001 Race White Reference Reference Black 0.93(0.88–0.99) 0.015 0.88(0.83–0.94) < 0.001 Asian or Pacific 1.06(0.96–1.16) 0.237 1.13(1.02–1.25) 0.017 American or Alaska 1.05(0.74–1.50) 0.774 0.88(0.59–1.29) 0.499 Marital Married Reference Reference Single 0.97(0.91–1.04) 0.397 0.98(0.91–1.05) 0.523 Divorced Separated 0.91(0.84–0.99) 0.031 0.93(0.85–1.01) 0.097 Other 0.91(0.79–1.06) 0.229 0.98(0.83–1.15) 0.805 Gleason Patterns Clinical group1 Reference Reference group2 0.24(0.22–0.25) < 0.001 0.20(0.18–0.21) < 0.001 group3 0.14(0.13–0.16) < 0.001 0.11(0.10–0.12) < 0.001 group4 0.21(0.20–0.23) < 0.001 0.15(0.14–0.16) < 0.001 PSA < 4 Reference Reference 4 ~ 10 1.08(1.00-1.15) 0.037 1.30(1.20–1.40) < 0.001 11 ~ 19 1.33(1.22–1.46) < 0.001 2.09(1.89–2.30) =20 1.50(1.34–1.67) < 0.001 2.70(2.40–3.04) < 0.001 Positive < 5 Reference Reference 5 ~ 9 0.85(0.81–0.89) =10 0.93(0.86-1.00) 0.042 1.25(1.13–1.38) < 0.001 Examined =10 0.83(0.78–0.89) < 0.001 0.90(0.83–0.98) 0.013 Percent =33% 0.92(0.88–0.96) < 0.001 1.25(1.16–1.34) < 0.001 Comparison and Validation of Model Performance Five supervised ML algorithms were employed to predict post-RP pathological GGU status, establishing binary models where labels were set as upgraded or otherwise. Surprisingly, all our ML models demonstrated relatively stable consistency, including LR, GBM, NNET, RF, and XGB, with their AUC values exceeding 0.7, namely 0.722, 0.726, 0.727, 0.703, and 0.728, respectively, as depicted in Fig. 2A. Despite minor differences in performance, the XGB model exhibited superior and stable results across both the training and internal validation groups. In the training group, the sensitivity, specificity, and AUC were 0.614, 0.760, and 0.728, respectively. Similarly, in the internal validation group, the XGB model maintained consistent performance with an AUC of 0.741, a sensitivity of 0.622, and a specificity of 0.767 ( Table 3 ) . Table 3 Predictive performance of each model. Training group Internal validation group Sensitivity Specificity AUC Sensitivity Specificity AUC LR 0.594 0.774 0.722 0.603 0.781 0.736 GBM 0.627 0.746 0.726 0.635 0.752 0.739 NNET 0.617 0.755 0.727 0.626 0.763 0.741 RF 0.573 0.790 0.703 0.577 0.791 0.712 XGB 0.614 0.760 0.728 0.622 0.767 0.741 Calibration curves and DCA were constructed for the five models used in our study (Figs. 2B, C, E, F). In our ML models, except for the RF model which showed a more pronounced deviation from the 45-degree line, the calibration curves fit well. In our DCA, the y-axis represents the net benefit, used to assess whether any specific clinical decision was more beneficial or harmful. Each point on the x-axis represents the threshold probability for distinguishing between patients with and without GGU. The results indicated that all models achieved a net clinical benefit compared to the all-or-none strategy, with the XGB model seemingly having the highest net benefit across the entire range of threshold probabilities, especially when the risk threshold was below 80%. Furthermore, we compared the accuracy of the ML models with that of the nomograms. The ROC curves of the training and internal validation groups showed that the accuracy of the XGB model was higher than that of the nomograms (training group AUC: 0.728 vs. 0.722; internal validation group AUC: 0.741 vs. 0.736), as shown in Fig. 3. Subsequently, we conducted an external validation of the XGB model using an external validation group and constructed the ROC, DCA, and calibration curves. DCA indicated that the XGB model still had a higher net benefit at risk thresholds of 70–90% (Fig. 4). Development of a Web-Based Predictor for GGU in Prostate Cancer Patients Finally, based on the XGB model, we developed a web-based predictor of GGU in PCa patients, providing a practical tool for clinicians to assess the risk of post-RP GGU. This predictor enhances clinical decision-making by allowing clinical physicians to quickly and accurately estimate a patient's likelihood of GGU, facilitating personalized treatment planning and potentially reducing the need for unnecessary interventions. By integrating easily accessible clinical data, this web-based predictor can be seamlessly incorporated into routine clinical practice, making it a valuable resource for improving patient outcomes and optimizing resource allocation in PCa management (Fig. 5) ( https://alao-riskmodel.shinyapps.io/workrun7/ ). Discussion In recent years, significant efforts have been made to refine biopsy techniques to improve detection rates and achieve greater consistency with post-RP pathology results. For example, Jiang X et al.( 28 ) introduced regional saturation biopsy, enhancing the detection of clinically significant PCa and accurately identifying index lesions. Additionally, research by Qu L et al.( 29 ) found that transperineal biopsy offers more accurate gleason grading than transrectal biopsy in patients without prior satisfactory magnetic resonance imaging (MRI). Furthermore, image fusion techniques have further increased detection rates, with targeted biopsy combined with ultrasound and multiparametric MRI reducing post-RP GGU compared to systematic biopsy. ( 30 – 32 ). Despite these advances, post-RP GGU remains a common issue, influencing decisions between active surveillance (AS) and aggressive treatment, highlighting the need for early screening models for high-risk PCa patients. This study utilized the extensive SEER database and ML algorithms to establish a predictive model for identifying PCa patients at risk of post-RP GGU before making treatment decisions. ML offers several advantages, including the ability to handle large and complex datasets, uncover hidden patterns and relationships within the data, and continuously improve predictive accuracy through learning and optimization. These capabilities enable more personalized and precise risk assessments, leading to better-informed clinical decisions. Multifactorial LR analysis identified age, race, preoperative PSA level, biopsy ISUP grade, total number of biopsy cores, number of positive biopsy cores, and the percentage of positive biopsy cores as independent risk factors for post-RP GGU. Results indicated that PCa patients aged ≥ 70 years, with preoperative PSA levels ≥ 20 ng/mL, and over 10 positive biopsy cores were at higher risk of post-RP GGU. Additionally, consistent with previous findings ( 33 , 34 ), the most common GGU occurred in ISUP GG1 and GG2, with the majority upgraded to GG2, followed by GG3. Notably, these cohorts are often considered for AS, suggesting a risk of missing curative opportunities if adverse pathology is discovered after biopsy. Approximately 30% of patients experience tumor progression during AS, and studies have shown that a significant portion of initially low-risk patients are upgraded to RP, indicating that AS may not suit those at risk of upgrading ( 35 ). Thus, identifying the risk factors associated with GGU is crucial to avoid undertreatment. In this study, five supervised ML algorithms were employed to predict the GGU from biopsy to RP. XGBoost exhibited the best discriminative ability among all cohorts. Comparing our findings with those of other studies investigating post-RP upgrading and downgrading, many of which utilized small historical cohorts, our ML-based models surpassed those of Athanazio et al. ( 36 ), based on a cohort of 2529 PCa patients undergoing RP treatment, where their study yielded an AUC value of 0.699. Additionally, compared to Zhuang et al. ( 37 ), based on a cohort of 515 individuals who established an ML model predicting pathological consistency using combined systematic and MRI-targeted prostate biopsy, whose best-performing model was XGBoost, yielding an AUC value of 0.71, our models, except for RF, were markedly superior. The strengths of this study lie in its large cohort and the utilization of nationally registered data. Additionally, multiple models were positively compared to the nomogram model, with XGB demonstrating superior discriminative ability in both the training and internal validation groups. The ML results are more reliable, enhancing the robustness of the outcomes, and the web-based predictor makes clinical application more convenient. However, this study had several limitations that warrant further investigation. Firstly, the overall population of our study was drawn from the SEER database and external validation only used a small sample size from a single center. Secondly, the SEER database provides only partial variables for patients with PCa and lacks crucial data such as prostate volume, PSA density, PI-RADS scores, post-RP surgical margin status, and testosterone levels, thereby restricting the inclusion of various potential risk factors impacting the GGU of patients with PCa. Integrating additional factors could notably augment model performance. Thirdly, the intrinsic opacity of ML algorithms may impede model interpretability. Despite these limitations, we assert the substantive robustness of our primary study findings. In conclusion, we developed a predictive model for appraising post-RP GGU risk in PCa patients, leveraging the XGB algorithm, and developing a web-based predictor. Individuals at high risk for post-RP GGU are recommended for further detailed screening based on web predictor factors. This may aid clinicians in tailoring personalized treatments for patients with PCa. Abbreviations AS active surveillance AUC area under curve DCA decision curve analysis GBM gradient boosting machine GGU gleason grade group upgrading ISUP International Society of Urological Pathology LR logistic regression ML machine learning MRI magnetic resonance imaging NNET neural network post-RP post-radical prostatectomy PCa prostate cancer PSA prostate-specific antigen RF random forest ROC receiver operating characteristic RP radical prostatectomy SSER Surveillance, Epidemiology, and End Results XGB XGBoost Declarations Competing interests All authors declare that there are no financial, personal, or other conflicts of interest in this study. The design, implementation, data analysis, and interpretation of the results in this research are entirely based on scientific principles, ensuring the fairness and objectivity of the research process and conclusions. Ethics approval and consent to participate The SEER database is an open and publicly accessible resource, thus not requiring approval or informed consent from an agency review committee. This study, which is a retrospective analysis of single-center data, does not involve patient-specific information and therefore does not require ethics committee approval. Funding This work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515220032), Guangdong Medical Science and Technology Research Foundation(B2023195), Science and Technology Project of Zhongshan City (2020B1073), Zhongshan city people's hospital Major Project of Scientific Research Foundation (BG20228249) and Zhongshan City People's Hospital Outstanding Youth Project (SG2023106). Author Contribution JW and JT conceived and designed the study. Administrative support was provided by HH, YZ, RY, and YL. Data collection and assembly were carried out by JW, JT, and ZC. Data analysis and interpretation were performed by YZ, XL, QL, JW, and JT. All authors contributed to the article and approved the submitted version. Acknowledgements The authors would like to thank the medical team at Zhongshan People's Hospital for their support and assistance during this study. Data Availability The study's data can be accessed from the repository and downloaded using the following link (https://seer.cancer.gov). Additional information can be provided upon reasonable request. References ME JF, Siegel RL, Isabelle Soerjomataram M, Ahmedin Jemal D. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 2024. Epstein JI, Feng Z, Trock BJ, Pierorazio PM. 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MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. N Engl J Med. 2020;382(10):917–28. Nassiri N, Beeder L, Nazemi A, Asanad K, Um J, Gill I, et al. Step-by-Step: Fusion-guided prostate biopsy in the diagnosis and surveillance of prostate cancer. Int Brazilian J Urology: official J Brazilian Soc Urol. 2019;45(6):1277. Andras I, Cata ED, Serban A, Kadula P, Telecan T, Buzoianu M, et al. Combined systematic and MRI-US fusion prostate biopsy has the highest grading accuracy when compared to final pathology. Medicina. 2021;57(6):519. Demirtaş A, Sönmez G, Tombul ŞT, Demirtaş T, Akgün H. Comparison of the upgrading rates of International Society of Urological Pathology grades and tumor laterality in patients undergoing standard 12-core prostate biopsy versus fusion prostate biopsy for prostate cancer. Urol Int. 2019;103(3):256–61. Li X, Wang Z-X, Zhu Y-P, Wang J, Yin Y-S, Zeng X-Y. Clinicopathological factors associated with pathological upgrading from biopsy to prostatectomy in patients with ISUP grade group ≤ 2 prostate cancer. Asian J Androl. 2022;24(5):487–93. Kulkarni GS, Lockwood G, Evans A, Toi A, Trachtenberg J, Jewett MA, et al. Clinical predictors of Gleason score upgrading: implications for patients considering watchful waiting, active surveillance, or brachytherapy. Cancer. 2007;109(12):2432–8. Athanazio D, Gotto G, Shea-Budgell M, Yilmaz A, Trpkov K. Global Gleason grade groups in prostate cancer: concordance of biopsy and radical prostatectomy grades and predictors of upgrade and downgrade. Histopathology. 2017;70(7):1098–106. Zhuang J, Kan Y, Wang Y, Marquis A, Qiu X, Oderda M, et al. Machine learning-based prediction of pathological upgrade from combined transperineal systematic and MRI-targeted prostate biopsy to final pathology: a multicenter retrospective study. Front Oncol. 2022;12:785684. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4959347","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345547170,"identity":"44a7db54-1bae-42d3-b166-712ca8988fcd","order_by":0,"name":"Jinfeng Wu","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Wu","suffix":""},{"id":345547171,"identity":"cee6c4be-f41b-414a-9f64-76442d2e74cc","order_by":1,"name":"Runqiang Yuan","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Runqiang","middleName":"","lastName":"Yuan","suffix":""},{"id":345547172,"identity":"76fb67f2-0222-464e-b055-4e7865596f19","order_by":2,"name":"Yangbai Lu","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yangbai","middleName":"","lastName":"Lu","suffix":""},{"id":345547173,"identity":"e399880e-c4d8-4ad4-a860-62147f0fc021","order_by":3,"name":"Jian Tan","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Tan","suffix":""},{"id":345547174,"identity":"13a5e99e-f308-472b-a837-a276fe85c6fc","order_by":4,"name":"Zhenjie Chen","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenjie","middleName":"","lastName":"Chen","suffix":""},{"id":345547175,"identity":"a718ad06-e8b0-4e05-ad0b-43a9d4c69eaf","order_by":5,"name":"Xianzhe Li","email":"","orcid":"","institution":"German Cancer Research Center (DKFZ)","correspondingAuthor":false,"prefix":"","firstName":"Xianzhe","middleName":"","lastName":"Li","suffix":""},{"id":345547176,"identity":"062895a1-a3f5-4764-bfb2-9ff2207fcfb2","order_by":6,"name":"Qu Leng","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qu","middleName":"","lastName":"Leng","suffix":""},{"id":345547177,"identity":"d0ace148-b3db-43a9-8926-066be32b967f","order_by":7,"name":"Rui Zhong","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhong","suffix":""},{"id":345547178,"identity":"4e401702-68d6-4418-92f3-2893da57d64b","order_by":8,"name":"Yongxin Zhang","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongxin","middleName":"","lastName":"Zhang","suffix":""},{"id":345547179,"identity":"6c2bf894-42be-4eca-bbb7-516bd4942afe","order_by":9,"name":"Hongxing Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACAxDB2AAk2BsbH34gTQvP4WZjCdK0SKS3CfAQo8VcIvnZw687DtvLz3zYxiDBYCen20BAi+WMNHNj2TNpzAa3E9seFDAkG5sdIOSwGwlm0pJtNmwG0ontBhIMBxK3EdaS/g2oRYJHfuZBIEmclhwzyY9tNhIMNxiJ1XLmTZk0Y1uagcGZRGAgGxDjl+Pp2yR/tgFDrP34w4cfKuzkCGoBAWZEdBgQoRwEGH8QqXAUjIJRMApGKAAAxgNBckPShCAAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hongxing","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-08-22 16:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4959347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4959347/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66740232,"identity":"46aeba16-1213-4dcc-be50-d8441884b749","added_by":"auto","created_at":"2024-10-16 05:40:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy flowchart.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure.1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4959347/v1/c8e5e60fad8d62f950c5ab37.jpg"},{"id":66739389,"identity":"2b3a9580-8ad5-4bbc-a7d9-37658809b73d","added_by":"auto","created_at":"2024-10-16 05:32:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":584612,"visible":true,"origin":"","legend":"\u003cp\u003eA-C: ROC, calibration, and DCA curve results of the machine learning models training group. D-F:ROC, calibration, and DCA curve results of the machine learning models internal validation group.\u003c/p\u003e","description":"","filename":"Figure.2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4959347/v1/2ec1c6309cd824d99738d37f.jpg"},{"id":66741658,"identity":"87520118-77e3-4fc2-a34d-b24937a51899","added_by":"auto","created_at":"2024-10-16 05:56:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":386410,"visible":true,"origin":"","legend":"\u003cp\u003eA:Nomogram of pathological upgrade prediction. B,C: ROC results of training group and internal validation group. D,E: Calibration curve results of training group and internal validation group. F,G: DCA curve results of training group and internal validation group.\u003c/p\u003e","description":"","filename":"Figure.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4959347/v1/d4aa35826d9257b43cacf37b.jpg"},{"id":66739387,"identity":"1d97ec0a-8341-4e4b-bbef-f424194184d7","added_by":"auto","created_at":"2024-10-16 05:32:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":294412,"visible":true,"origin":"","legend":"\u003cp\u003eA: ROC results of external validation group. B: Calibration curve results of external validation group. C: DCA curve results of external validation group.\u003c/p\u003e","description":"","filename":"Figure.4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4959347/v1/4b4895c493d8291861488b8d.jpg"},{"id":66739385,"identity":"0189f6a8-af51-4991-8063-9b73104158a5","added_by":"auto","created_at":"2024-10-16 05:32:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86566,"visible":true,"origin":"","legend":"\u003cp\u003eMachine learning model-based web predictor for predicting GGU in PCa.\u003c/p\u003e","description":"","filename":"Figure.5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4959347/v1/e08ffa36dd408cc6b1db041f.jpg"},{"id":92130011,"identity":"150602eb-d15a-47e2-8063-9177ca20d246","added_by":"auto","created_at":"2025-09-25 02:46:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2308998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4959347/v1/7979ed88-2200-4d88-b45c-c608a1cee373.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment and Validation of a Machine Learning Model Predicting Post-Radical Prostatectomy Gleason grading group upgrading Author’s information","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) is the second most common cancer globally and ranks as the fifth leading cause of cancer-related deaths in men as of 2022, with an estimated 1.5\u0026nbsp;million new cases and 397,000 deaths worldwide. Variations in PCa diagnostic practices among countries are key factors contributing to the significant differences in PCa incidence worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Currently, the diagnostic process for PCa heavily relies on the histological examination of biopsy tissues. The PCa grading and staging system derived from biopsies serves as a primary reference for assessing patient conditions and formulating treatment plans. Ideally, there should be complete consistency between the pathological grading from preoperative biopsies and the final pathological grading reported post-radical prostatectomy (post-RP). However, due to the multifocality and high intralesional heterogeneity of PCa, combined with potential grading errors by pathologists and sampling inaccuracies, biopsy pathology often underestimates the disease compared to post-RP pathology (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Studies based on the PCa grading and staging system proposed by the International Society of Urological Pathology (ISUP) in 2014 have shown a high rate of inconsistency between post-RP and preoperative biopsy pathological grading, with Gleason grading group upgrading (GGU) being the most common occurrence(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, GGU has been significantly associated with post-RP biochemical recurrence and a higher likelihood of surgical margin positivity in radical specimens (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Underestimating PCa biopsy pathological grading may lead to an overly conservative selection of initial treatment regimens for prostate cancer, while overestimating patient prognosis, posing significant challenges to clinical treatment.\u003c/p\u003e \u003cp\u003eWith advancements in statistical methods and artificial intelligence, predictive models are increasingly utilized in medical diagnostics. Predicting pathological upgrades based on preoperative and biopsy-known variables could be of crucial significance for surgeons when formulating surgical plans and determining postoperative treatment strategies for patients. Scientists have been striving to explore various methods to assess the risk of post-RP GGU more accurately. However, owing to the complexity of medical data and the intricate interplay among various factors, there are significant differences in the calculation methods of the models. Machine learning (ML) is a promising cross-disciplinary method capable of accurately predicting outcomes from multiple unrelated datasets that would otherwise be discrete and challenging to correlate (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). With the rapid development of evidence-based medicine, large and complex medical datasets require more advanced techniques for interpretation, and ML has become a promising choice for the diagnosis and prognostic prediction of many diseases. Previous approaches often relied on the construction of predictive nomograms or analyses based on multiple unfavorable factors (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, nomogram models are limited to independent predictive variables and risk overfitting when numerous predictors are incorporated, potentially leading to model failure. Consequently, there has been a shift towards more robust predictive classifiers that use ML methods. By processing and analyzing large, heterogeneous, and complex clinical data, practical clinical problems can be addressed, thereby making diagnostic and treatment processes more efficient (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Relevant research has also emerged in the field of urological treatments, for instance, ML algorithms that utilize pathological data have been pivotal in developing and validating automated pathological models for diagnosing and predicting outcomes in patients with bladder cancer or clear-cell renal cell carcinoma (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Similarly, studies on PCa have employed ML models to predict bone metastasis in patients with PCa or lymph node metastasis in intermediate-to-high-risk PCa patients, aiming for precise diagnosis and treatment guidance (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Presently, investigations into the risk factors of post-RP GGU are constrained by small sample sizes, single-center methodologies, and predictive models lacking external data validation. In this study, we aimed to identify the risk factors of post-RP GGU, and develop and validate a new ML model to predict post-RP GGU in PCa patients from the Surveillance, Epidemiology, and End Results (SEER) database as training and internal validation groups and PCa patients from Zhongshan People's Hospital as external validation groups using five ML methods.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData Collection\u003c/p\u003e \u003cp\u003eData from patients diagnosed with PCa from 2010 to 2018 were retrieved using SEER*Stat software (version 8.4.0.1). The International Classification of Diseases for Oncology, 3rd edition, code 8140/3, was used for data filtering. The inclusion criteria were primary PCa, age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, and complete clinical information. The exclusion criteria were preoperative neoadjuvant systemic therapy or other local treatments and incomplete clinical information. The SEER database is publicly accessible and anonymizes all patient information, hence exempt from ethical review. The external validation data from this retrospective clinical study waived the requirement for informed consent from the participants, with all procedures conducted in accordance with the Helsinki Declaration.\u003c/p\u003e \u003cp\u003eInclusion of Analytical Variables\u003c/p\u003e \u003cp\u003eEight variables potentially affecting GGU in patients with PCa were included: age, race, marital status, preoperative prostate-specific antigen (PSA) level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores.\u003c/p\u003e \u003cp\u003eStatistical Methods\u003c/p\u003e \u003cp\u003eData were processed using R Studio (version 4.2.3). Normally distributed continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), while non-normally distributed continuous data were presented as median (25th percentile, 75th percentile). Count data were presented as frequency and percentage (%). Intergroup comparisons were conducted using the chi-square test or Fisher's exact test. To determine the variables included in the ML model, a univariate logistic regression analysis of GGU risk factors was first performed, followed by a multivariate logistic regression analysis to identify independent predictive factors associated with GGU, considering P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as significant.\u003c/p\u003e \u003cp\u003eMachine Learning Classifiers and Model Building and Evaluation\u003c/p\u003e \u003cp\u003eFive ML and decision tree models were constructed based on selected independent risk factors: logistic regression (LR), gradient boosting machine (GBM), neural network (NNET), random forest (RF), and XGBoost (XGB). Initially, LR, one of the most fundamental classifiers, was used to predict the likelihood of a patient achieving a specific outcome based on relevant information or clinical features(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Next, the GBM method was applied. This ensemble technique constructs multiple decision trees sequentially, each aiming to correct the errors of its predecessors, thus effectively managing complex data and nonlinear relationships (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). NNET was also employed, characterized by its structure of multiple layers of neurons that perform nonlinear transformations, making it particularly adept at capturing intricate patterns and processing large datasets (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Furthermore, the RF algorithm was implemented to build numerous decision trees using bootstrap samples of the training data and random subsets of features for each split. It combines the outputs of these trees through majority voting to improve prediction accuracy and prevent overfitting (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Finally, XGB was used, a highly efficient and scalable tree-boosting system that builds robust models even with relatively small sample sizes by creating new models to predict the residuals of previous models and aggregating these for the final prediction (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The SEER dataset was divided into a 7:3 ratio for the training and internal validation groups, with external validation serving as a separate group. Each model underwent 10-fold cross-validation during training to ensure stability, and the best hyperparameters were selected using a random search method. Receiver operating characteristic (ROC) curves were constructed for both groups. Differences in predictive performance among the ML models were assessed using the area under the ROC curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity, and specificity, with the best-performing model selected as the final model after a comprehensive comparison.\u003c/p\u003e \u003cp\u003eWeb-based Predictor\u003c/p\u003e \u003cp\u003eFinally, the accuracy and generalizability of the best predictive model were validated using an independent external validation group. This step involved evaluating the model's performance on a separate dataset not used in the training and internal validation groups. The model's ability to accurately predict outcomes and maintain performance across different datasets and conditions was assessed, ensuring that it was not overfitted to the training data and could generalize well to new, unseen data. To enhance the usability and presentation of the data, a web-based predictor was developed using the Shiny package in R. Shiny is a powerful tool that allows the creation of interactive web applications directly from R software.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics and Postoperative Pathological Upgrading\u003c/p\u003e \u003cp\u003eThis study included a total of 65,574 PCa patients from the SEER database. They were randomly divided into training (45,903 cases) and internal validation (19,671 cases) groups at a ratio of 7:3. Additionally, 130 patients with PCa who underwent RP at Zhongshan People's Hospital between 2018 and 2023 were included. After excluding 32 patients who did not meet the inclusion criteria, 98 patients were included in the external validation study. The process is illustrated in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the patient characteristics for the training, internal validation, and external validation groups were presented. Among these, 11,931 patients (25.9%) in the training group, 5,112 (25.9%) in the internal validation group, and 24 (24.4%) in the external validation group experienced post-RP GGU.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining group(n\u0026thinsp;=\u0026thinsp;45,903)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal validation group (n\u0026thinsp;=\u0026thinsp;19,671)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExternal validation group (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,610 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,129 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,973 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,316 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (36.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,320 (87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,226 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (56.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35,983 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,462 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,075 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,999 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,669 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,136 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican or Alaska\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36,483 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,730 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (98.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,967 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,085 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,462 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,454 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e991 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e402 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGleason Patterns Clinical (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,245 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,679 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,208 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,902 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,827 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,389 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,268 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,280 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (30.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,355 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,421 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGleason Patterns Pathological (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,194 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,503 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,745 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,359 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (29.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,489 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,029 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (24.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,535 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,139 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,940 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,641 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,106 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,189 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,416 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,892 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026thinsp;~\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,476 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,318 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (23.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,905 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,272 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,485 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,103 (51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (46.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,404 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,415 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (32.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,014 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,153 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExamined = \u0026gt;=10 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,310 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,701 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent = \u0026gt;=33% (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27,789 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,819 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (53.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariable and Multivariable Logistic Regression Analysis\u003c/p\u003e \u003cp\u003eSubsequently, univariate logistic regression analysis revealed significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between GGU and variables such as age, race, marital status, preoperative PSA level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores among PCa patients. Multivariate logistic regression analysis was conducted on variables with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The results indicated that, except for marital status, all other variables were independent prognostic risk factors for GGU in patients with PCa \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Additionally, increasing age and preoperative PSA levels were associated with an increased likelihood of pathological upgrading, especially when age was \u0026ge;\u0026thinsp;70 years and preoperative PSA was \u0026ge;\u0026thinsp;20 ng/mL (OR 1.85; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and OR 2.70; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001/ng/mL).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Analysis and Multivariate Logistic Regression Analysis of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20(1.06\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41(1.24\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.02\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85(1.67\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93(0.88\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88(0.83\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.96\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13(1.02\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican or Alaska\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.74\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88(0.59\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97(0.91\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.91\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.84\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.85\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.79\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.83\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGleason Patterns Clinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24(0.22\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20(0.18\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14(0.13\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11(0.10\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21(0.20\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15(0.14\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08(1.00-1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30(1.20\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026thinsp;~\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(1.22\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.09(1.89\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50(1.34\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.70(2.40\u0026ndash;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0.81\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06(0.99\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93(0.86-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25(1.13\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExamined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.78\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90(0.83\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92(0.88\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25(1.16\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparison and Validation of Model Performance\u003c/p\u003e \u003cp\u003eFive supervised ML algorithms were employed to predict post-RP pathological GGU status, establishing binary models where labels were set as upgraded or otherwise. Surprisingly, all our ML models demonstrated relatively stable consistency, including LR, GBM, NNET, RF, and XGB, with their AUC values exceeding 0.7, namely 0.722, 0.726, 0.727, 0.703, and 0.728, respectively, as depicted in \u003cb\u003eFig.\u0026nbsp;2A.\u003c/b\u003e Despite minor differences in performance, the XGB model exhibited superior and stable results across both the training and internal validation groups. In the training group, the sensitivity, specificity, and AUC were 0.614, 0.760, and 0.728, respectively. Similarly, in the internal validation group, the XGB model maintained consistent performance with an AUC of 0.741, a sensitivity of 0.622, and a specificity of 0.767 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive performance of each model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTraining group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eInternal validation group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNNET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eCalibration curves and DCA were constructed for the five models used in our study \u003cb\u003e(Figs.\u0026nbsp;2B, C, E, F).\u003c/b\u003e In our ML models, except for the RF model which showed a more pronounced deviation from the 45-degree line, the calibration curves fit well. In our DCA, the y-axis represents the net benefit, used to assess whether any specific clinical decision was more beneficial or harmful. Each point on the x-axis represents the threshold probability for distinguishing between patients with and without GGU. The results indicated that all models achieved a net clinical benefit compared to the all-or-none strategy, with the XGB model seemingly having the highest net benefit across the entire range of threshold probabilities, especially when the risk threshold was below 80%. Furthermore, we compared the accuracy of the ML models with that of the nomograms. The ROC curves of the training and internal validation groups showed that the accuracy of the XGB model was higher than that of the nomograms (training group AUC: 0.728 vs. 0.722; internal validation group AUC: 0.741 vs. 0.736), as shown in \u003cb\u003eFig.\u0026nbsp;3.\u003c/b\u003e Subsequently, we conducted an external validation of the XGB model using an external validation group and constructed the ROC, DCA, and calibration curves. DCA indicated that the XGB model still had a higher net benefit at risk thresholds of 70\u0026ndash;90% \u003cb\u003e(Fig.\u0026nbsp;4).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDevelopment of a Web-Based Predictor for GGU in Prostate Cancer Patients\u003c/p\u003e \u003cp\u003eFinally, based on the XGB model, we developed a web-based predictor of GGU in PCa patients, providing a practical tool for clinicians to assess the risk of post-RP GGU. This predictor enhances clinical decision-making by allowing clinical physicians to quickly and accurately estimate a patient's likelihood of GGU, facilitating personalized treatment planning and potentially reducing the need for unnecessary interventions. By integrating easily accessible clinical data, this web-based predictor can be seamlessly incorporated into routine clinical practice, making it a valuable resource for improving patient outcomes and optimizing resource allocation in PCa management \u003cb\u003e(Fig.\u0026nbsp;5)\u003c/b\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alao-riskmodel.shinyapps.io/workrun7/\u003c/span\u003e\u003cspan address=\"https://alao-riskmodel.shinyapps.io/workrun7/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, significant efforts have been made to refine biopsy techniques to improve detection rates and achieve greater consistency with post-RP pathology results. For example, Jiang X et al.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) introduced regional saturation biopsy, enhancing the detection of clinically significant PCa and accurately identifying index lesions. Additionally, research by Qu L et al.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) found that transperineal biopsy offers more accurate gleason grading than transrectal biopsy in patients without prior satisfactory magnetic resonance imaging (MRI). Furthermore, image fusion techniques have further increased detection rates, with targeted biopsy combined with ultrasound and multiparametric MRI reducing post-RP GGU compared to systematic biopsy. (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Despite these advances, post-RP GGU remains a common issue, influencing decisions between active surveillance (AS) and aggressive treatment, highlighting the need for early screening models for high-risk PCa patients.\u003c/p\u003e \u003cp\u003eThis study utilized the extensive SEER database and ML algorithms to establish a predictive model for identifying PCa patients at risk of post-RP GGU before making treatment decisions. ML offers several advantages, including the ability to handle large and complex datasets, uncover hidden patterns and relationships within the data, and continuously improve predictive accuracy through learning and optimization. These capabilities enable more personalized and precise risk assessments, leading to better-informed clinical decisions. Multifactorial LR analysis identified age, race, preoperative PSA level, biopsy ISUP grade, total number of biopsy cores, number of positive biopsy cores, and the percentage of positive biopsy cores as independent risk factors for post-RP GGU. Results indicated that PCa patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years, with preoperative PSA levels\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/mL, and over 10 positive biopsy cores were at higher risk of post-RP GGU. Additionally, consistent with previous findings (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), the most common GGU occurred in ISUP GG1 and GG2, with the majority upgraded to GG2, followed by GG3. Notably, these cohorts are often considered for AS, suggesting a risk of missing curative opportunities if adverse pathology is discovered after biopsy. Approximately 30% of patients experience tumor progression during AS, and studies have shown that a significant portion of initially low-risk patients are upgraded to RP, indicating that AS may not suit those at risk of upgrading (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Thus, identifying the risk factors associated with GGU is crucial to avoid undertreatment.\u003c/p\u003e \u003cp\u003eIn this study, five supervised ML algorithms were employed to predict the GGU from biopsy to RP. XGBoost exhibited the best discriminative ability among all cohorts. Comparing our findings with those of other studies investigating post-RP upgrading and downgrading, many of which utilized small historical cohorts, our ML-based models surpassed those of Athanazio et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), based on a cohort of 2529 PCa patients undergoing RP treatment, where their study yielded an AUC value of 0.699. Additionally, compared to Zhuang et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), based on a cohort of 515 individuals who established an ML model predicting pathological consistency using combined systematic and MRI-targeted prostate biopsy, whose best-performing model was XGBoost, yielding an AUC value of 0.71, our models, except for RF, were markedly superior.\u003c/p\u003e \u003cp\u003eThe strengths of this study lie in its large cohort and the utilization of nationally registered data. Additionally, multiple models were positively compared to the nomogram model, with XGB demonstrating superior discriminative ability in both the training and internal validation groups. The ML results are more reliable, enhancing the robustness of the outcomes, and the web-based predictor makes clinical application more convenient. However, this study had several limitations that warrant further investigation. Firstly, the overall population of our study was drawn from the SEER database and external validation only used a small sample size from a single center. Secondly, the SEER database provides only partial variables for patients with PCa and lacks crucial data such as prostate volume, PSA density, PI-RADS scores, post-RP surgical margin status, and testosterone levels, thereby restricting the inclusion of various potential risk factors impacting the GGU of patients with PCa. Integrating additional factors could notably augment model performance. Thirdly, the intrinsic opacity of ML algorithms may impede model interpretability. Despite these limitations, we assert the substantive robustness of our primary study findings.\u003c/p\u003e \u003cp\u003eIn conclusion, we developed a predictive model for appraising post-RP GGU risk in PCa patients, leveraging the XGB algorithm, and developing a web-based predictor. Individuals at high risk for post-RP GGU are recommended for further detailed screening based on web predictor factors. This may aid clinicians in tailoring personalized treatments for patients with PCa.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eactive surveillance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egradient boosting machine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egleason grade group upgrading\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISUP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Society of Urological Pathology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elogistic regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emachine learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneural network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epost-RP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epost-radical prostatectomy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprostate cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprostate-specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erandom forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradical prostatectomy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurveillance, Epidemiology, and End Results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXGB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eAll authors declare that there are no financial, personal, or other conflicts of interest in this study. The design, implementation, data analysis, and interpretation of the results in this research are entirely based on scientific principles, ensuring the fairness and objectivity of the research process and conclusions.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe SEER database is an open and publicly accessible resource, thus not requiring approval or informed consent from an agency review committee. This study, which is a retrospective analysis of single-center data, does not involve patient-specific information and therefore does not require ethics committee approval.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515220032), Guangdong Medical Science and Technology Research Foundation(B2023195), Science and Technology Project of Zhongshan City (2020B1073), Zhongshan city people's hospital Major Project of Scientific Research Foundation (BG20228249) and Zhongshan City People's Hospital Outstanding Youth Project (SG2023106).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJW and JT conceived and designed the study. Administrative support was provided by HH, YZ, RY, and YL. Data collection and assembly were carried out by JW, JT, and ZC. Data analysis and interpretation were performed by YZ, XL, QL, JW, and JT. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the medical team at Zhongshan People's Hospital for their support and assistance during this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe study's data can be accessed from the repository and downloaded using the following link (https://seer.cancer.gov). Additional information can be provided upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eME JF, Siegel RL, Isabelle Soerjomataram M, Ahmedin Jemal D. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpstein JI, Feng Z, Trock BJ, Pierorazio PM. 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Front Oncol. 2022;12:785684.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"machine learning, post-radical prostatectomy gleason grading group upgrading, risk prediction, prostate","lastPublishedDoi":"10.21203/rs.3.rs-4959347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4959347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBased on the 2014 International Society of Urological Pathology (ISUP) grading system, the study assesses the disparities in gleason grading group between preoperative needle biopsy pathology and post-radical prostatectomy (post-RP) specimens for prostate cancer (PCa). It investigates the risk factors for post-RP gleason grading group upgrading (GGU) and develops and validates a machine learning (ML) model for predicting post-RP GGU in PCa patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis is conducted on demographic and clinicopathological variables of PCa patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018. Five different ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), neural network (NNET), random forest (RF), and XGBoost (XGB), are utilized. The patients with localized PCa who underwent radical prostatectomy (RP) at Zhongshan People's Hospital from January 2018 to December 2023 were selected as the external validation group. Model performance is evaluated using receiver operating characteristic (ROC) area under the curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity (recall), and specificity. A web-based predictor is developed based on the best-performing model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study included a total of 65,574 PCa patients from the SEER database and 98 patients from the external validation group. Among them, there were 11,931 in the training group, 5,112 in the internal validation group, and 24 in the external validation group who experienced post-RP GGU. Risk factors such as patient age, race, preoperative prostate-specific antigen (PSA) level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores were significantly associated with GGU (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Five ML algorithms demonstrated relatively stable consistency, with their AUC values exceeding 0.7. A web-based predictor was developed using the XGB model, which showed the best predictive performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study introduced a ML model and an online predictor designed to assess the risk of post-RP GGU in PCa patients, aiding physicians in customizing clinical decisions and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Establishment and Validation of a Machine Learning Model Predicting Post-Radical Prostatectomy Gleason grading group upgrading Author’s information","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 05:32:02","doi":"10.21203/rs.3.rs-4959347/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe976adb-7f09-4b47-a5da-25ac530d543f","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-25T02:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-16 05:32:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4959347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4959347","identity":"rs-4959347","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}