Predicting Early Castration-Resistant Prostate Cancer in Metastatic Prostate Cancer: Development and Internal Validation of a Nomogram for Patients Receiving Abiraterone plus Androgen-Deprivation Therapy

Predicting Early Castration-Resistant Prostate Cancer in Metastatic Prostate Cancer: Development and Internal Validation of a Nomogram for Patients Receiving Abiraterone plus Androgen-Deprivation Therapy | 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 Predicting Early Castration-Resistant Prostate Cancer in Metastatic Prostate Cancer: Development and Internal Validation of a Nomogram for Patients Receiving Abiraterone plus Androgen-Deprivation Therapy Yujie Wang, Xiaofu Zeng, Liangliang Qing, Guangqing Fu, Dengjun Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7733342/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Some men with metastatic prostate cancer progress to castration-resistant disease rapidly after initiating abiraterone acetate in combination with androgen-deprivation therapy. Early risk assessment may guide subsequent follow-up and treatment decisions. This study aimed to identify factors associated with progression within two years and to develop a straightforward predictive tool for the early identification of high-risk patients. Methods A retrospective analysis of 208 men with bone-only metastatic prostate cancer who received abiraterone acetate in combination with androgen-deprivation therapy at our single institution between January 2019 and March 2025 was conducted. Univariate and multivariate Cox proportional hazards regression analyses were employed to identify predictors of progression to metastatic castration-resistant prostate cancer (mCRPC). Subsequently, a nomogram was developed based on the multivariate model and internally validated using 1,000 bootstrap resamples. The model's discrimination, calibration, and clinical utility were then assessed. Results Of the 208 patients, 80 (38.5%) progressed to mCRPC within two years. Multivariate analysis identified four independent predictors of early progression: Gleason score of 9–10 (HR = 4.334, 95% CI: 2.248–8.355), clinical T stage 3–4 (HR = 2.315, 95% CI: 1.168–4.589), higher prostate-specific antigen nadir (nPSA; HR = 1.267, 95% CI: 1.119–1.435), and shorter time to nPSA(TTN;HR = 0.863, 95% CI: 0.801–0.930). In Kaplan–Meier analysis, each of these factors was significantly associated with shorter progression-free survival (log-rank P < 0.001). Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.894 (95% CI: 0.851–0.938), indicating the developed nomogram possesses excellent discriminatory capability. The calibration curve demonstrated high concordance between predicted and actual outcomes, with a mean absolute error of 0.018. Decision curve analysis (DCA) further confirmed the model's favorable clinical utility. Conclusion Patients with higher Gleason scores (9–10), advanced clinical T stage (T3–4), higher nPSA, and shorter TTN are at increased risk of early castration resistance following treatment with abiraterone acetate plus androgen deprivation therapy. The developed nomogram serves as a practical tool to estimate individualized two-year progression risk, potentially facilitating tailored clinical management through intensified monitoring and timely treatment modification in high-risk individuals. External validation in prospective, multicenter cohorts is warranted before broader clinical application. Metastatic prostate cancer Castration-resistant prostate cancer Abiraterone acetate Androgen deprivation therapy Nomogram Predictive model Risk factors Figures Figure 1 Figure 2 Figure 3 Introduction Prostate cancer is one of the most prevalent malignancies affecting men globally. In China, the incidence rate has seen a marked increase over the past decade. Current projections indicate that by 2030, prostate cancer may ascend from being the seventh to the third most frequently diagnosed cancer in the country, with an anticipated surge of 517% in cases between 2015 and 2030[ 1 ]. These trends underscore the growing public health challenge posed by prostate cancer in the Chinese population. In this region, a considerable proportion of patients are diagnosed at advanced, metastatic stages[ 2 ]. While metastatic prostate cancer remains incurable, the advent of combination therapy—namely, abiraterone acetate(AA) with androgen deprivation therapy (ADT)—has significantly improved outcomes for patients with metastatic hormone-sensitive prostate cancer (mHSPC). This combined approach has demonstrated efficacy in delaying disease progression and extending overall survival in a substantial subset of these patients[ 3 ]. Despite these advances, a substantial number of patients still develop metastatic castration-resistant prostate cancer (mCRPC) within a short period after initiating treatment [ 4 ]. This early progression presents a major clinical challenge, as mCRPC is associated with limited therapeutic options and a poor prognosis [ 5 ]. The transition to castration resistance is driven by a complex interplay of clinicopathological and molecular factors. Tumors can acquire genetic alterations, adapt their microenvironment, and evolve under selective therapeutic pressure, ultimately leading to treatment resistance [ 6 ]. Several biomarkers have been implicated in this process, including high Gleason score, elevated prostate-specific antigen nadir (nPSA), short time to nPSA(TTN), and the presence of perineural invasion—each reflecting more aggressive disease biology and a heightened risk of early resistance [ 7 – 9 ]. Notably, the time to castration resistance exhibits a strong correlation with overall survival, underscoring the critical need for early identification of patients at high risk[ 10 ]. Emerging evidence suggests that pre-existing castration-resistant subclones may already be present at initial diagnosis, thereby emphasizing the importance of tailored treatment strategies that account for tumor heterogeneity [ 11 ]. In line with this imperative, the 2023 Advanced Prostate Cancer Consensus Conference recommended risk-adapted approaches incorporating individual patient characteristics[ 12 ]. However, a critical gap persists in current clinical practice: the absence of reliable tools to identify patients likely to exhibit rapid progression to mCRPC despite receiving AA in conjunction with ADT. Early prediction could facilitate personalized surveillance strategies and timely intervention, such as intensified imaging or alternative treatments[ 13 ]. Nomograms have emerged as useful instruments for integrating multiple prognostic variables into practical, individualized risk-assessment tools [ 14 ]. While various prediction models have been developed for different scenarios in prostate cancer, a straightforward and validated model specifically targeting early progression to castration-resistant prostate cancer (CRPC) in men undergoing AA plus ADT, particularly those with bone-only metastases, is currently lacking. Therefore, this study aimed to achieve two primary objectives: first, to identify readily available clinical risk factors predictive of progression to mCRPC within two years; and second, to develop and internally validate a simplified nomogram for quantifying this risk. This would thereby facilitate risk-adapted monitoring, support clinical decision-making, and enable more personalized and timely treatment strategies. Materials and Methods Study Population and Data Collection This single-center, retrospective cohort study was conducted in accordance with both institutional and international ethical standards. The study protocol received approval from the Ethics Committee of Zigong Fourth People’s Hospital (Approval No: 2025-032). Informed consent was obtained from all participants throughout the research process. This investigation included 208 patients diagnosed with prostate cancer and concomitant bone metastases, all of whom were treated at our institution between January 2019 and March 2025. Each patient underwent ultrasound-guided prostate biopsy, with histopathological evaluation confirming the presence of acinar adenocarcinoma. Comprehensive imaging studies revealed multiple metastatic lesions localized to the axial skeleton, with no evidence of visceral or extremity bone involvement at the time of the enrollment evaluation. The stringent inclusion criteria implemented ensured the formation of a clinically homogeneous cohort for analysis. Treatment protocol All eligible patients received a standardized first-line treatment regimen consisting of daily oral AA (1000 mg) combined with prednisone (5 mg). Furthermore, medical castration therapy was administered via subcutaneous injection of goserelin (3.6 mg) every 28 days. Treatment continued until any of the following occurred: development of castration-resistant disease, unacceptable treatment-related toxicity despite optimal management, patient request for discontinuation, or reaching the pre-specified study termination date. Eligibility criteria Inclusion criteria were defined a priori to ensure a clinically homogeneous cohort. All patients were required to meet the following:(1)Histopathological confirmation of prostatic adenocarcinoma.༈2༉Objective evidence of multiple metastatic lesions in the axial skeleton, as demonstrated by bone scintigraphy. Findings were to be reviewed by a board-certified radiologist.༈3༉At least one scheduled post-baseline assessment suitable for evaluating treatment response or progression. Exclusion criteria were established to mitigate confounding from coexisting conditions or prior therapies:༈1༉Any concurrent malignancy or a prior malignancy with a substantial likelihood of affecting outcome or survival.༈2༉Use of 5α-reductase inhibitors within 6 months prior to the initiation of AA therapy, to minimize prostate-specific antigen (PSA)-related measurement bias.༈3༉Receipt of systemic chemotherapy or radiotherapy during the on-study follow-up period, which could alter progression risk independently of the study regimen.༈4༉Radiographic evidence of visceral metastases (e.g., hepatic, pulmonary, or cerebral lesions) at baseline.༈5༉Documented non-adherence to AA plus ADT that precluded a meaningful assessment of treatment effect.These criteria ensured that all participants initiated the same therapeutic protocol, had bone-only metastatic disease at enrollment, and could be reliably followed for outcomes of interest. Comprehensive clinical information was abstracted from the electronic medical record using a standardized template and cross-checked by two independent reviewers. Variables were prespecified and grouped into the following six domains:(1)Demographics: Age at treatment initiation and body mass index (BMI, kg/m²).༈2༉Tumor Characteristics: Gleason score, clinical T stage, perineural invasion (PNI) on pathology, baseline PSA(ng/mL), and alkaline phosphatase (ALP, U/L).༈3༉On-Treatment Response Metrics: nPSA (defined as the lowest PSA value observed after therapy initiation) and TTN(the interval in months from treatment start to nPSA).༈4༉Systemic Inflammation Indices: Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). For each index, values were calculated from the same complete blood count as absolute neutrophils/lymphocytes and platelets/lymphocytes, respectively. (5)Metabolic and Lifestyle Factors: Diabetes mellitus status, cigarette smoking history, and alcohol consumption as recorded at baseline.༈6༉Laboratory Parameters: with a particular emphasis on hemoglobin (HGB, g/L). Baseline laboratory values were defined as the measurements closest to the start of AA plus ADT. Follow-up and Outcome Definition Patients were monitored via a standardized clinical pathway. Serum PSA levels were measured monthly following treatment initiation, concurrently with clinical assessments. Imaging was acquired in cases of rising PSA or new symptoms. Radiographic progression was adjudicated according to PCWG3 criteria by board-certified radiologists, who were blinded to clinical data whenever feasible. The primary endpoint of this study was progression to mCRPC. Progression was defined according to the 2014 European Association of Urology (EAU) criteria, which necessitate castrate serum testosterone levels (below 50 ng/dL or 1.7 nmol/L), in conjunction with objective evidence of disease progression [ 15 ]. Such evidence could be biochemical and/or radiographic. Radiographic progression was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 for soft-tissue disease and by the identification of new lesions on bone scans for osseous disease. Progression-free survival (PFS) was defined as the interval from the initiation of combined abiraterone acetate and androgen deprivation therapy until the first documentation of mCRPC. Patients who did not experience progression were censored at the date of their last adequate disease assessment within the study period. Statistical Analysis All statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria). Data were assessed for normal distribution using the Kolmogorov-Smirnov test. Variables conforming to a normal distribution were analyzed with an independent samples t-test and reported as mean ± standard deviation. For variables deviating from normal distribution, the Mann-Whitney U test was employed, with findings presented as median (interquartile range). Categorical variables were reported as counts and percentages and compared using the chi-square test or Fisher's exact test when expected cell counts were small. Time-to-event associations with progression to metastatic castration-resistant prostate cancer within two years were evaluated using Cox proportional hazards regression. Initially, univariable models were fitted to screen candidate predictors. Subsequently, variables with a P-value < 0.05 were entered into a multivariable Cox model to identify independent prognostic factors. Effect sizes were expressed as hazard ratios (HR) with corresponding 95% confidence intervals (CI). Survival functions were estimated using the Kaplan–Meier method, and between-group differences were tested with the log-rank test. A prediction nomogram was constructed from the final multivariable Cox model. Model discrimination was quantified using Receiver operating characteristic (ROC) curves, and calibration was assessed with calibration curves to evaluate the consistency between predicted and actual probabilities. Decision curve analysis (DCA) was utilized to assess the net clinical benefit of the nomogram. All hypothesis tests were two-sided, and a P-value < 0.05 was considered statistically significant. Results Patient Characteristics This study included 208 patients with prostate cancer presenting exclusively with multiple axial bone metastases. The mean age was 75.87 ± 6.03 years. The median time to progression to castration-resistant prostate cancer (CRPC) was 31 months (interquartile range [IQR], 14–44 months). Patients were divided into two groups based on the time to CRPC progression: 80 patients (38.5%) developed mCRPC within two years and were classified as the rapid progression group (Group 1); the remaining 128 patients (61.5%) progressed after two years and were classified as the slow progression group (Group 0). The baseline characteristics of the two groups are presented in Table 1 . There were no statistically significant differences between the groups in terms of age, baseline PSA levels, HGB concentration, NLR, PLR, smoking history, alcohol consumption, or diabetes (all P > 0.05). Notably, baseline PSA levels did not differ significantly between the groups (P = 0.982). This suggests that on-treatment PSA dynamics—such as nPSA and TTN—may serve as more reliable predictors of disease progression than initial PSA values. Table 1 comparison of Clinical Characteristics Between Rapid Progressors (Group 1) and Slow Progressors (Group 0). Characteristics Total (n = 208) Group 1 (n = 80) Group 0 (n = 128) p value Age(years) 75.87 ± 6.03 77.00(71.25-81.00) 77.00(71.25-80.00) 0.571 BMI(kg/m 2 ) 22.92(20.94–24.64) 22.54(20.83–23.78) 23.20(21.06–25.02) 0.025 Gleason Score,n(%) < 0.001 ≤7 90(43.3) 13(16.3) 77 (60.2) 8 38(18.3) 13(16.3) 25 (19.5) 9–10 80(38.4) 54 (67.5) 26 (20.3) Clinical T stage, n (%) < 0.001 T1-2 84(40.4) 15 (18.8) 69(53.9) T3-4 124(59.6) 65 (81.3) 59(46.1) PNI, n (%) < 0.001 Yes 84(40.4) 49(61.25) 35 (27.34) No 124(59.6) 31(38.75) 93 (72.66) baseline_PSA (ng/mL) 170(100–241) 168(95–239) 172(101–245) 0.982 nPSA (ng/mL) 0.07(0.02–0.83) 0.93(0.29–1.76) 0.035(0.01–0.07) < 0.001 TTN(months) 9(7–11) 7.5 (5–10) 9.0 (7–12) < 0.001 HGB (g/L) 133(122–142) 136(120-144.75) 131(123-141.75) 0.406 ALP (U/L) 80(69.25–95.50) 95.0(79.25-134.75) 76.0 (66.0–84.0) < 0.001 NLR 3.61(2.72–4.66) 3.955(2.62–5.145) 3.465(2.765–4.505) 0.212 PLR 122.31(98.09–147.00) 129.07(100-165.33) 119.33(94.88-143.45) 0.075 smoking, n (%) 0.076 Yes 68 32 (40) 36(28.1) No 140 48 (60) 92 (71.9) Drinking, n (%) 0.748 Yes 28 10 (12.5) 18 (14.1) No 180 70(87.5) 110(85.9) diabetes, n (%) 0.678 Yes 34 12 (15) 22 (17.2) No 174 68 (85) 106 (82.8) crpc_time (months) 31(14–44) 12.5(8.0–15.0) 41.0(32.25–49.75) < 0.001 BMI body mass index , PNI perineural invasion , PSA prostate-specific antigen , nPSA prostate-specific antigen nadir , TTN time to PSA nadir , HGB hemoglobin , ALP alkaline phosphatase , NLR neutrophil-to-lymphocyte ratio , PLR platelet-to-lymphocyte ratio , CRPC castration-resistant prostate cancer. ------------------------------------- Insert Table 1 -------------------------------------- Risk Factor Analysis A univariate Cox proportional hazards regression analysis identified that several factors were significantly associated with the time to progression to mCRPC. As presented in Table 2 , these factors included BMI with a p-value of 0.032, Gleason Score (p < 0.001), clinical T stage (p < 0.001), PNI (p < 0.001), nPSA (p < 0.001), TTN (p < 0.001), ALP (p < 0.001), and the PLR with a p-value of 0.022. Each of these variables demonstrated a statistically significant correlation with the risk of progression to mCRPC. Table 2 Univariate and Multivariate Cox Regression Analyses for Factors Associated with Progression to mCRPC Variables Univariate analysis Multivariate analysis HR 95%CI P value HR 95%CI P value Age 1.018 0.980 ~ 1.057 0.352 BMI 0.920 0.853 ~ 0.993 0.032 0.950 0.874–1.033 0.228 Gleason Score ≤ 7 1 =8 2.654 1.230–5.727 0.013 1.631 0.722–3.683 0.239 ≥ 9 7.225 3.928–13.289 < 0.001 4.334 2.248–8.355 < 0.001 Clinical T stage T1-2 1 T3-4 3.972 2.262–6.975 < 0.001 2.315 1.168–4.589 0.016 PNI no 1.000 Yes 3.033 1.930–4.765 < 0.001 1.145 0.673–1.947 0.617 baseline_PSA 1.000 0.998–1.002 0.895 nPSA 1.365 1.264–1.473 < 0.001 1.267 1.119–1.435 < 0.001 TTN 0.874 0.817–0.934 < 0.001 0.863 0.801–0.930 < 0.001 HGB 0.998 0.986–1.011 0.765 ALP 1.005 1.003–1.007 < 0.001 1.002 0.999–1.005 0.161 NLR 0.993 0.914–1.078 0.861 PLR 1.003 1.000-1.005 0.022 1.000 0.996–1.004 0.997 smoking no 1 yes 1.468 0.938–2.296 0.093 Drinking no 1 yes 0.892 0.460–1.730 0.735 diabetes No 1 Yes 0.892 0.483–1.648 0.715 BMI body mass index , PNI perineural invasion , PSA prostate-specific antigen , nPSA prostate-specific antigen nadir , TTN time to PSA nadir , HGB hemoglobin , ALP alkaline phosphatase , NLR neutrophil-to-lymphocyte ratio , PLR platelet-to-lymphocyte ratio. Bold text indicates a statistically significant difference. A multivariate Cox proportional hazards regression analysis was conducted using variables that demonstrated significance in the univariate analysis. This analysis identified four independent factors significantly associated with an increased risk of early progression to mCRPC: Gleason score ≥ 9 (HR: 4.334, 95% CI: 2.248–8.355, P < 0.001), clinical T stage T3–4 (HR: 2.315, 95% CI: 1.168–4.589, P = 0.016), elevated nPSA levels (HR: 1.267, 95% CI: 1.119–1.435, P < 0.001), and shortened TTN (HR: 0.863, 95% CI: 0.801–0.930, P < 0.001). A comprehensive summary of these findings is detailed in Table 2 . ------------------------------------- Insert Table 2 -------------------------------------- Survival Analysis and Nomogram Construction Kaplan-Meier analysis was employed to evaluate PFS based on key prognostic variables. The analysis revealed that patients exhibiting the following characteristics experienced significantly worse PFS: Gleason score ≥ 9, clinical T stage T3–4, nPSA level > 0.2 ng/mL, and TTN < 9 months. The Log-rank test indicated statistically significant differences for these factors (P < 0.001), as illustrated in Fig. 1 . Utilizing the multivariate Cox proportional hazards model, a predictive nomogram was developed to estimate the individualized 2-year risk of progression to mCRPC (Fig. 2 ). This nomogram incorporates four independently significant prognostic variables: Gleason Score, clinical T stage, nPSA level,and TTN, thereby serving as a practical tool for clinical risk stratification. ROC analysis demonstrated an AUC of 0.894 (95% CI: 0.851–0.938), indicating excellent discriminatory power, as illustrated in Fig. 3 a. The calibration curve, validated through bootstrap resampling with 1000 iterations, showed exceptional congruence between predicted probabilities and observed outcomes, evidenced by a mean absolute error of 0.018, as depicted in Fig. 3 b. DCA was performed to assess the clinical utility of the prediction model. The results, presented in Fig. 3 c, indicate that the nomogram can effectively estimate the probability of progression to mCRPC in patients receiving AA in conjunction with ADT across a probability threshold ranging from 0.1 to 0.90. Discussion Main Findings This study successfully developed and internally validated a nomogram designed to predict the 2-year risk of progression to mCRPC in patients with metastatic prostate cancer receiving first-line AA combined with ADT. Four readily accessible clinical parameters were identified as independent prognostic factors: Gleason Score, clinical T stage, nPSA, and TTN. The nomogram demonstrated robust discriminatory capability, as evidenced by ROC analysis yielding an AUC of 0.894 (95% CI: 0.851–0.938). Furthermore, the calibration curve indicated a high degree of concordance between predicted probabilities and observed outcomes, with a mean absolute error of 0.018 (Fig. 3 b). These findings highlight the nomogram's excellent discriminatory power and calibration, positioning it as a valuable tool for personalized risk assessment at the initiation of treatment. Comparison with Previous Studies and Potential Mechanisms The present findings are consistent with and extend existing evidence regarding prognostic indicators in metastatic mHSPC. A high Gleason Score (≥ 9) demonstrated a strong association with aggressive disease progression[ 16 ]. This aligns with literature suggesting that poorly differentiated tumors exhibit heightened genomic instability, intrinsic heterogeneity, and increased resistance to androgen receptor signaling inhibitors such as AA [ 17 ]. Similarly, an advanced clinical T stage (T3–4) reflects locally invasive disease with a higher tumor burden and an increased risk of micrometastases, corroborating its established role as an independent predictor of early progression[ 18 ]. Notably, our study underscores the superior prognostic value of on-treatment PSA kinetics, specifically nPSA and TTN, over baseline PSA levels. An elevated nPSA indicates a significant proportion of cancer cells have progressed to a castration-resistant phenotype, enabling them to evade ADT[ 19 ]. This observation is consistent with prior findings demonstrating a significant association between lower nPSA and longer PFS[ 20 , 21 ].Conversely, a shorter TTN was associated with a poorer prognosis, a finding that aligns with previous mainstream research[ 7 , 21 ]. However, this observation challenges the theoretical notion linking a rapid PSA decline to enhanced survival. This discrepancy may be attributed to differences in treatment context and definitions of “rapid response.” Within the setting of AA combined with ADT, a very short TTN might reflect the rapid depletion of androgen-sensitive cells. This, in turn, could accelerate the outgrowth of pre-existing resistant clones through a process potentially involving therapy-induced lineage plasticity[ 22 , 23 ], thereby highlighting the complex interplay between tumor biology and treatment pressure. Interestingly, while baseline inflammatory markers (PLR) and PNI demonstrated significance in univariate analysis, they did not retain independent prognostic value in the multivariate model. This observation suggests that their predictive utility may be overshadowed by stronger determinants, namely Gleason score, clinical T stage, and PSA kinetics. Previous studies have also reported inconsistent results regarding the prognostic role of PLR in mCRPC, Some studies suggest that a low PLR correlates with improved survival rates, particularly in patients with mCRPC treated with abiraterone[ 24 ]. However, other studies have failed to identify a significant association between PLR and survival rates[ 25 ]. This discrepancy may stem from variations in patient populations, treatment modalities, and study designs. In mHSPC patients, the predictive value of PLR remains under-validated, but its role as a marker of inflammatory and coagulation status warrants further investigation.In contrast, NLR has often demonstrated more stable predictive value across different cohorts [ 26 , 27 ]. Integrating PLR and NLR into refined inflammatory markers (such as the systemic inflammatory response index) may enhance prognostic accuracy, but this falls outside the scope of the present study. Clinical Implications Building upon this pathophysiological framework, the proposed nomogram offers a quantitative method for risk stratification at the initiation of AA plus ADT. The ROC curve analysis, comparing our nomogram to individual clinical factors, demonstrates its superior discriminative ability, thereby supporting its clinical utility. High-risk patients, characterized by high nomogram scores, may benefit from intensified monitoring, including shortened PSA testing intervals and earlier imaging. Consideration of upfront treatment intensification is also warranted. For instance, the addition of docetaxel to ADT and Androgen Receptor Signalling Inhibitors has demonstrated survival benefits in high-volume or high-risk mHSPC patients, as evidenced in the ARASENS and PEACE-1 trials[ 28 , 29 ]. Furthermore, early transition to next-line therapies, such as Poly(ADP-ribose) polymerase inhibitors for patients with homologous recombination repair mutations or Lutetium Prostate-Specific Membrane Antigen (PSMA) therapy for eligible cases, may be warranted upon signs of progression [ 30 ]. For low-risk patients, the model supports maintaining standard treatment with less intensive follow-up, thus optimizing resource allocation. Additionally, the model may aid in patient selection for clinical trials exploring novel combination therapies or treatment de-escalation strategies. Limitations Notwithstanding these implications, this study is subject to several notable limitations that require consideration. Firstly, the retrospective, single-center design inherently introduces potential selection bias and restricts the external validity of the findings. Secondly, while the sample size is substantial, it remains moderate in scale, and the limited number of events within certain subgroups may compromise the precision of statistical estimates. Thirdly, the absence of critical molecular biomarker data, including Androgen Receptor Variant 7 (AR-V7), DNA repair gene mutations, and Phosphatase and tensin homolog (PTEN) loss, represents a significant constraint, as these factors could potentially enhance risk stratification and elucidate underlying resistance mechanisms. Fourthly, PSA dynamics are assessed at one-month intervals, yet radiological evaluations are symptom-driven, potentially introducing variability in assessing time to progression. Lastly, the proposed nomogram necessitates external validation through independent, preferably prospective, multi-center cohorts to establish its generalizability and clinical utility prior to widespread implementation. Future Directions To address the aforementioned limitations and build upon our findings, future research should prioritize the following key areas: (1) External validation of the proposed nomogram across diverse patient populations to ensure generalizability; (2) Development of integrated predictive models that combine molecular biomarkers (derived from liquid or tissue biopsies) with conventional clinical variables to create comprehensive multi-omics-based prognostic tools; (3) Prospective evaluation of the nomogram's clinical utility and its impact on treatment decision-making processes and patient outcomes; and (4) Investigation of the underlying biological mechanisms associated with disease progression in patients exhibiting the identified risk profile, with particular emphasis on elucidating the dynamic relationship between PSA kinetics and tumor clonal evolution. Conclusion In patients with metastatic prostate cancer undergoing treatment with AA in conjunction with ADT, several independent risk factors significantly heighten the likelihood of early progression to mCRPC within two years. Key indicators include a Gleason Score of 9 or higher, clinical T stage T3–4, elevated nPSA levels, and a shorter TTN. The developed nomogram adeptly integrates these critical variables, showcasing impressive calibration and discriminative performance in predicting individual risk. This innovative predictive model not only enhances patient stratification but also facilitates the customization of therapeutic strategies, enabling earlier interventions for high-risk patients and thereby potentially improving clinical outcomes. To validate its applicability across diverse clinical settings, external validation in various cohorts is essential to establish its utility in routine clinical practice. Abbreviations AA abiraterone acetate ADT androgen deprivation therapy mHSPC metastatic hormone sensitive prostate cancer mCRPC metastatic castration resistant prostate cancer nPSA prostate specific antigen nadir TTN time to PSA nadir CRPC castration resistant prostate cancer PSA prostate specific antigen BMI body mass index PNI perineural invasion ALP alkaline phosphatase NLR neutrophil to - lymphocyte ratio PLR platelet to - lymphocyte ratio HGB hemoglobin EAU European Association of Urology RECIST Response Evaluation Criteria in Solid Tumors PFS Progression free survival HR Hazard Ratio CI Confidence interval ROC Receiver operating characteristic AUC Area under the curve DCA Decision curve analysis . Declarations Competing interests: The authors declare that they have no competing interests. Ethics approval and consent to participate: This study was approved by the Ethics Committee of Zigong Fourth People's Hospital (Approval No: 2025-032). Informed consent was obtained from all participants throughout the research process. All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. Consent for publication: Not applicable. Funding: This study received no specific funding. Author Contribution Yujie Wang: Conceptualization , Data curation , Formal analysis , Writing – original draft , Writing – review & editing , Supervision , Correspondence .Xiaofu Zeng: Data curation , Investigation , Writing – review & editing .Liangliang Qing: Data curation, Validation, Resources .Guangqing Fu: Supervision , Methodology, Writing – review & editing .Dengjun Han: Supervision , Writing – review & editing .Dayong Ye: Supervision , Resources , Writing – review & editing . Acknowledgments: Not applicable. 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Shore ND, Drake CG, Lin DW, Ryan CJ, Stratton KL, Dunshee C, et al. Optimizing the management of castration-resistant prostate cancer patients: A practical guide for clinicians. Prostate. 2020;80(14):1159–76. Morlacco A, Modonutti D, Motterle G, Martino F, Dal Moro F, Novara G. Nomograms in Urologic Oncology: Lights and Shadows. J Clin Med. 2021;10(5). Heidenreich A, Bastian PJ, Bellmunt J, Bolla M, Joniau S, van der Kwast T, et al. EAU guidelines on prostate cancer. Part II: Treatment of advanced, relapsing, and castration-resistant prostate cancer. Eur Urol. 2014;65(2):467–79. Goto K, Kobatake K, Fukuoka K, Kagiyama Y, Hatayama T, Kirishima F, et al. Risk classification by pathological and biochemical prognostic factors determined by extensive exploration for metastatic hormone sensitive prostate cancer. World J Urol. 2025;43(1):483. Hatano K, Nonomura N. Genomic Profiling of Prostate Cancer: An Updated Review. World J Mens Health. 2022;40(3):368–79. Kadena S, Urabe F, Iwatani K, Suzuki H, Imai Y, Tashiro K, et al. The prognostic significance of the clinical T stage and Grade Group in patients with locally advanced prostate cancer treated via high-dose-rate brachytherapy and external beam radiation. Int J Clin Oncol. 2023;28(8):1092–100. Chawla S, Rockstroh A, Lehman M, Ratther E, Jain A, Anand A, et al. Gene expression based inference of cancer drug sensitivity. Nat Commun. 2022;13(1):5680. Lin TT, Chen YH, Wu YP, Chen SZ, Li XD, Lin YZ, et al. Risk factors for progression to castration-resistant prostate cancer in metastatic prostate cancer patients. J Cancer. 2019;10(22):5608–13. Tian S, Lei Z, Gong Z, Sun Z, Xu D, Piao M. Clinical implication of prognostic and predictive biomarkers for castration-resistant prostate cancer: a systematic review. Cancer Cell Int. 2020;20:409. Cheng Q, Butler W, Zhou Y, Zhang H, Tang L, Perkinson K, et al. Pre-existing Castration-resistant Prostate Cancer-like Cells in Primary Prostate Cancer Promote Resistance to Hormonal Therapy. Eur Urol. 2022;81(5):446–55. Jeong JH, Park SJ, Dickinson SI, Luo JL. A Constitutive Intrinsic Inflammatory Signaling Circuit Composed of miR-196b, Meis2, PPP3CC, and p65 Drives Prostate Cancer Castration Resistance. Mol Cell. 2017;65(1):154–67. Guan Y, Xiong H, Feng Y, Liao G, Tong T, Pang J. Revealing the prognostic landscape of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in metastatic castration-resistant prostate cancer patients treated with abiraterone or enzalutamide: a meta-analysis. Prostate Cancer Prostatic Dis. 2020;23(2):220–31. Şahin E, Kefeli U, Zorlu Ş, Seyyar M, Ozkorkmaz Akdag M, Can Sanci P, et al. Prognostic role of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index, and pan-immune-inflammation value in metastatic castration-resistant prostate cancer patients who underwent 177Lu-PSMA-617. Med (Baltim). 2023;102(47):e35843. Kawahara T, Kato M, Tabata K, Kojima I, Yamada H, Kamihira O, et al. A high neutrophil-to-lymphocyte ratio is a poor prognostic factor for castration-resistant prostate cancer patients who undergo abiraterone acetate or enzalutamide treatment. BMC Cancer. 2020;20(1):919. Zhang Y, Zhou X, Xu R, Luo G, Wang X. Neutrophil-to-lymphocyte ratio as a prognostic factor in patients with castration-resistant prostate cancer treated with docetaxel-based chemotherapy: a meta-analysis. BMC Urol. 2025;25(1):17. Saad F, Hussain MHA, Tombal B, Fizazi K, Sternberg CN, Crawford ED, et al. Deep and Durable Prostate-specific Antigen Response to Darolutamide with Androgen Deprivation Therapy and Docetaxel, and Association with Clinical Outcomes for Patients with High- or Low-volume Metastatic Hormone-sensitive Prostate Cancer: Analyses of the Randomized Phase 3 ARASENS Study. Eur Urol. 2024;86(4):329–39. Fizazi K, Foulon S, Carles J, Roubaud G, McDermott R, Fléchon A, et al. Abiraterone plus prednisone added to androgen deprivation therapy and docetaxel in de novo metastatic castration-sensitive prostate cancer (PEACE-1): a multicentre, open-label, randomised, phase 3 study with a 2 × 2 factorial design. Lancet. 2022;399(10336):1695–707. Yang W, Chen H, Ma L, Dong J, Wei M, Xue X, et al. SHOX2 promotes prostate cancer proliferation and metastasis through disruption of the Hippo-YAP pathway. iScience. 2023;26(9):107617. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 30 Oct, 2025 Editor invited by journal 09 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 07 Oct, 2025 First submitted to journal 28 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7733342","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542276180,"identity":"11f81350-429e-4cc3-9619-16669e721522","order_by":0,"name":"Yujie Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACNvbmg49//rOx45d/fIA4LXw8x5KNGdjSkiUb0hKI0yInkWMmzcB2mHFDQ44BkQ5jSEuQLuA5zGzAcObjjTcMdnK6DQS1HD5gPEMinc+csXez5RyGZGOzA4S0MLYlJPAYWDNbNvNuk+ZhOJC4jaAWZh6DAzwJzIwbjvE8I1ILG49hM88BZ8YNZ3jYiNTCw5bMOLMBGMgz2Iwt5xgQ4Rf5+Y+P//jYAIxKCeaHN95U2MkR1IICJHiIjBpkLaTqGAWjYBSMghEBAOO0Pj1GbPt5AAAAAElFTkSuQmCC","orcid":"","institution":"Zigong Fourth People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Wang","suffix":""},{"id":542276181,"identity":"3ad8ef65-8e37-4a75-95ce-bd14f6606d5d","order_by":1,"name":"Xiaofu Zeng","email":"","orcid":"","institution":"Zigong Fourth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaofu","middleName":"","lastName":"Zeng","suffix":""},{"id":542276184,"identity":"52066409-862e-426e-8458-f702621e9803","order_by":2,"name":"Liangliang Qing","email":"","orcid":"","institution":"Zigong Fourth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liangliang","middleName":"","lastName":"Qing","suffix":""},{"id":542276189,"identity":"5f79663e-d635-4ff3-b2f8-aabb676086c0","order_by":3,"name":"Guangqing Fu","email":"","orcid":"","institution":"Zigong Fourth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangqing","middleName":"","lastName":"Fu","suffix":""},{"id":542276191,"identity":"8a862386-477e-49f6-81dd-d9225e1d2c71","order_by":4,"name":"Dengjun Han","email":"","orcid":"","institution":"Zigong Fourth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dengjun","middleName":"","lastName":"Han","suffix":""},{"id":542276194,"identity":"390f9c08-dd40-4d5b-a3d9-7e2be908dbe3","order_by":5,"name":"Dayong Ye","email":"","orcid":"","institution":"Zigong Fourth People's 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02:19:58","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129016,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7733342/v1/66fa3824d9cd08bc61f34324.html"},{"id":95691452,"identity":"f4a59888-81f0-4dfc-80e5-83ec95258996","added_by":"auto","created_at":"2025-11-12 02:19:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45117,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan–Meier curves showing PFS stratified by: (a) Gleason score, (b) clinical T stage, (c) nPSA level, and (d) TTN. Log-rank tests revealed statistically significant differences between all stratified groups (all P \u0026lt; 0.001).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7733342/v1/15198b5200e6be137b631fdf.png"},{"id":95798725,"identity":"f2d555c1-6c04-4208-80c8-bde21a0f7e55","added_by":"auto","created_at":"2025-11-13 08:17:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNomogram for predicting the 2-year risk of mCRPC. Points are assigned for each of the four predictors: Gleason score, clinical T stage, nPSA, and TTN. The total points are summed and projected to the bottom scale to estimate the individual probability of progression to mCRPC within 24 months.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7733342/v1/52c4e98cc405d9fd7627c6e5.png"},{"id":95691450,"identity":"81eacd22-3123-4233-abee-c2f7b6a6e04b","added_by":"auto","created_at":"2025-11-12 02:19:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(a) ROC curve of the nomogram based on four independent risk factors. The AUC is 0.894 (95% CI: 0.851–0.938), demonstrating strong discriminatory performance. (b) Calibration curves comparing nomogram-predicted probabilities with observed probabilities of CRPC after treatment with AA plus ADT. The apparent, bias-corrected (from 1000 bootstrap repetitions), and ideal curves are shown. The mean absolute error is 0.018 (n = 208). (c) Decision curve analysis showing the net benefit of the nomogram across threshold probabilities, compared to “treat all” and “treat none” strategies. The x-axis also indicates cost-benefit ratios.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7733342/v1/c95c0c8cb13c091071e243c3.png"},{"id":95804574,"identity":"0db07d3c-3396-41ca-8e59-fb6693dc6992","added_by":"auto","created_at":"2025-11-13 08:38:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1594829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7733342/v1/03f9ae49-f6e7-4482-903c-36dc6500df72.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Early Castration-Resistant Prostate Cancer in Metastatic Prostate Cancer: Development and Internal Validation of a Nomogram for Patients Receiving Abiraterone plus Androgen-Deprivation Therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is one of the most prevalent malignancies affecting men globally. In China, the incidence rate has seen a marked increase over the past decade. Current projections indicate that by 2030, prostate cancer may ascend from being the seventh to the third most frequently diagnosed cancer in the country, with an anticipated surge of 517% in cases between 2015 and 2030[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These trends underscore the growing public health challenge posed by prostate cancer in the Chinese population. In this region, a considerable proportion of patients are diagnosed at advanced, metastatic stages[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While metastatic prostate cancer remains incurable, the advent of combination therapy\u0026mdash;namely, abiraterone acetate(AA) with androgen deprivation therapy (ADT)\u0026mdash;has significantly improved outcomes for patients with metastatic hormone-sensitive prostate cancer (mHSPC). This combined approach has demonstrated efficacy in delaying disease progression and extending overall survival in a substantial subset of these patients[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these advances, a substantial number of patients still develop metastatic castration-resistant prostate cancer (mCRPC) within a short period after initiating treatment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This early progression presents a major clinical challenge, as mCRPC is associated with limited therapeutic options and a poor prognosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The transition to castration resistance is driven by a complex interplay of clinicopathological and molecular factors. Tumors can acquire genetic alterations, adapt their microenvironment, and evolve under selective therapeutic pressure, ultimately leading to treatment resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several biomarkers have been implicated in this process, including high Gleason score, elevated prostate-specific antigen nadir (nPSA), short time to nPSA(TTN), and the presence of perineural invasion\u0026mdash;each reflecting more aggressive disease biology and a heightened risk of early resistance [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, the time to castration resistance exhibits a strong correlation with overall survival, underscoring the critical need for early identification of patients at high risk[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Emerging evidence suggests that pre-existing castration-resistant subclones may already be present at initial diagnosis, thereby emphasizing the importance of tailored treatment strategies that account for tumor heterogeneity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In line with this imperative, the 2023 Advanced Prostate Cancer Consensus Conference recommended risk-adapted approaches incorporating individual patient characteristics[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, a critical gap persists in current clinical practice: the absence of reliable tools to identify patients likely to exhibit rapid progression to mCRPC despite receiving AA in conjunction with ADT. Early prediction could facilitate personalized surveillance strategies and timely intervention, such as intensified imaging or alternative treatments[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nomograms have emerged as useful instruments for integrating multiple prognostic variables into practical, individualized risk-assessment tools [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While various prediction models have been developed for different scenarios in prostate cancer, a straightforward and validated model specifically targeting early progression to castration-resistant prostate cancer (CRPC) in men undergoing AA plus ADT, particularly those with bone-only metastases, is currently lacking.\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to achieve two primary objectives: first, to identify readily available clinical risk factors predictive of progression to mCRPC within two years; and second, to develop and internally validate a simplified nomogram for quantifying this risk. This would thereby facilitate risk-adapted monitoring, support clinical decision-making, and enable more personalized and timely treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population and Data Collection\u003c/h2\u003e\u003cp\u003e This single-center, retrospective cohort study was conducted in accordance with both institutional and international ethical standards. The study protocol received approval from the Ethics Committee of Zigong Fourth People\u0026rsquo;s Hospital (Approval No: 2025-032). Informed consent was obtained from all participants throughout the research process.\u003c/p\u003e\u003cp\u003eThis investigation included 208 patients diagnosed with prostate cancer and concomitant bone metastases, all of whom were treated at our institution between January 2019 and March 2025. Each patient underwent ultrasound-guided prostate biopsy, with histopathological evaluation confirming the presence of acinar adenocarcinoma. Comprehensive imaging studies revealed multiple metastatic lesions localized to the axial skeleton, with no evidence of visceral or extremity bone involvement at the time of the enrollment evaluation. The stringent inclusion criteria implemented ensured the formation of a clinically homogeneous cohort for analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTreatment protocol\u003c/h3\u003e\n\u003cp\u003eAll eligible patients received a standardized first-line treatment regimen consisting of daily oral AA (1000 mg) combined with prednisone (5 mg). Furthermore, medical castration therapy was administered via subcutaneous injection of goserelin (3.6 mg) every 28 days. Treatment continued until any of the following occurred: development of castration-resistant disease, unacceptable treatment-related toxicity despite optimal management, patient request for discontinuation, or reaching the pre-specified study termination date.\u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eInclusion criteria were defined a priori to ensure a clinically homogeneous cohort. All patients were required to meet the following:(1)Histopathological confirmation of prostatic adenocarcinoma.༈2༉Objective evidence of multiple metastatic lesions in the axial skeleton, as demonstrated by bone scintigraphy. Findings were to be reviewed by a board-certified radiologist.༈3༉At least one scheduled post-baseline assessment suitable for evaluating treatment response or progression. Exclusion criteria were established to mitigate confounding from coexisting conditions or prior therapies:༈1༉Any concurrent malignancy or a prior malignancy with a substantial likelihood of affecting outcome or survival.༈2༉Use of 5α-reductase inhibitors within 6 months prior to the initiation of AA therapy, to minimize prostate-specific antigen (PSA)-related measurement bias.༈3༉Receipt of systemic chemotherapy or radiotherapy during the on-study follow-up period, which could alter progression risk independently of the study regimen.༈4༉Radiographic evidence of visceral metastases (e.g., hepatic, pulmonary, or cerebral lesions) at baseline.༈5༉Documented non-adherence to AA plus ADT that precluded a meaningful assessment of treatment effect.These criteria ensured that all participants initiated the same therapeutic protocol, had bone-only metastatic disease at enrollment, and could be reliably followed for outcomes of interest.\u003c/p\u003e\u003cp\u003eComprehensive clinical information was abstracted from the electronic medical record using a standardized template and cross-checked by two independent reviewers. Variables were prespecified and grouped into the following six domains:(1)Demographics: Age at treatment initiation and body mass index (BMI, kg/m\u0026sup2;).༈2༉Tumor Characteristics: Gleason score, clinical T stage, perineural invasion (PNI) on pathology, baseline PSA(ng/mL), and alkaline phosphatase (ALP, U/L).༈3༉On-Treatment Response Metrics: nPSA (defined as the lowest PSA value observed after therapy initiation) and TTN(the interval in months from treatment start to nPSA).༈4༉Systemic Inflammation Indices: Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). For each index, values were calculated from the same complete blood count as absolute neutrophils/lymphocytes and platelets/lymphocytes, respectively.\u003c/p\u003e\u003cp\u003e(5)Metabolic and Lifestyle Factors: Diabetes mellitus status, cigarette smoking history, and alcohol consumption as recorded at baseline.༈6༉Laboratory Parameters: with a particular emphasis on hemoglobin (HGB, g/L). Baseline laboratory values were defined as the measurements closest to the start of AA plus ADT.\u003c/p\u003e\n\u003ch3\u003eFollow-up and Outcome Definition\u003c/h3\u003e\n\u003cp\u003ePatients were monitored via a standardized clinical pathway. Serum PSA levels were measured monthly following treatment initiation, concurrently with clinical assessments. Imaging was acquired in cases of rising PSA or new symptoms. Radiographic progression was adjudicated according to PCWG3 criteria by board-certified radiologists, who were blinded to clinical data whenever feasible.\u003c/p\u003e\u003cp\u003eThe primary endpoint of this study was progression to mCRPC. Progression was defined according to the 2014 European Association of Urology (EAU) criteria, which necessitate castrate serum testosterone levels (below 50 ng/dL or 1.7 nmol/L), in conjunction with objective evidence of disease progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Such evidence could be biochemical and/or radiographic.\u003c/p\u003e\u003cp\u003eRadiographic progression was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 for soft-tissue disease and by the identification of new lesions on bone scans for osseous disease. Progression-free survival (PFS) was defined as the interval from the initiation of combined abiraterone acetate and androgen deprivation therapy until the first documentation of mCRPC. Patients who did not experience progression were censored at the date of their last adequate disease assessment within the study period.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria). Data were assessed for normal distribution using the Kolmogorov-Smirnov test. Variables conforming to a normal distribution were analyzed with an independent samples t-test and reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. For variables deviating from normal distribution, the Mann-Whitney U test was employed, with findings presented as median (interquartile range). Categorical variables were reported as counts and percentages and compared using the chi-square test or Fisher's exact test when expected cell counts were small.\u003c/p\u003e\u003cp\u003eTime-to-event associations with progression to metastatic castration-resistant prostate cancer within two years were evaluated using Cox proportional hazards regression. Initially, univariable models were fitted to screen candidate predictors. Subsequently, variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were entered into a multivariable Cox model to identify independent prognostic factors. Effect sizes were expressed as hazard ratios (HR) with corresponding 95% confidence intervals (CI). Survival functions were estimated using the Kaplan\u0026ndash;Meier method, and between-group differences were tested with the log-rank test. A prediction nomogram was constructed from the final multivariable Cox model. Model discrimination was quantified using Receiver operating characteristic (ROC) curves, and calibration was assessed with calibration curves to evaluate the consistency between predicted and actual probabilities. Decision curve analysis (DCA) was utilized to assess the net clinical benefit of the nomogram.\u003c/p\u003e\u003cp\u003eAll hypothesis tests were two-sided, and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eThis study included 208 patients with prostate cancer presenting exclusively with multiple axial bone metastases. The mean age was 75.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03 years. The median time to progression to castration-resistant prostate cancer (CRPC) was 31 months (interquartile range [IQR], 14\u0026ndash;44 months). Patients were divided into two groups based on the time to CRPC progression: 80 patients (38.5%) developed mCRPC within two years and were classified as the rapid progression group (Group 1); the remaining 128 patients (61.5%) progressed after two years and were classified as the slow progression group (Group 0).\u003c/p\u003e\u003cp\u003eThe baseline characteristics of the two groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no statistically significant differences between the groups in terms of age, baseline PSA levels, HGB concentration, NLR, PLR, smoking history, alcohol consumption, or diabetes (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, baseline PSA levels did not differ significantly between the groups (P\u0026thinsp;=\u0026thinsp;0.982). This suggests that on-treatment PSA dynamics\u0026mdash;such as nPSA and TTN\u0026mdash;may serve as more reliable predictors of disease progression than initial PSA values.\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\u003ecomparison of Clinical Characteristics Between Rapid Progressors (Group 1) and Slow Progressors (Group 0).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;208)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGroup 0\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep 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(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.00(71.25-81.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.00(71.25-80.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.92(20.94\u0026ndash;24.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.54(20.83\u0026ndash;23.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.20(21.06\u0026ndash;25.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGleason Score,n(%)\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=\"char\" char=\".\" colname=\"c5\"\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\u0026le;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90(43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80(38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical T stage, n (%)\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=\"char\" char=\".\" colname=\"c5\"\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\u003eT1-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84(40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69(53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124(59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (81.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59(46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI, n (%)\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=\"char\" char=\".\" colname=\"c5\"\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84(40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49(61.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (27.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124(59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(38.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (72.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebaseline_PSA (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170(100\u0026ndash;241)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168(95\u0026ndash;239)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e172(101\u0026ndash;245)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enPSA (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07(0.02\u0026ndash;0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93(0.29\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035(0.01\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eTTN(months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(7\u0026ndash;11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5 (5\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.0 (7\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHGB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133(122\u0026ndash;142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136(120-144.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131(123-141.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80(69.25\u0026ndash;95.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.0(79.25-134.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.0 (66.0\u0026ndash;84.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.61(2.72\u0026ndash;4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.955(2.62\u0026ndash;5.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.465(2.765\u0026ndash;4.505)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122.31(98.09\u0026ndash;147.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.07(100-165.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.33(94.88-143.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esmoking, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36(28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (71.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70(87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110(85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ediabetes, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106 (82.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecrpc_time (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31(14\u0026ndash;44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5(8.0\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.0(32.25\u0026ndash;49.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eBMI\u003c/b\u003e \u003cem\u003ebody mass index\u003c/em\u003e, \u003cb\u003ePNI\u003c/b\u003e \u003cem\u003eperineural invasion\u003c/em\u003e, \u003cb\u003ePSA\u003c/b\u003e \u003cem\u003eprostate-specific antigen\u003c/em\u003e, \u003cb\u003enPSA\u003c/b\u003e \u003cem\u003eprostate-specific antigen nadir\u003c/em\u003e, \u003cb\u003eTTN\u003c/b\u003e \u003cem\u003etime to PSA nadir\u003c/em\u003e, \u003cb\u003eHGB\u003c/b\u003e \u003cem\u003ehemoglobin\u003c/em\u003e, \u003cb\u003eALP\u003c/b\u003e \u003cem\u003ealkaline phosphatase\u003c/em\u003e, \u003cb\u003eNLR\u003c/b\u003e \u003cem\u003eneutrophil-to-lymphocyte ratio\u003c/em\u003e, \u003cb\u003ePLR\u003c/b\u003e \u003cem\u003eplatelet-to-lymphocyte ratio\u003c/em\u003e, \u003cb\u003eCRPC\u003c/b\u003e \u003cem\u003ecastration-resistant prostate cancer.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e-------------------------------------\u003c/p\u003e\u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003e--------------------------------------\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRisk Factor Analysis\u003c/h3\u003e\n\u003cp\u003eA univariate Cox proportional hazards regression analysis identified that several factors were significantly associated with the time to progression to mCRPC. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, these factors included BMI with a p-value of 0.032, Gleason Score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), clinical T stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PNI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), nPSA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TTN (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ALP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the PLR with a p-value of 0.022. Each of these variables demonstrated a statistically significant correlation with the risk of progression to mCRPC.\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 and Multivariate Cox Regression Analyses for Factors Associated with Progression to mCRPC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.980\u0026thinsp;~\u0026thinsp;1.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.853\u0026thinsp;~\u0026thinsp;0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.874\u0026ndash;1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGleason Score\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e=8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.230\u0026ndash;5.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.722\u0026ndash;3.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.928\u0026ndash;13.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.248\u0026ndash;8.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical T stage\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.262\u0026ndash;6.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.168\u0026ndash;4.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.930\u0026ndash;4.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.673\u0026ndash;1.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebaseline_PSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998\u0026ndash;1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enPSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.264\u0026ndash;1.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.119\u0026ndash;1.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.817\u0026ndash;0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.801\u0026ndash;0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHGB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.986\u0026ndash;1.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.003\u0026ndash;1.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.999\u0026ndash;1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.914\u0026ndash;1.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000-1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.996\u0026ndash;1.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esmoking\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.938\u0026ndash;2.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.460\u0026ndash;1.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ediabetes\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\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.483\u0026ndash;1.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eBMI\u003c/b\u003e \u003cem\u003ebody mass index\u003c/em\u003e, \u003cb\u003ePNI\u003c/b\u003e \u003cem\u003eperineural invasion\u003c/em\u003e, \u003cb\u003ePSA\u003c/b\u003e \u003cem\u003eprostate-specific antigen\u003c/em\u003e, \u003cb\u003enPSA\u003c/b\u003e \u003cem\u003eprostate-specific antigen nadir\u003c/em\u003e, \u003cb\u003eTTN\u003c/b\u003e \u003cem\u003etime to PSA nadir\u003c/em\u003e, \u003cb\u003eHGB\u003c/b\u003e \u003cem\u003ehemoglobin\u003c/em\u003e, \u003cb\u003eALP\u003c/b\u003e \u003cem\u003ealkaline phosphatase\u003c/em\u003e, \u003cb\u003eNLR\u003c/b\u003e \u003cem\u003eneutrophil-to-lymphocyte ratio\u003c/em\u003e, \u003cb\u003ePLR\u003c/b\u003e \u003cem\u003eplatelet-to-lymphocyte ratio. Bold text indicates a statistically significant difference.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA multivariate Cox proportional hazards regression analysis was conducted using variables that demonstrated significance in the univariate analysis. This analysis identified four independent factors significantly associated with an increased risk of early progression to mCRPC: Gleason score\u0026thinsp;\u0026ge;\u0026thinsp;9 (HR: 4.334, 95% CI: 2.248\u0026ndash;8.355, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), clinical T stage T3\u0026ndash;4 (HR: 2.315, 95% CI: 1.168\u0026ndash;4.589, P\u0026thinsp;=\u0026thinsp;0.016), elevated nPSA levels (HR: 1.267, 95% CI: 1.119\u0026ndash;1.435, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and shortened TTN (HR: 0.863, 95% CI: 0.801\u0026ndash;0.930, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A comprehensive summary of these findings is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e-------------------------------------\u003c/p\u003e\u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003cp\u003e--------------------------------------\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSurvival Analysis and Nomogram Construction\u003c/h2\u003e\u003cp\u003eKaplan-Meier analysis was employed to evaluate PFS based on key prognostic variables. The analysis revealed that patients exhibiting the following characteristics experienced significantly worse PFS: Gleason score\u0026thinsp;\u0026ge;\u0026thinsp;9, clinical T stage T3\u0026ndash;4, nPSA level\u0026thinsp;\u0026gt;\u0026thinsp;0.2 ng/mL, and TTN\u0026thinsp;\u0026lt;\u0026thinsp;9 months. The Log-rank test indicated statistically significant differences for these factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUtilizing the multivariate Cox proportional hazards model, a predictive nomogram was developed to estimate the individualized 2-year risk of progression to mCRPC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This nomogram incorporates four independently significant prognostic variables: Gleason Score, clinical T stage, nPSA level,and TTN, thereby serving as a practical tool for clinical risk stratification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eROC analysis demonstrated an AUC of 0.894 (95% CI: 0.851\u0026ndash;0.938), indicating excellent discriminatory power, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. The calibration curve, validated through bootstrap resampling with 1000 iterations, showed exceptional congruence between predicted probabilities and observed outcomes, evidenced by a mean absolute error of 0.018, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. DCA was performed to assess the clinical utility of the prediction model. The results, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, indicate that the nomogram can effectively estimate the probability of progression to mCRPC in patients receiving AA in conjunction with ADT across a probability threshold ranging from 0.1 to 0.90.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMain Findings\u003c/h2\u003e\u003cp\u003eThis study successfully developed and internally validated a nomogram designed to predict the 2-year risk of progression to mCRPC in patients with metastatic prostate cancer receiving first-line AA combined with ADT. Four readily accessible clinical parameters were identified as independent prognostic factors: Gleason Score, clinical T stage, nPSA, and TTN. The nomogram demonstrated robust discriminatory capability, as evidenced by ROC analysis yielding an AUC of 0.894 (95% CI: 0.851\u0026ndash;0.938). Furthermore, the calibration curve indicated a high degree of concordance between predicted probabilities and observed outcomes, with a mean absolute error of 0.018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). These findings highlight the nomogram's excellent discriminatory power and calibration, positioning it as a valuable tool for personalized risk assessment at the initiation of treatment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eComparison with Previous Studies and Potential Mechanisms\u003c/h2\u003e\u003cp\u003eThe present findings are consistent with and extend existing evidence regarding prognostic indicators in metastatic mHSPC. A high Gleason Score (\u0026ge;\u0026thinsp;9) demonstrated a strong association with aggressive disease progression[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This aligns with literature suggesting that poorly differentiated tumors exhibit heightened genomic instability, intrinsic heterogeneity, and increased resistance to androgen receptor signaling inhibitors such as AA [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, an advanced clinical T stage (T3\u0026ndash;4) reflects locally invasive disease with a higher tumor burden and an increased risk of micrometastases, corroborating its established role as an independent predictor of early progression[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, our study underscores the superior prognostic value of on-treatment PSA kinetics, specifically nPSA and TTN, over baseline PSA levels. An elevated nPSA indicates a significant proportion of cancer cells have progressed to a castration-resistant phenotype, enabling them to evade ADT[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This observation is consistent with prior findings demonstrating a significant association between lower nPSA and longer PFS[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].Conversely, a shorter TTN was associated with a poorer prognosis, a finding that aligns with previous mainstream research[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, this observation challenges the theoretical notion linking a rapid PSA decline to enhanced survival. This discrepancy may be attributed to differences in treatment context and definitions of \u0026ldquo;rapid response.\u0026rdquo; Within the setting of AA combined with ADT, a very short TTN might reflect the rapid depletion of androgen-sensitive cells. This, in turn, could accelerate the outgrowth of pre-existing resistant clones through a process potentially involving therapy-induced lineage plasticity[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], thereby highlighting the complex interplay between tumor biology and treatment pressure.\u003c/p\u003e\u003cp\u003eInterestingly, while baseline inflammatory markers (PLR) and PNI demonstrated significance in univariate analysis, they did not retain independent prognostic value in the multivariate model. This observation suggests that their predictive utility may be overshadowed by stronger determinants, namely Gleason score, clinical T stage, and PSA kinetics. Previous studies have also reported inconsistent results regarding the prognostic role of PLR in mCRPC, Some studies suggest that a low PLR correlates with improved survival rates, particularly in patients with mCRPC treated with abiraterone[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, other studies have failed to identify a significant association between PLR and survival rates[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This discrepancy may stem from variations in patient populations, treatment modalities, and study designs. In mHSPC patients, the predictive value of PLR remains under-validated, but its role as a marker of inflammatory and coagulation status warrants further investigation.In contrast, NLR has often demonstrated more stable predictive value across different cohorts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Integrating PLR and NLR into refined inflammatory markers (such as the systemic inflammatory response index) may enhance prognostic accuracy, but this falls outside the scope of the present study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eBuilding upon this pathophysiological framework, the proposed nomogram offers a quantitative method for risk stratification at the initiation of AA plus ADT. The ROC curve analysis, comparing our nomogram to individual clinical factors, demonstrates its superior discriminative ability, thereby supporting its clinical utility. High-risk patients, characterized by high nomogram scores, may benefit from intensified monitoring, including shortened PSA testing intervals and earlier imaging. Consideration of upfront treatment intensification is also warranted. For instance, the addition of docetaxel to ADT and Androgen Receptor Signalling Inhibitors has demonstrated survival benefits in high-volume or high-risk mHSPC patients, as evidenced in the ARASENS and PEACE-1 trials[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, early transition to next-line therapies, such as Poly(ADP-ribose) polymerase inhibitors for patients with homologous recombination repair mutations or Lutetium Prostate-Specific Membrane Antigen (PSMA) therapy for eligible cases, may be warranted upon signs of progression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For low-risk patients, the model supports maintaining standard treatment with less intensive follow-up, thus optimizing resource allocation. Additionally, the model may aid in patient selection for clinical trials exploring novel combination therapies or treatment de-escalation strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eNotwithstanding these implications, this study is subject to several notable limitations that require consideration. Firstly, the retrospective, single-center design inherently introduces potential selection bias and restricts the external validity of the findings. Secondly, while the sample size is substantial, it remains moderate in scale, and the limited number of events within certain subgroups may compromise the precision of statistical estimates. Thirdly, the absence of critical molecular biomarker data, including Androgen Receptor Variant 7 (AR-V7), DNA repair gene mutations, and Phosphatase and tensin homolog (PTEN) loss, represents a significant constraint, as these factors could potentially enhance risk stratification and elucidate underlying resistance mechanisms. Fourthly, PSA dynamics are assessed at one-month intervals, yet radiological evaluations are symptom-driven, potentially introducing variability in assessing time to progression. Lastly, the proposed nomogram necessitates external validation through independent, preferably prospective, multi-center cohorts to establish its generalizability and clinical utility prior to widespread implementation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eTo address the aforementioned limitations and build upon our findings, future research should prioritize the following key areas: (1) External validation of the proposed nomogram across diverse patient populations to ensure generalizability; (2) Development of integrated predictive models that combine molecular biomarkers (derived from liquid or tissue biopsies) with conventional clinical variables to create comprehensive multi-omics-based prognostic tools; (3) Prospective evaluation of the nomogram's clinical utility and its impact on treatment decision-making processes and patient outcomes; and (4) Investigation of the underlying biological mechanisms associated with disease progression in patients exhibiting the identified risk profile, with particular emphasis on elucidating the dynamic relationship between PSA kinetics and tumor clonal evolution.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients with metastatic prostate cancer undergoing treatment with AA in conjunction with ADT, several independent risk factors significantly heighten the likelihood of early progression to mCRPC within two years. Key indicators include a Gleason Score of 9 or higher, clinical T stage T3\u0026ndash;4, elevated nPSA levels, and a shorter TTN. The developed nomogram adeptly integrates these critical variables, showcasing impressive calibration and discriminative performance in predicting individual risk. This innovative predictive model not only enhances patient stratification but also facilitates the customization of therapeutic strategies, enabling earlier interventions for high-risk patients and thereby potentially improving clinical outcomes. To validate its applicability across diverse clinical settings, external validation in various cohorts is essential to establish its utility in routine clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAA\u003c/b\u003e abiraterone acetate\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eADT\u003c/b\u003e androgen deprivation therapy\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003emHSPC\u003c/b\u003e \u003cem\u003emetastatic hormone\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003esensitive prostate cancer\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003emCRPC\u003c/b\u003e metastatic castration\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eresistant prostate cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003enPSA\u003c/b\u003e \u003cem\u003eprostate\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003especific antigen nadir\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTTN\u003c/b\u003e \u003cem\u003etime to PSA nadir\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCRPC\u003c/b\u003e castration\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eresistant prostate cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePSA\u003c/b\u003e \u003cem\u003eprostate\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003especific antigen\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e \u003cem\u003ebody mass index\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePNI\u003c/b\u003e \u003cem\u003eperineural invasion\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eALP\u003c/b\u003e \u003cem\u003ealkaline phosphatase\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e \u003cem\u003eneutrophil\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eto\u003c/em\u003e-\u003cem\u003elymphocyte ratio\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePLR\u003c/b\u003e \u003cem\u003eplatelet\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eto\u003c/em\u003e-\u003cem\u003elymphocyte ratio\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHGB\u003c/b\u003e \u003cem\u003ehemoglobin\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEAU\u003c/b\u003e European Association of Urology\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRECIST\u003c/b\u003e Response Evaluation Criteria in Solid Tumors\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePFS\u003c/b\u003e \u003cem\u003eProgression\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003efree survival\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e \u003cem\u003eHazard Ratio\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e \u003cem\u003eConfidence interval\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e \u003cem\u003eReceiver operating characteristic\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e \u003cem\u003eArea under the curve\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDCA\u003c/b\u003e \u003cem\u003eDecision curve analysis .\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003e This study was approved by the Ethics Committee of Zigong Fourth People's Hospital (Approval No: 2025-032). Informed consent was obtained from all participants throughout the research process. All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis study received no specific funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYujie Wang: Conceptualization , Data curation , Formal analysis , Writing \u0026ndash; original draft , Writing \u0026ndash; review \u0026amp; editing , Supervision , Correspondence .Xiaofu Zeng: Data curation , Investigation , Writing \u0026ndash; review \u0026amp; editing .Liangliang Qing: Data curation, Validation, Resources .Guangqing Fu: Supervision , Methodology, Writing \u0026ndash; review \u0026amp; editing .Dengjun Han: Supervision , Writing \u0026ndash; review \u0026amp; editing .Dayong Ye: Supervision , Resources , Writing \u0026ndash; review \u0026amp; editing .\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient privacy regulations but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShi Z, Lin J, Wu Y, Fu S, Wan Y, Fang Y. Burden of cancer and changing cancer spectrum among older adults in China: Trends and projections to 2030. Cancer Epidemiol. 2022;76:102068.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHealth Commission Of The People's Republic Of China N. National guidelines for diagnosis and treatment of prostate cancer 2022 in China (English version). Chin J Cancer Res. 2022;34(3):270\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAttard G, Murphy L, Clarke NW, Sachdeva A, Jones C, Hoyle A, et al. Abiraterone acetate plus prednisolone with or without enzalutamide for patients with metastatic prostate cancer starting androgen deprivation therapy: final results from two randomised phase 3 trials of the STAMPEDE platform protocol. Lancet Oncol. 2023;24(5):443\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSathianathen NJ, Oestreich MC, Brown SJ, Gupta S, Konety BR, Dahm P, et al. Abiraterone acetate in combination with androgen deprivation therapy compared to androgen deprivation therapy only for metastatic hormone-sensitive prostate cancer. Cochrane Database Syst Rev. 2020;12(12):Cd013245.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenr\u0026iacute;quez I, Roach M 3rd, Morgan TM, Bossi A, G\u0026oacute;mez JA, Abuchaibe O et al. Current and Emerging Therapies for Metastatic Castration-Resistant Prostate Cancer (mCRPC). Biomedicines. 2021;9(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen F, Fu Y, Bai G, Qiu J, Hua K. Multi-omics dissection of tumor microenvironment-mediated drug resistance: mechanisms and therapeutic reprogramming. Front Pharmacol. 2025;16:1634413.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu M, Mao Y, Guan C, Tang Z, Bao Z, Li Y, et al. Dynamic changes in PSA levels predict prognostic outcomes in prostate cancer patients undergoing androgen -deprivation therapy: A multicenter retrospective analysis. Front Oncol. 2023;13:1047388.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu B, Shu F, Liu Y, Zhu J, Wang H, Xie N, et al. Analysis of Risk Factors for Early Progression of Prostate Cancer After Initial Endocrine Therapy. J Cancer. 2023;14(4):519\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoskes M, Martinez-Fundichely A, Li W, Cohen S, Xu H, ElNaggar S, et al. Abstract 3883: Evolution of genomic and epigenomic heterogeneity in prostate cancer from tissue and liquid biopsies. Cancer Res. 2025;85(8Supple1):3883.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBournakis E, Efstathiou E, Varkaris A, Wen S, Chrisofos M, Deliveliotis C, et al. Time to castration resistance is an independent predictor of castration-resistant prostate cancer survival. Anticancer Res. 2011;31(4):1475\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang W, Wang T, Wang Y, Zhu F, Shi H, Zhang J, et al. Intratumor heterogeneity and clonal evolution revealed in castration-resistant prostate cancer by longitudinal genomic analysis. Transl Oncol. 2022;16:101311.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVirgo KS, Rumble RB, Talcott J. Initial Management of Noncastrate Advanced, Recurrent, or Metastatic Prostate Cancer: ASCO Guideline Update. J Clin Oncol. 2023;41(20):3652\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShore ND, Drake CG, Lin DW, Ryan CJ, Stratton KL, Dunshee C, et al. Optimizing the management of castration-resistant prostate cancer patients: A practical guide for clinicians. Prostate. 2020;80(14):1159\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorlacco A, Modonutti D, Motterle G, Martino F, Dal Moro F, Novara G. Nomograms in Urologic Oncology: Lights and Shadows. J Clin Med. 2021;10(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeidenreich A, Bastian PJ, Bellmunt J, Bolla M, Joniau S, van der Kwast T, et al. EAU guidelines on prostate cancer. Part II: Treatment of advanced, relapsing, and castration-resistant prostate cancer. Eur Urol. 2014;65(2):467\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoto K, Kobatake K, Fukuoka K, Kagiyama Y, Hatayama T, Kirishima F, et al. Risk classification by pathological and biochemical prognostic factors determined by extensive exploration for metastatic hormone sensitive prostate cancer. World J Urol. 2025;43(1):483.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHatano K, Nonomura N. Genomic Profiling of Prostate Cancer: An Updated Review. World J Mens Health. 2022;40(3):368\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKadena S, Urabe F, Iwatani K, Suzuki H, Imai Y, Tashiro K, et al. The prognostic significance of the clinical T stage and Grade Group in patients with locally advanced prostate cancer treated via high-dose-rate brachytherapy and external beam radiation. Int J Clin Oncol. 2023;28(8):1092\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChawla S, Rockstroh A, Lehman M, Ratther E, Jain A, Anand A, et al. Gene expression based inference of cancer drug sensitivity. Nat Commun. 2022;13(1):5680.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin TT, Chen YH, Wu YP, Chen SZ, Li XD, Lin YZ, et al. Risk factors for progression to castration-resistant prostate cancer in metastatic prostate cancer patients. J Cancer. 2019;10(22):5608\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian S, Lei Z, Gong Z, Sun Z, Xu D, Piao M. Clinical implication of prognostic and predictive biomarkers for castration-resistant prostate cancer: a systematic review. Cancer Cell Int. 2020;20:409.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng Q, Butler W, Zhou Y, Zhang H, Tang L, Perkinson K, et al. Pre-existing Castration-resistant Prostate Cancer-like Cells in Primary Prostate Cancer Promote Resistance to Hormonal Therapy. Eur Urol. 2022;81(5):446\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeong JH, Park SJ, Dickinson SI, Luo JL. A Constitutive Intrinsic Inflammatory Signaling Circuit Composed of miR-196b, Meis2, PPP3CC, and p65 Drives Prostate Cancer Castration Resistance. Mol Cell. 2017;65(1):154\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan Y, Xiong H, Feng Y, Liao G, Tong T, Pang J. Revealing the prognostic landscape of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in metastatic castration-resistant prostate cancer patients treated with abiraterone or enzalutamide: a meta-analysis. Prostate Cancer Prostatic Dis. 2020;23(2):220\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eŞahin E, Kefeli U, Zorlu Ş, Seyyar M, Ozkorkmaz Akdag M, Can Sanci P, et al. Prognostic role of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index, and pan-immune-inflammation value in metastatic castration-resistant prostate cancer patients who underwent 177Lu-PSMA-617. Med (Baltim). 2023;102(47):e35843.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawahara T, Kato M, Tabata K, Kojima I, Yamada H, Kamihira O, et al. A high neutrophil-to-lymphocyte ratio is a poor prognostic factor for castration-resistant prostate cancer patients who undergo abiraterone acetate or enzalutamide treatment. BMC Cancer. 2020;20(1):919.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhou X, Xu R, Luo G, Wang X. Neutrophil-to-lymphocyte ratio as a prognostic factor in patients with castration-resistant prostate cancer treated with docetaxel-based chemotherapy: a meta-analysis. BMC Urol. 2025;25(1):17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaad F, Hussain MHA, Tombal B, Fizazi K, Sternberg CN, Crawford ED, et al. Deep and Durable Prostate-specific Antigen Response to Darolutamide with Androgen Deprivation Therapy and Docetaxel, and Association with Clinical Outcomes for Patients with High- or Low-volume Metastatic Hormone-sensitive Prostate Cancer: Analyses of the Randomized Phase 3 ARASENS Study. Eur Urol. 2024;86(4):329\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFizazi K, Foulon S, Carles J, Roubaud G, McDermott R, Fl\u0026eacute;chon A, et al. Abiraterone plus prednisone added to androgen deprivation therapy and docetaxel in de novo metastatic castration-sensitive prostate cancer (PEACE-1): a multicentre, open-label, randomised, phase 3 study with a 2 \u0026times; 2 factorial design. Lancet. 2022;399(10336):1695\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang W, Chen H, Ma L, Dong J, Wei M, Xue X, et al. SHOX2 promotes prostate cancer proliferation and metastasis through disruption of the Hippo-YAP pathway. iScience. 2023;26(9):107617.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metastatic prostate cancer, Castration-resistant prostate cancer, Abiraterone acetate, Androgen deprivation therapy, Nomogram, Predictive model, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7733342/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7733342/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSome men with metastatic prostate cancer progress to castration-resistant disease rapidly after initiating abiraterone acetate in combination with androgen-deprivation therapy. Early risk assessment may guide subsequent follow-up and treatment decisions. This study aimed to identify factors associated with progression within two years and to develop a straightforward predictive tool for the early identification of high-risk patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective analysis of 208 men with bone-only metastatic prostate cancer who received abiraterone acetate in combination with androgen-deprivation therapy at our single institution between January 2019 and March 2025 was conducted. Univariate and multivariate Cox proportional hazards regression analyses were employed to identify predictors of progression to metastatic castration-resistant prostate cancer (mCRPC). Subsequently, a nomogram was developed based on the multivariate model and internally validated using 1,000 bootstrap resamples. The model's discrimination, calibration, and clinical utility were then assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 208 patients, 80 (38.5%) progressed to mCRPC within two years. Multivariate analysis identified four independent predictors of early progression: Gleason score of 9\u0026ndash;10 (HR\u0026thinsp;=\u0026thinsp;4.334, 95% CI: 2.248\u0026ndash;8.355), clinical T stage 3\u0026ndash;4 (HR\u0026thinsp;=\u0026thinsp;2.315, 95% CI: 1.168\u0026ndash;4.589), higher prostate-specific antigen nadir (nPSA; HR\u0026thinsp;=\u0026thinsp;1.267, 95% CI: 1.119\u0026ndash;1.435), and shorter time to nPSA(TTN;HR\u0026thinsp;=\u0026thinsp;0.863, 95% CI: 0.801\u0026ndash;0.930). In Kaplan\u0026ndash;Meier analysis, each of these factors was significantly associated with shorter progression-free survival (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.894 (95% CI: 0.851\u0026ndash;0.938), indicating the developed nomogram possesses excellent discriminatory capability. The calibration curve demonstrated high concordance between predicted and actual outcomes, with a mean absolute error of 0.018. Decision curve analysis (DCA) further confirmed the model's favorable clinical utility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePatients with higher Gleason scores (9\u0026ndash;10), advanced clinical T stage (T3\u0026ndash;4), higher nPSA, and shorter TTN are at increased risk of early castration resistance following treatment with abiraterone acetate plus androgen deprivation therapy. The developed nomogram serves as a practical tool to estimate individualized two-year progression risk, potentially facilitating tailored clinical management through intensified monitoring and timely treatment modification in high-risk individuals. External validation in prospective, multicenter cohorts is warranted before broader clinical application.\u003c/p\u003e","manuscriptTitle":"Predicting Early Castration-Resistant Prostate Cancer in Metastatic Prostate Cancer: Development and Internal Validation of a Nomogram for Patients Receiving Abiraterone plus Androgen-Deprivation Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 02:19:53","doi":"10.21203/rs.3.rs-7733342/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-20T00:32:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285266237897313328301443816476333796455","date":"2025-11-06T22:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5077091734216667460937280735191757714","date":"2025-10-31T08:35:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T20:57:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-09T09:15:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T04:25:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-07T04:24:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Urology","date":"2025-09-28T09:16:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75f1d666-cd3e-4b21-9276-36c87d475569","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T02:19:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 02:19:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7733342","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7733342","identity":"rs-7733342","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}