Nomogram for predicting the survival outcome of cabazitaxel treatment in patients with metastatic castration-resistant prostate cancer: A multi-institutional analysis

Nomogram for predicting the survival outcome of cabazitaxel treatment in patients with metastatic castration-resistant prostate cancer: A multi-institutional analysis | 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 Nomogram for predicting the survival outcome of cabazitaxel treatment in patients with metastatic castration-resistant prostate cancer: A multi-institutional analysis Kotaro Suzuki, JUNICHIRO HIRATA, HIDETO UEKI, NAOTO WAKITA, YASUYOSHI OKAMURA, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6693954/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cabazitaxel (CBZ) is the mainstay of treatment for metastatic castration-resistant prostate cancer (mCRPC). In the present study, we developed a nomogram to predict the individual survival probability after CBZ treatment in patients with mCRPC. Methods: We retrospectively analyzed 345 patients with mCRPC who started CBZ treatment between September 2019 and March 2024 and randomly divided them into a development cohort (n=230) and a validation cohort (n=115). We investigated several potential risk factors for a poor overall survival (OS) using the Cox proportional hazard model and developed a nomogram to predict the 1-year survival probability. The accuracy and discrimination ability of the nomograms were evaluated according to Harrell's concordance index (C-index) and calibration plot. Results: We developed a nomogram predicting the 1-year survival probability with predictors including ECOG-PS ≥2, presence of liver metastasis, an initial PSA ≥30 ng/mL, a PSADT ≤3 months, radiological progression of disease during docetaxel, Hb ≤12 g/dL, and LDH ≥250 U/L. C-indices of our Cox hazard model at internal validation and external validation were 0.72 and 0.67, respectively. The model was adequately calibrated, and their predictions were correlated with the observed outcomes in both cohorts. The OS was significantly different among the risk groups defined by the total points calculated from the nomogram in both cohorts. Conclusion: Our validated nomogram, which is predictive of the survival outcome after CBZ treatment in patients with mCRPC, may help in individual clinical decision-making. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Among newly diagnosed patients with prostate cancer (PCa), 5%-10% have de novo metastatic disease [ 1 ]. Most of these cases show a good response to androgen deprivation therapy (ADT); however, they eventually progress to a resistant state, generally referred to as castration-resistant PCa (CRPC) within two to three years [ 2 ]. While the prognosis of metastatic CRPC (mCRPC) has significantly improved with the advent of new-generation androgen receptor pathway inhibitors (ARSI) [ 3 – 6 ] and poly (ADP-ribose) polymerase inhibitors (PARPi) [ 7 – 9 ], it is still an invariably fatal disease. Despite the increasing number of agents, chemotherapy remains the mainstay of treatment for mCRPC. Since the survival advantage of cabazitaxel (CBZ) in mCRPC patients treated with docetaxel (DOC) was demonstrated in the TROPIC trial [ 10 ], the sequential use of CBZ following DOC has been one of the recommended treatment options for mCRPC [ 11 ]. However, compared with other non-chemotherapeutic agents, chemotherapy sometimes results in severe adverse events. In CBZ treatment, severe neutropenia was clinically significant. The TROPIC trial demonstrated that 82% of patients had grade ≥ 3 neutropenia [ 10 ]. Thus, we may need to identify the patient subgroups that are most likely to benefit from CBZ. Several previous studies have identified risk factors predicting poor survival outcomes, which may provide insights into treatment response [ 12 ]. However, it may be difficult to assess risk in individual patients in clinical practice. To address this issue, a nomogram has been proposed as a visual and quantitative assessment tool for individualized risk prediction. By integrating multiple prognostic factors, the nomogram enables the estimation of patient-specific survival probabilities, which allows clinicians to make more informed treatment decisions and provide better patient counseling. In the present study, we investigated the pre-therapeutic risk factors associated with survival outcomes in patients with mCRPC treated with CBZ and developed a nomogram to predict individual survival probabilities based on these risk factors. Patients and Methods Patients A total of 345 patients with mCRPC who started CBZ treatment following DOC treatment at Kobe University Hospital and related hospitals between September 2019 and March 2024 were enrolled in this study. The study design was approved by the Research Ethics Committee of our institution (No. B230214). The study was conducted in accordance with the Declaration of Helsinki. Treatments and procedures CBZ was injected intravenously for 3-week cycles at a dose of 25 mg/m 2 as the standard therapeutic dose, and pegfilgrastim was administered for prophylactic purposes on day two of each treatment cycle. CBZ was continued until disease progression, unacceptable adverse events, withdrawal, or death occurred. Depending on the condition of the patients, dose modification of CBZ therapy was permitted at the discretion of the treating physician in this retrospective study. We retrospectively collected the following information from the patients’ medical records: patient demographics, Eastern Cooperative Oncology Group Performance Status (ECOG-PS), blood examination results (prostate-specific antigen [PSA], hemoglobin [Hb], and lactate dehydrogenase [LDH]), therapeutic history prior to CBZ, location of metastases, and date of all-cause death. The PSA doubling time (PSADT) was calculated as previously described [13]. Statistical analyses The overall survival (OS) was estimated using the Kaplan–Meier method. We divided the study dataset into development (n=230) and validation cohorts (n=115) at a ratio of 2:1. Several potential pre-treatment factors, including the age, body mass index (BMI), presence of liver metastasis, radiological progression in prior DOC treatment, progression-free survival during DOC treatment, PSA, PSADT, Hb, and LDH, for predicting the OS with CBZ treatment in the development cohort were assessed using the Cox proportional hazards model. A nomogram was generated based on the coefficients derived from the Cox model. Each predictor variable was assigned a corresponding score, and the total score was used to estimate one-year survival probabilities. The accuracy and discrimination ability of the nomograms were evaluated according to Harrell's concordance index (C-index) and a calibration curve (1000 bootstrap resamples). An optimal cutoff value of total points was calculated to stratify the patients into low- and high-risk groups using a receiver operating characteristic (ROC) analysis. All statistical analyses were performed using the R software program (version 4.4.2) with the “rms” and “survival” packages. Each test was 2-sided, and P values <0.05 were considered statistically significant. Results Patients’ characteristics The clinical characteristics of 345 patients are summarized in Table 1. The median observation period was 12.2 months. The median age and BMI were 71 (range: 48-88) years old and 22.9 (12.6-42.5) kg/m 2 , respectively. Forty-five (13.9%) patients had an ECOG PS ≥2. The median baseline PSA value and PSADT were 36.9 (range: 1.1-8380) ng/mL and 3.5 (range: 0.4-92.3) months, respectively. Forty-one (11.9%) patients had lung metastasis, while 30 (8.7%) had liver metastasis. Regarding the treatment history, 97 patients (28.1%) underwent curative local therapy (radical prostatectomy, 45; radiation therapy, 52); 64 (18.6%) patients received more than two regimens of ARSI before CBZ treatment, and 227 (65.8%) showed radiological progression disease (rPD) during DOC treatment. The median progression-free survival during DOC was 6.5 (range 5.7-7.5) months. The median value of Hb and LDH were 11.8 (range: 4.3-15.8) g/dL and 242 (range: 115-2231) U/L, respectively. There was no significant difference in patient characteristics between the development cohort (n=230) and the validation cohort (n=115), except for the presence of liver metastasis (Table 2, 11.3% vs. 3.5%). In addition, no significant difference was observed in the OS between the two cohorts (Figure 1, median: 14.6 months vs. 14.6 months, p =0.498). Survival outcomes and prognostic markers in CBZ treatment In the development cohort, the 1-year survival rate was 59.8% (95% confidence interval [CI]: 52.9%-66.1%), and the median OS was 14.6 (95% CI: 12.3-17.4) months (Figure 1). In the analysis of the association between the clinical characteristics and the OS, ECOG-PS ≥2, presence of liver metastasis, an initial PSA ≥30 ng/mL, a PSADT ≤3 months, rPD during DOC, Hb ≤12 g/dL and a LDH ≥250 U/L were identified were identified as factors significantly associated with a poor OS, with HRs of 1.81 (95% CI 1.26-2.60, p =0.001), 1.60 (95% CI 1.04-2.46, p =0.034), 1.96 (95% CI 1.49-2.59, p <0.001), 1.63 (95% CI 1.25-2.12, p <0.001), 1.58 (95% CI 1.18-2.10, p =0.002), 1.57 (95% CI 1.20-2.06, p =0.001), and 1.65 (95% CI 1.27-2.14, p<0.001), respectively (Table 3). Nomogram for predicting the survival outcome in mCRPC patients treated with CBZ We developed a Cox regression model using the identified risk factors (Table 4). C-indices of our model with the development cohort and validation cohort were 0.72 (95% CI: 0.70-0.74) and 0.67 (95% CI: 0.60-0.73), respectively. The calibration curve with 1000 bootstrap resamples for the assessment of the discrimination ability predicting 1-year survival probabilities is shown in Figure 2. Our Cox hazard model was adequately calibrated, and its predictions correlated with the observed outcomes in both the development and validation cohorts. In addition, as shown in Figure 3, a nomogram predicting 1-year survival probability was developed based on the Cox regression model with the development cohort. Furthermore, we defined the risk group as low and high based on the calculated total points from the nomogram (Figure 4, low: <206.4 points, high: ≥206.4 points) to assess the predictive ability of the nomogram for the OS. As shown in Figure 5, the OS duration differed significantly among the risk groups in both the development (median, low: 24.0 vs. high: 9.6 months; p<0.001) and validation cohorts (median, low: 24.8 vs. high: 9.2 months; p<0.001). Discussion In the present study, we identified several risk factors associated with a poor OS, including ECOG-PS ≥ 2, presence of liver metastasis, an initial PSA ≥ 30 ng/mL, PSADT ≤ 3 months, rPD during DOC, Hb ≤ 12 g/dL, and LDH ≥ 250 U/L, and developed a nomogram for predicting the 1-year survival probability after CBZ treatment. In the recent treatment strategy for metastatic PCa, CBZ is generally administered for aggressive diseases with ARSI resistance and DOC resistance. In addition, chemotherapy, including CBZ, sometimes results in severe AEs. Thus, the prediction of the treatment efficacy and survival expectancy is important for making decisions for both attending physicians and patients. Several previous studies have reported risk factors for poor survival outcomes [ 14 – 17 ]. However, while these studies showed the survival outcomes of patient groups with certain risk factors, estimating the survival probabilities of individual cases from their results may be difficult. Nomograms help quantify risk and guide treatment choices by integrating various prognostic factors, making them an essential tool for personalized medicine. For patients identified as being at a poor risk, more intensive therapies, such as the add-on of PSMA-targeted radioligand therapy [ 18 ] and carboplatin [ 19 ] to CBZ, may be beneficial. A nomogram was developed for the TROPIC cohort [ 20 ]. However, that study included not only patients treated with CBZ but also those treated with mitoxantrone or satraplatin [ 21 ]. Thus, to the best of our knowledge, our study is the first to report a useful nomogram for CBZ following DOC treatment. In addition, given that many patients in our cohort had a history of treatment with ARSIs, our nomogram may be more representative of the current ARSI era. Although the CARD trial demonstrated that CBZ significantly improved survival outcomes in DOC- and ARSI-resistant PCa compared to alternative ARSI therapy [ 11 ], cross-resistance between DOC and CBZ is a clinical concern. Duran et al. reported that CBZ showed less affinity for the ABCB1/P-glycoprotein (P-gp) transporter than DOC, making it more effective in a DOC-resistant PCa cell model [ 22 ]. In clinical data, whether or not treatment outcomes in prior DOC affect the efficacy of CBZ has been controversial. Terada et al. reported no correlation between the PSA decrease rate and time to progression between DOC and CBZ [ 23 ]. Similarly, Kosaka et al. showed that neither PSA response nor the number of DOC treatment cycles affected the survival outcomes of CBZ [ 24 ]. In the present study, while disease progression within 6 months of DOC therapy was not an independent risk factor, PSADT ≤ 3 months and radiological progression during DOC treatment were significantly associated with poor OS after CBZ treatment. A post hoc analysis of the PROSELICA study demonstrated that pain progression at the induction of CBZ was a significant predictor of a poor prognosis [ 25 ]. In addition, a post-hoc analysis of FIRSTANA, which investigated the efficacy of CBZ in chemo-naïve mCRPC, reported that radiological tumor progression and a short PSADT were significantly associated with the survival outcomes of CBZ [ 26 ]. These findings suggest that the prognosis of CBZ treatment may be affected by the progression pattern at the initiation of CBZ treatment, not by the therapeutic outcomes of DOC. In the recent treatment strategy for PCa, genetic testing for mCRPC has been recommended for characterizing advanced PCa. Identifying not only clinical risk factors but also gene abnormalities that predict survival outcomes after CBZ treatment may help establish a more precise nomogram. However, to our knowledge, no gene mutation has been reported to be a predictive biomarker for CBZ treatment. Only a few studies have investigated the gene mutation of AR, which has been reported to be associated with the efficacy of AR-targeted therapy, in patients with mCRPC treated with CBZ. However, Conteduca et al. reported that the plasma AR copy number status did not affect the treatment efficacy of CBZ but was significantly associated with the survival outcome of AR-targeted treatment [ 27 ]. Similarly, several previous studies showed that the presence of the AR splice variant in circulating tumor cells had no significant impact on the efficacy of CBZ [ 28 , 29 ]. Further investigations are needed to identify novel genetic biomarkers that can predict the efficacy of CBZ in mCRPC. Several limitations associated with the present study warrant mention. This retrospective study involved a relatively small number of patients. In addition, our nomogram included only pretreatment parameters, but not the treatment intensity of CBZ. In clinical practice, CBZ dose modification is often performed at the discretion of the attending physician. Thus, the CBZ treatment intensity may affect the predictive ability of our nomogram in future clinical applications. Furthermore, the C-indices of our model are not ideal. The addition of unevaluated confounders to a larger cohort may increase the prognostic discrimination. Conclusion We developed and validated a nomogram to predict the one-year survival probability of CBZ treatment based on clinical risk factors. The model may aid in individual clinical decision making and but should first be further tested and updated. Declarations Disclosure of ethical statements Conflict of interest: The authors declare no conflicts of interest in association with the present study. Approval of the research protocol by an Institutional Reviewer Board: The study design was approved by the Research Ethics Committee of our institution (No. B230214). Informed consent: Informed consent was obtained in an opt-out manner. Data availability statement: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Author contributions: KS and JT designed this study. KS, JH, HU, NW, YO and TH acquired and analyzed the data. KS drafted the manuscript. TT, YH, KC, JT, and HM critically revised the manuscript for intellectual content. All authors provided final approval for the version to be published. References Helgstrand JT, Røder MA, Klemann N, Toft BG, Lichtensztajn DY et al. (2018) Trends in incidence and 5-year mortality in men with newly diagnosed, metastatic prostate cancer-A population-based analysis of 2 national cohorts. Cancer 124 (14):2931-2938. doi:10.1002/cncr.31384 Attar RM, Takimoto CH, Gottardis MM (2009) Castration-resistant prostate cancer: locking up the molecular escape routes. Clinical cancer research : an official journal of the American Association for Cancer Research 15 (10):3251-3255. doi:10.1158/1078-0432.Ccr-08-1171 de Bono JS, Logothetis CJ, Molina A, Fizazi K, North S et al. (2011) Abiraterone and increased survival in metastatic prostate cancer. The New England journal of medicine 364 (21):1995-2005. doi:10.1056/NEJMoa1014618 Scher HI, Fizazi K, Saad F, Taplin M-E, Sternberg CN et al. (2012) Increased Survival with Enzalutamide in Prostate Cancer after Chemotherapy. New England Journal of Medicine 367 (13):1187-1197. doi:doi:10.1056/NEJMoa1207506 Ryan CJ, Smith MR, Bono JSd, Molina A, Logothetis CJ et al. (2013) Abiraterone in Metastatic Prostate Cancer without Previous Chemotherapy. New England Journal of Medicine 368 (2):138-148. doi:doi:10.1056/NEJMoa1209096 Beer TM, Armstrong AJ, Rathkopf DE, Loriot Y, Sternberg CN et al. (2014) Enzalutamide in Metastatic Prostate Cancer before Chemotherapy. New England Journal of Medicine 371 (5):424-433. doi:doi:10.1056/NEJMoa1405095 Bono Jd, Mateo J, Fizazi K, Saad F, Shore N et al. (2020) Olaparib for Metastatic Castration-Resistant Prostate Cancer. New England Journal of Medicine 382 (22):2091-2102. doi:doi:10.1056/NEJMoa1911440 Saad F, Clarke NW, Oya M, Shore N, Procopio G et al. (2023) Olaparib plus abiraterone versus placebo plus abiraterone in metastatic castration-resistant prostate cancer (PROpel): final prespecified overall survival results of a randomised, double-blind, phase 3 trial. The Lancet Oncology 24 (10):1094-1108. doi:10.1016/S1470-2045(23)00382-0 Agarwal N, Azad AA, Carles J, Fay AP, Matsubara N et al. (2023) Talazoparib plus enzalutamide in men with first-line metastatic castration-resistant prostate cancer (TALAPRO-2): a randomised, placebo-controlled, phase 3 trial. The Lancet 402 (10398):291-303. doi:10.1016/S0140-6736(23)01055-3 de Bono JS, Oudard S, Ozguroglu M, Hansen S, Machiels JP et al. (2010) Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial. Lancet (London, England) 376 (9747):1147-1154. doi:10.1016/s0140-6736(10)61389-x de Wit R, de Bono J, Sternberg CN, Fizazi K, Tombal B et al. (2019) Cabazitaxel versus Abiraterone or Enzalutamide in Metastatic Prostate Cancer. The New England journal of medicine 381 (26):2506-2518. doi:10.1056/NEJMoa1911206 Yanagisawa T, Kawada T, Rajwa P, Mostafaei H, Motlagh RS et al. (2023) Sequencing impact and prognostic factors in metastatic castration-resistant prostate cancer patients treated with cabazitaxel: A systematic review and meta-analysis. Urologic oncology 41 (4):177-191. doi:10.1016/j.urolonc.2022.06.018 Arlen PM, Bianco F, Dahut WL, D'Amico A, Figg WD et al. (2008) Prostate Specific Antigen Working Group guidelines on prostate specific antigen doubling time. The Journal of urology 179 (6):2181-2185; discussion 2185-2186. doi:10.1016/j.juro.2008.01.099 Buonerba C, Pond GR, Sonpavde G, Federico P, Rescigno P et al. (2013) Potential value of Gleason score in predicting the benefit of cabazitaxel in metastatic castration-resistant prostate cancer. Future oncology (London, England) 9 (6):889-897. doi:10.2217/fon.13.39 van Soest RJ, Nieuweboer AJ, de Morrée ES, Chitu D, Bergman AM et al. (2015) The influence of prior novel androgen receptor targeted therapy on the efficacy of cabazitaxel in men with metastatic castration-resistant prostate cancer. European journal of cancer (Oxford, England : 1990) 51 (17):2562-2569. doi:10.1016/j.ejca.2015.07.037 Yokom DW, Stewart J, Alimohamed NS, Winquist E, Berry S et al. (2018) Prognostic and predictive clinical factors in patients with metastatic castration-resistant prostate cancer treated with cabazitaxel. Canadian Urological Association journal = Journal de l'Association des urologues du Canada 12 (8):E365-e372. doi:10.5489/cuaj.5108 Terada N, Kamoto T, Tsukino H, Mukai S, Akamatsu S et al. (2019) The efficacy and toxicity of cabazitaxel for treatment of docetaxel-resistant prostate cancer correlating with the initial doses in Japanese patients. BMC cancer 19 (1):156. doi:10.1186/s12885-019-5342-9 Sartor O, de Bono J, Chi KN, Fizazi K, Herrmann K et al. (2021) Lutetium-177-PSMA-617 for Metastatic Castration-Resistant Prostate Cancer. The New England journal of medicine 385 (12):1091-1103. doi:10.1056/NEJMoa2107322 Corn PG, Heath EI, Zurita A, Ramesh N, Xiao L et al. (2019) Cabazitaxel plus carboplatin for the treatment of men with metastatic castration-resistant prostate cancers: a randomised, open-label, phase 1-2 trial. The Lancet Oncology 20 (10):1432-1443. doi:10.1016/s1470-2045(19)30408-5 Halabi S, Lin CY, Small EJ, Armstrong AJ, Kaplan EB et al. (2013) Prognostic model predicting metastatic castration-resistant prostate cancer survival in men treated with second-line chemotherapy. Journal of the National Cancer Institute 105 (22):1729-1737. doi:10.1093/jnci/djt280 Sternberg CN, Petrylak DP, Sartor O, Witjes JA, Demkow T et al. (2009) Multinational, double-blind, phase III study of prednisone and either satraplatin or placebo in patients with castrate-refractory prostate cancer progressing after prior chemotherapy: the SPARC trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 27 (32):5431-5438. doi:10.1200/jco.2008.20.1228 Duran GE, Derdau V, Weitz D, Philippe N, Blankenstein J et al. (2018) Cabazitaxel is more active than first-generation taxanes in ABCB1(+) cell lines due to its reduced affinity for P-glycoprotein. Cancer chemotherapy and pharmacology 81 (6):1095-1103. doi:10.1007/s00280-018-3572-1 Terada N, Sawada A, Kawanishi H, Fujimoto T, Magaribuchi T et al. (2023) The efficacy of sequential therapy with docetaxel and cabazitaxel for castration-resistant prostate cancer: A retrospective multi-institutional study in Japan. International journal of urology : official journal of the Japanese Urological Association 30 (2):227-234. doi:10.1111/iju.15097 Kosaka T, Hongo H, Watanabe K, Mizuno R, Kikuchi E et al. (2018) No significant impact of patient age and prior treatment profile with docetaxel on the efficacy of cabazitaxel in patient with castration-resistant prostate cancer. Cancer chemotherapy and pharmacology 82 (6):1061-1066. doi:10.1007/s00280-018-3698-1 Delanoy N, Robbrecht D, Eisenberger M, Sartor O, de Wit R et al. (2021) Pain Progression at Initiation of Cabazitaxel in Metastatic Castration-Resistant Prostate Cancer (mCRPC): A Post Hoc Analysis of the PROSELICA Study. Cancers 13 (6). doi:10.3390/cancers13061284 Carrot A, Oudard S, Colomban O, Fizazi K, Maillet D et al. (2024) Prognostic Value of the Modeled Prostate-Specific Antigen KELIM Confirmation in Metastatic Castration-Resistant Prostate Cancer Treated With Taxanes in FIRSTANA. JCO clinical cancer informatics 8:e2300208. doi:10.1200/cci.23.00208 Conteduca V, Castro E, Wetterskog D, Scarpi E, Jayaram A et al. (2019) Plasma AR status and cabazitaxel in heavily treated metastatic castration-resistant prostate cancer. European journal of cancer (Oxford, England : 1990) 116:158-168. doi:10.1016/j.ejca.2019.05.007 Sieuwerts AM, Onstenk W, Kraan J, Beaufort CM, Van M et al. (2019) AR splice variants in circulating tumor cells of patients with castration-resistant prostate cancer: relation with outcome to cabazitaxel. Molecular oncology 13 (8):1795-1807. doi:10.1002/1878-0261.12529 Onstenk W, Sieuwerts AM, Kraan J, Van M, Nieuweboer AJ et al. (2015) Efficacy of Cabazitaxel in Castration-resistant Prostate Cancer Is Independent of the Presence of AR-V7 in Circulating Tumor Cells. European urology 68 (6):939-945. doi:10.1016/j.eururo.2015.07.007 Tables Table 1. Patient characteristics of entire cohort n=345 Observation periods, median (range), months 12.2 (0.7-87.1) Age, median (range), years 71 (48-88) BMI, median (range), kg/m 2 22.9 (12.6-42.5) ECOG-PS, n (%) 0 1 2 77 228 45 (22.3) (66.1) (13.9) PSA, median (range), ng/mL 36.9 (1.0-8379) PSADT, median (range), months 3.5 (0.4-92.3) Visceral metastasis, n (%) Lung Liver 41 30 (12.6) (11.3) Prior local therapy, n (%) RP RT 45 52 (12.8) (14.3) Number of prior ARSI regimens, n (%) 0 1 ≥2 93 188 64 (24.8) (57.4) (17.8) rPD during DOC treatment, n (%) 227 (67.4) PFS during DOC treatment, median (range) 6.5 (5.7-7.5) Hb, median (range), g/dL 11.8 (4.3-15.8) LDH, median (range), U/L 242 (115-2231) BMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; RP, radical prostatectomy; RT, radiation therapy; ARSI, androgen receptor signaling inhibitor; PFS, progression-free survival; DOC, docetaxel; rPD, radiological progression disease; Hb, hemoglobin; LDH, lactate dehydrogenase. Table 2. Patient characteristics of development and validation cohort n=345 Development cohort (n=230) Validation cohort (n=115) P-value Observation periods, median (range), months 12.0 (0.7-87.1) 13.2 (1.6-71.5) 0.530 Age, median (range), years 72 (48-88) 74 (52-87) 0.953 BMI, median (range), kg/m 2 22.7 (12.6-42.5) 23.0 (16.5-34.8) 0.261 ECOG-PS, n (%) 0 1 2 52 151 32 (22.6) (65.7) (13.9) 25 77 13 (21.7) (67.0) (11.3) 0.835 PSA, median (range), ng/mL 42.4 (1.0-8379) 26.5 (1.1-975.3) 0.141 PSADT, median (range), months 3.6 (1.1-26.0) 3.3 (0.4-92.3) 0.779 Visceral metastasis, n (%) Lung Liver 29 26 (12.6) (11.3) 12 4 (10.4) (3.5) 0.601 0.015 Prior local therapy, n (%) RP RT 28 33 (12.8) (14.3) 17 19 (14.8) (16.5) 0.502 0.633 Number of prior ARSI regimens, n (%) 0 1 ≥2 57 132 41 (24.8) (57.4) (17.8) 36 56 23 (31.3) (48.7) (20.0) 0.248 rPD during DOC treatment, n (%) 155 (67.4) 72 (62.6) 0.401 Hb, median (range), g/dL 11.8 (4.3-15.8) 11.5 (8.3-15.3) 0.524 LDH, median (range), U/L 241 (115-2231) 242 (115-737) 0.107 BMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; RP, radical prostatectomy; RT, radiation therapy; ARSI, androgen receptor signaling inhibitor; PFS, progression-free survival; DOC, docetaxel; rPD, radiological progression disease; Hb, hemoglobin; LDH, lactate dehydrogenase. Table 3. Univariate and multivariate analyses of factors associated with the overall survival in mCRPC patients treated with cabazitaxel Univariate analysis Multivariate analysis n=230 HR (95% CI) P -value HR (95% CI) P -value Age (≥75 vs. <75), years 1.34 (0.98-1.83) 0.065 - - BMI (<22 vs. ≥22), kg/m 2 1.16 (0.85-1.58) 0.346 - - ECOG-PS (≥2 vs. 0 or 1) 1.93 (1.28-2.91) 0.001 1.81 (1.26-2.60) 0.001 Presence of liver metastasis (Yes vs. No) 1.71 (1.09-2.68) 0.021 1.60 (1.04-2.46) 0.034 Prior curative local therapy (Yes vs. No) 0.73 (0.51-1.05) 0.089 - - iPSA (≥30 vs. <30), ng/mL 2.61 (1.87-3.64) <0.001 1.96 (1.49-2.59) 3.0), months 2.06 (1.50-2.84) <0.001 1.63 (1.25-2.12) <0.001 Number of prior ARSI regimens (2 vs. 0 or 1) 1.06 (0.71-1.60) 0.773 - - rPD during DOC (Yes vs. No) 2.00 (1.41-2.85) <0.001 1.58 (1.18-2.10) 0.002 PFS during DOC (<6 vs. ≥6), months 1.55 (1.20-2.00) 12), g/dL 1.60 (1.17-2.18) 0.004 1.57 (1.20-2.06) 0.001 LDH (≥250 vs. <250), U/L 2.33 (1.71-3.19) <0.001 1.65 (1.27-2.14) <0.001 BMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; RP, radical prostatectomy; RT, radiation therapy; ARSI, androgen receptor signaling inhibitor; DOC, docetaxel; rPD, radiological progression disease; PFS, progression-free survival; CI, confidence interval; Hb, hemoglobin; LDH, lactate dehydrogenase. Table 4. Cox hazard model used for developing nomogram Variables HR (95% CI) P -value ECOG-PS (≥2 vs. 0 or 1) 1.91 (1.24-2.94) 0.003 Presence of liver metastasis (Yes vs. No) 1.63 (1.02-2.60) 0.040 iPSA (≥30 vs. <30), ng/mL 2.09 (1.46-2.98) 3.0), months 1.79 (1.28-2.51) 12), g/dL 1.52 (1.10-2.10) 0.019 LDH (≥250 vs. <250), U/L 1.76 (1.26-2.44) <0.001 ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; DOC, docetaxel; rPD, radiological progression disease; PFS, progression-free survival; CI, confidence interval; Hb, hemoglobin; LDH, lactate dehydrogenase. Cite Share Download PDF Status: Posted Version 1 posted 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-6693954","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459255605,"identity":"b0bfc05a-61eb-4fac-8e93-c63bcb792092","order_by":0,"name":"Kotaro Suzuki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACZjYIzcbM2PjgA4jBToyWA0Caj7252XAGWC9Ba6Ba5HiOt0nzgA0hoMGcnS3x84eae3JsEokN0ja/tsnzMTMwfviYg1uLZTPbYYkDx4qNQVqMc/tuG7YxMzBLztyGW4vBYfYGiQNsCYltQC3JuT23GYFa2Jh58Wtp/nHgH0TLYcue2/ZEaGE7JnGwDaiF52BjM8OP24kEtQD9kmZxti/BmI29sZmxt+F2chszYzNev5jzHzO+UfEtQU6+mf35jx9/btvOb28++OEjPoeh8BjbwGQDbvUYWhj+4FU8CkbBKBgFIxQAAJc3UHNYexrVAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4589-9251","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":true,"prefix":"","firstName":"Kotaro","middleName":"","lastName":"Suzuki","suffix":""},{"id":459255606,"identity":"2511813a-e81f-4754-8a45-69bb7d9fad0b","order_by":1,"name":"JUNICHIRO HIRATA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"JUNICHIRO","middleName":"","lastName":"HIRATA","suffix":""},{"id":459255607,"identity":"4674784d-6f2b-4858-8524-b4523205cbb3","order_by":2,"name":"HIDETO UEKI","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"HIDETO","middleName":"","lastName":"UEKI","suffix":""},{"id":459255608,"identity":"630a8f9b-f1a7-43f3-886d-ddfcc4e774bb","order_by":3,"name":"NAOTO WAKITA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"NAOTO","middleName":"","lastName":"WAKITA","suffix":""},{"id":459255609,"identity":"6b3c0f36-ddc0-4a3e-8443-d93fe70fc03e","order_by":4,"name":"YASUYOSHI OKAMURA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"YASUYOSHI","middleName":"","lastName":"OKAMURA","suffix":""},{"id":459255610,"identity":"44de22d5-5461-44f3-bebe-f48bcbfd5efb","order_by":5,"name":"TAKUTO HARA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"TAKUTO","middleName":"","lastName":"HARA","suffix":""},{"id":459255611,"identity":"9ea5dc40-452d-4e8d-8d29-496e2cdb9eda","order_by":6,"name":"TOMOAKI TERAKAWA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"TOMOAKI","middleName":"","lastName":"TERAKAWA","suffix":""},{"id":459255612,"identity":"cd7ba196-5978-4c8d-89a2-0f6904bd0a6f","order_by":7,"name":"YOJI HYODO","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"YOJI","middleName":"","lastName":"HYODO","suffix":""},{"id":459255613,"identity":"9f961651-a3dc-4e9a-a1e8-b8ae5d7c4fa3","order_by":8,"name":"KOJI CHIBA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"KOJI","middleName":"","lastName":"CHIBA","suffix":""},{"id":459255614,"identity":"339193f1-cf3d-4a02-93f5-c4b95cd83267","order_by":9,"name":"JUN TEISHIMA","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"JUN","middleName":"","lastName":"TEISHIMA","suffix":""},{"id":459255615,"identity":"4e78761e-5f59-4ff6-afe5-b496870c3b98","order_by":10,"name":"HIDEAKI MIYAKE","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine School of Medicine: Kobe Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"HIDEAKI","middleName":"","lastName":"MIYAKE","suffix":""}],"badges":[],"createdAt":"2025-05-19 00:33:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6693954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6693954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83421321,"identity":"32b8409b-4af8-4bde-93e3-39d4c3018c0a","added_by":"auto","created_at":"2025-05-26 02:07:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1305837,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier estimates of the overall survival during CBZ treatment in the development and validation cohort.\u003c/p\u003e","description":"","filename":"IJCOSuzukietalfigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6693954/v1/137ddd6a168c6c5d13aeade4.png"},{"id":83420572,"identity":"91ce3ac6-3c35-478d-8876-26e86cd31a3f","added_by":"auto","created_at":"2025-05-26 01:51:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":976130,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of one-year survival probabilities in (A) the development and (B) validation cohort.\u003c/p\u003e","description":"","filename":"IJCOSuzukietalfigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6693954/v1/48730b02b335f80d41f25045.png"},{"id":83420566,"identity":"0db4957f-8e35-4f1e-874f-0f3b55c47cf6","added_by":"auto","created_at":"2025-05-26 01:51:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1237882,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting probability of overall survival at one year.\u003c/p\u003e","description":"","filename":"IJCOSuzukietalfigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6693954/v1/8c4a3282d2cb3356990f1785.png"},{"id":83421045,"identity":"c259f3fb-6dce-4339-ba72-bc08ecff6660","added_by":"auto","created_at":"2025-05-26 01:59:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":779608,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curve of the total points calculated from the nomogram for predicting the 1-year survival.\u003c/p\u003e","description":"","filename":"IJCOSuzukietalfigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6693954/v1/443496ce9f59f1228c639916.png"},{"id":83420574,"identity":"39c27c38-de53-48f9-b304-8c8cc552fac0","added_by":"auto","created_at":"2025-05-26 01:51:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1634499,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier estimates of the overall survival during CBZ treatment based on risk group in (A) the development and (B) validation cohort.\u003c/p\u003e","description":"","filename":"IJCOSuzukietalfigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6693954/v1/d88e6a06ea29930e31f20a37.png"},{"id":84653177,"identity":"bde2a397-fbcb-4039-b800-3300c9e54d46","added_by":"auto","created_at":"2025-06-16 00:52:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5885279,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693954/v1/fde42208-45fc-44ad-bc3b-945427460f21.pdf"}],"financialInterests":"","formattedTitle":"Nomogram for predicting the survival outcome of cabazitaxel treatment in patients with metastatic castration-resistant prostate cancer: A multi-institutional analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmong newly diagnosed patients with prostate cancer (PCa), 5%-10% have \u003cem\u003ede novo\u003c/em\u003e metastatic disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most of these cases show a good response to androgen deprivation therapy (ADT); however, they eventually progress to a resistant state, generally referred to as castration-resistant PCa (CRPC) within two to three years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the prognosis of metastatic CRPC (mCRPC) has significantly improved with the advent of new-generation androgen receptor pathway inhibitors (ARSI) [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and poly (ADP-ribose) polymerase inhibitors (PARPi) [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], it is still an invariably fatal disease.\u003c/p\u003e \u003cp\u003eDespite the increasing number of agents, chemotherapy remains the mainstay of treatment for mCRPC. Since the survival advantage of cabazitaxel (CBZ) in mCRPC patients treated with docetaxel (DOC) was demonstrated in the TROPIC trial [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the sequential use of CBZ following DOC has been one of the recommended treatment options for mCRPC [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, compared with other non-chemotherapeutic agents, chemotherapy sometimes results in severe adverse events. In CBZ treatment, severe neutropenia was clinically significant. The TROPIC trial demonstrated that 82% of patients had grade\u0026thinsp;\u0026ge;\u0026thinsp;3 neutropenia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, we may need to identify the patient subgroups that are most likely to benefit from CBZ.\u003c/p\u003e \u003cp\u003eSeveral previous studies have identified risk factors predicting poor survival outcomes, which may provide insights into treatment response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, it may be difficult to assess risk in individual patients in clinical practice. To address this issue, a nomogram has been proposed as a visual and quantitative assessment tool for individualized risk prediction. By integrating multiple prognostic factors, the nomogram enables the estimation of patient-specific survival probabilities, which allows clinicians to make more informed treatment decisions and provide better patient counseling.\u003c/p\u003e \u003cp\u003eIn the present study, we investigated the pre-therapeutic risk factors associated with survival outcomes in patients with mCRPC treated with CBZ and developed a nomogram to predict individual survival probabilities based on these risk factors.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 345 patients with mCRPC who started CBZ treatment following DOC treatment at Kobe University Hospital and related hospitals between September 2019 and March 2024 were enrolled in this study. The study design was approved by the Research Ethics Committee of our institution (No. B230214). The study was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTreatments and procedures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCBZ was injected intravenously for 3-week cycles at a dose of 25\u0026nbsp;mg/m\u003csup\u003e2\u003c/sup\u003e as the standard therapeutic dose, and pegfilgrastim was administered for prophylactic purposes on day two of each treatment cycle. CBZ was continued until disease progression, unacceptable adverse events, withdrawal, or death occurred. Depending on the condition of the patients, dose modification of CBZ therapy was permitted at the discretion of the treating physician in this retrospective study.\u003c/p\u003e\n\u003cp\u003eWe retrospectively collected the following information from the patients\u0026rsquo; medical records: patient demographics, Eastern Cooperative Oncology Group Performance Status (ECOG-PS), blood examination results (prostate-specific antigen [PSA], hemoglobin [Hb], and lactate dehydrogenase [LDH]), therapeutic history prior to CBZ, location of metastases, and date of all-cause death. The PSA doubling time (PSADT) was calculated as previously described [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe overall survival (OS) was estimated using the Kaplan\u0026ndash;Meier method. We divided the study dataset into development (n=230) and validation cohorts (n=115) at a ratio of 2:1. Several potential pre-treatment factors, including the age, body mass index (BMI), presence of liver metastasis, radiological progression in prior DOC treatment, progression-free survival during DOC treatment, PSA, PSADT, Hb, and LDH, for predicting the OS with CBZ treatment in the development cohort were assessed using the Cox proportional hazards model. A nomogram was generated based on the coefficients derived from the Cox model. Each predictor variable was assigned a corresponding score, and the total score was used to estimate one-year survival probabilities. The accuracy and discrimination ability of the nomograms were evaluated according to Harrell\u0026apos;s concordance index (C-index) and a calibration curve (1000 bootstrap resamples). An optimal cutoff value of total points was calculated to stratify the patients into low- and high-risk groups using a receiver operating characteristic (ROC) analysis.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using the R software program (version 4.4.2) with the \u0026ldquo;rms\u0026rdquo; and \u0026ldquo;survival\u0026rdquo; packages. Each test was 2-sided, and P values \u0026lt;0.05 were considered statistically significant.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatients\u0026rsquo; characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical characteristics of 345 patients are summarized in Table 1. The median observation period was 12.2 months. The median age and BMI were 71 (range: 48-88) years old and 22.9 (12.6-42.5) kg/m\u003csup\u003e2\u003c/sup\u003e, respectively. Forty-five (13.9%) patients had an ECOG PS \u0026ge;2. The median baseline PSA value and PSADT were 36.9 (range: 1.1-8380) ng/mL and 3.5 (range: 0.4-92.3) months, respectively. Forty-one (11.9%) patients had lung metastasis, while 30 (8.7%) had liver metastasis. Regarding the treatment history, 97 patients (28.1%) underwent curative local therapy (radical prostatectomy, 45; radiation therapy, 52); 64 (18.6%) patients received more than two regimens of ARSI before CBZ treatment, and 227 (65.8%) showed radiological progression disease (rPD) during DOC treatment. The median progression-free survival during DOC was 6.5 (range 5.7-7.5) months. The median value of Hb and LDH were 11.8 (range: 4.3-15.8) g/dL and 242 (range: 115-2231) U/L, respectively.\u003c/p\u003e\n\u003cp\u003eThere was no significant difference in patient characteristics between the development cohort (n=230) and the validation cohort (n=115), except for the presence of liver metastasis (Table 2, 11.3% vs. 3.5%). In addition, no significant difference was observed in the OS between the two cohorts (Figure 1, median: 14.6 months vs. 14.6 months, \u003cem\u003ep\u003c/em\u003e=0.498).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurvival outcomes and prognostic markers in CBZ treatment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the development cohort, the 1-year survival rate was 59.8% (95% confidence interval [CI]: 52.9%-66.1%), and the median OS was 14.6 (95% CI: 12.3-17.4) months (Figure 1). In the analysis of the association between the clinical characteristics and the OS, ECOG-PS \u0026ge;2, presence of liver metastasis, an initial PSA \u0026ge;30 ng/mL, a PSADT \u0026le;3 months, rPD during DOC, Hb \u0026le;12 g/dL and a LDH \u0026ge;250 U/L were identified were identified as factors significantly associated with a poor OS, with HRs of 1.81 (95% CI 1.26-2.60, \u003cem\u003ep\u003c/em\u003e=0.001), 1.60 (95% CI 1.04-2.46, \u003cem\u003ep\u003c/em\u003e=0.034), 1.96 (95% CI 1.49-2.59, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), 1.63 (95% CI 1.25-2.12, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), 1.58 (95% CI 1.18-2.10, \u003cem\u003ep\u003c/em\u003e=0.002), 1.57 (95% CI 1.20-2.06, \u003cem\u003ep\u003c/em\u003e=0.001), and 1.65 (95% CI 1.27-2.14, p\u0026lt;0.001), respectively (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNomogram for predicting the survival outcome in mCRPC patients treated with CBZ\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a Cox regression model using the identified risk factors (Table 4). C-indices of our model with the development cohort and validation cohort were 0.72 (95% CI: 0.70-0.74) and 0.67 (95% CI: 0.60-0.73), respectively. The calibration curve with 1000 bootstrap resamples for the assessment of the discrimination ability predicting 1-year survival probabilities is shown in Figure 2. Our Cox hazard model was adequately calibrated, and its predictions correlated with the observed outcomes in both the development and validation cohorts. In addition, as shown in Figure 3, a nomogram predicting 1-year survival probability was developed based on the Cox regression model with the development cohort.\u003c/p\u003e\n\u003cp\u003eFurthermore, we defined the risk group as low and high based on the calculated total points from the nomogram (Figure 4, low: \u0026lt;206.4 points, high: \u0026ge;206.4 points) to assess the predictive ability of the nomogram for the OS. As shown in Figure 5, the OS duration differed significantly among the risk groups in both the development (median, low: 24.0 vs. high: 9.6 months; p\u0026lt;0.001) and validation cohorts (median, low: 24.8 vs. high: 9.2 months; p\u0026lt;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we identified several risk factors associated with a poor OS, including ECOG-PS\u0026thinsp;\u0026ge;\u0026thinsp;2, presence of liver metastasis, an initial PSA\u0026thinsp;\u0026ge;\u0026thinsp;30 ng/mL, PSADT\u0026thinsp;\u0026le;\u0026thinsp;3 months, rPD during DOC, Hb\u0026thinsp;\u0026le;\u0026thinsp;12 g/dL, and LDH\u0026thinsp;\u0026ge;\u0026thinsp;250 U/L, and developed a nomogram for predicting the 1-year survival probability after CBZ treatment.\u003c/p\u003e \u003cp\u003eIn the recent treatment strategy for metastatic PCa, CBZ is generally administered for aggressive diseases with ARSI resistance and DOC resistance. In addition, chemotherapy, including CBZ, sometimes results in severe AEs. Thus, the prediction of the treatment efficacy and survival expectancy is important for making decisions for both attending physicians and patients. Several previous studies have reported risk factors for poor survival outcomes [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, while these studies showed the survival outcomes of patient groups with certain risk factors, estimating the survival probabilities of individual cases from their results may be difficult.\u003c/p\u003e \u003cp\u003eNomograms help quantify risk and guide treatment choices by integrating various prognostic factors, making them an essential tool for personalized medicine. For patients identified as being at a poor risk, more intensive therapies, such as the add-on of PSMA-targeted radioligand therapy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and carboplatin [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to CBZ, may be beneficial. A nomogram was developed for the TROPIC cohort [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, that study included not only patients treated with CBZ but also those treated with mitoxantrone or satraplatin [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, to the best of our knowledge, our study is the first to report a useful nomogram for CBZ following DOC treatment. In addition, given that many patients in our cohort had a history of treatment with ARSIs, our nomogram may be more representative of the current ARSI era.\u003c/p\u003e \u003cp\u003eAlthough the CARD trial demonstrated that CBZ significantly improved survival outcomes in DOC- and ARSI-resistant PCa compared to alternative ARSI therapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], cross-resistance between DOC and CBZ is a clinical concern. Duran et al. reported that CBZ showed less affinity for the ABCB1/P-glycoprotein (P-gp) transporter than DOC, making it more effective in a DOC-resistant PCa cell model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In clinical data, whether or not treatment outcomes in prior DOC affect the efficacy of CBZ has been controversial. Terada et al. reported no correlation between the PSA decrease rate and time to progression between DOC and CBZ [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, Kosaka et al. showed that neither PSA response nor the number of DOC treatment cycles affected the survival outcomes of CBZ [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the present study, while disease progression within 6 months of DOC therapy was not an independent risk factor, PSADT\u0026thinsp;\u0026le;\u0026thinsp;3 months and radiological progression during DOC treatment were significantly associated with poor OS after CBZ treatment. A post hoc analysis of the PROSELICA study demonstrated that pain progression at the induction of CBZ was a significant predictor of a poor prognosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, a post-hoc analysis of FIRSTANA, which investigated the efficacy of CBZ in chemo-na\u0026iuml;ve mCRPC, reported that radiological tumor progression and a short PSADT were significantly associated with the survival outcomes of CBZ [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings suggest that the prognosis of CBZ treatment may be affected by the progression pattern at the initiation of CBZ treatment, not by the therapeutic outcomes of DOC.\u003c/p\u003e \u003cp\u003eIn the recent treatment strategy for PCa, genetic testing for mCRPC has been recommended for characterizing advanced PCa. Identifying not only clinical risk factors but also gene abnormalities that predict survival outcomes after CBZ treatment may help establish a more precise nomogram. However, to our knowledge, no gene mutation has been reported to be a predictive biomarker for CBZ treatment. Only a few studies have investigated the gene mutation of AR, which has been reported to be associated with the efficacy of AR-targeted therapy, in patients with mCRPC treated with CBZ. However, Conteduca et al. reported that the plasma AR copy number status did not affect the treatment efficacy of CBZ but was significantly associated with the survival outcome of AR-targeted treatment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, several previous studies showed that the presence of the AR splice variant in circulating tumor cells had no significant impact on the efficacy of CBZ [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Further investigations are needed to identify novel genetic biomarkers that can predict the efficacy of CBZ in mCRPC.\u003c/p\u003e \u003cp\u003eSeveral limitations associated with the present study warrant mention. This retrospective study involved a relatively small number of patients. In addition, our nomogram included only pretreatment parameters, but not the treatment intensity of CBZ. In clinical practice, CBZ dose modification is often performed at the discretion of the attending physician. Thus, the CBZ treatment intensity may affect the predictive ability of our nomogram in future clinical applications. Furthermore, the C-indices of our model are not ideal. The addition of unevaluated confounders to a larger cohort may increase the prognostic discrimination.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed and validated a nomogram to predict the one-year survival probability of CBZ treatment based on clinical risk factors. The model may aid in individual clinical decision making and but should first be further tested and updated.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure of ethical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no conflicts of interest in association with the present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval of the research protocol by an Institutional Reviewer Board:\u003c/strong\u003e The study design was approved by the Research Ethics Committee of our institution (No. B230214).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e Informed consent was obtained in an opt-out manner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e KS and JT designed this study. KS, JH, HU, NW, YO and TH acquired and analyzed the data. KS drafted the manuscript. TT, YH, KC, JT, and HM critically revised the manuscript for intellectual content. All authors provided final approval for the version to be published.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHelgstrand JT, R\u0026oslash;der MA, Klemann N, Toft BG, Lichtensztajn DY et al. (2018) Trends in incidence and 5-year mortality in men with newly diagnosed, metastatic prostate cancer-A population-based analysis of 2 national cohorts. Cancer 124 (14):2931-2938. doi:10.1002/cncr.31384\u003c/li\u003e\n\u003cli\u003eAttar RM, Takimoto CH, Gottardis MM (2009) Castration-resistant prostate cancer: locking up the molecular escape routes. Clinical cancer research : an official journal of the American Association for Cancer Research 15 (10):3251-3255. doi:10.1158/1078-0432.Ccr-08-1171\u003c/li\u003e\n\u003cli\u003ede Bono JS, Logothetis CJ, Molina A, Fizazi K, North S et al. (2011) Abiraterone and increased survival in metastatic prostate cancer. The New England journal of medicine 364 (21):1995-2005. doi:10.1056/NEJMoa1014618\u003c/li\u003e\n\u003cli\u003eScher HI, Fizazi K, Saad F, Taplin M-E, Sternberg CN et al. (2012) Increased Survival with Enzalutamide in Prostate Cancer after Chemotherapy. New England Journal of Medicine 367 (13):1187-1197. doi:doi:10.1056/NEJMoa1207506\u003c/li\u003e\n\u003cli\u003eRyan CJ, Smith MR, Bono JSd, Molina A, Logothetis CJ et al. (2013) Abiraterone in Metastatic Prostate Cancer without Previous Chemotherapy. New England Journal of Medicine 368 (2):138-148. doi:doi:10.1056/NEJMoa1209096\u003c/li\u003e\n\u003cli\u003eBeer TM, Armstrong AJ, Rathkopf DE, Loriot Y, Sternberg CN et al. (2014) Enzalutamide in Metastatic Prostate Cancer before Chemotherapy. New England Journal of Medicine 371 (5):424-433. doi:doi:10.1056/NEJMoa1405095\u003c/li\u003e\n\u003cli\u003eBono Jd, Mateo J, Fizazi K, Saad F, Shore N et al. (2020) Olaparib for Metastatic Castration-Resistant Prostate Cancer. New England Journal of Medicine 382 (22):2091-2102. doi:doi:10.1056/NEJMoa1911440\u003c/li\u003e\n\u003cli\u003eSaad F, Clarke NW, Oya M, Shore N, Procopio G et al. (2023) Olaparib plus abiraterone versus placebo plus abiraterone in metastatic castration-resistant prostate cancer (PROpel): final prespecified overall survival results of a randomised, double-blind, phase 3 trial. The Lancet Oncology 24 (10):1094-1108. doi:10.1016/S1470-2045(23)00382-0\u003c/li\u003e\n\u003cli\u003eAgarwal N, Azad AA, Carles J, Fay AP, Matsubara N et al. (2023) Talazoparib plus enzalutamide in men with first-line metastatic castration-resistant prostate cancer (TALAPRO-2): a randomised, placebo-controlled, phase 3 trial. The Lancet 402 (10398):291-303. doi:10.1016/S0140-6736(23)01055-3\u003c/li\u003e\n\u003cli\u003ede Bono JS, Oudard S, Ozguroglu M, Hansen S, Machiels JP et al. (2010) Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial. Lancet (London, England) 376 (9747):1147-1154. doi:10.1016/s0140-6736(10)61389-x\u003c/li\u003e\n\u003cli\u003ede Wit R, de Bono J, Sternberg CN, Fizazi K, Tombal B et al. (2019) Cabazitaxel versus Abiraterone or Enzalutamide in Metastatic Prostate Cancer. The New England journal of medicine 381 (26):2506-2518. doi:10.1056/NEJMoa1911206\u003c/li\u003e\n\u003cli\u003eYanagisawa T, Kawada T, Rajwa P, Mostafaei H, Motlagh RS et al. (2023) Sequencing impact and prognostic factors in metastatic castration-resistant prostate cancer patients treated with cabazitaxel: A systematic review and meta-analysis. Urologic oncology 41 (4):177-191. doi:10.1016/j.urolonc.2022.06.018\u003c/li\u003e\n\u003cli\u003eArlen PM, Bianco F, Dahut WL, D\u0026apos;Amico A, Figg WD et al. (2008) Prostate Specific Antigen Working Group guidelines on prostate specific antigen doubling time. The Journal of urology 179 (6):2181-2185; discussion 2185-2186. doi:10.1016/j.juro.2008.01.099\u003c/li\u003e\n\u003cli\u003eBuonerba C, Pond GR, Sonpavde G, Federico P, Rescigno P et al. (2013) Potential value of Gleason score in predicting the benefit of cabazitaxel in metastatic castration-resistant prostate cancer. Future oncology (London, England) 9 (6):889-897. doi:10.2217/fon.13.39\u003c/li\u003e\n\u003cli\u003evan Soest RJ, Nieuweboer AJ, de Morr\u0026eacute;e ES, Chitu D, Bergman AM et al. (2015) The influence of prior novel androgen receptor targeted therapy on the efficacy of cabazitaxel in men with metastatic castration-resistant prostate cancer. European journal of cancer (Oxford, England : 1990) 51 (17):2562-2569. doi:10.1016/j.ejca.2015.07.037\u003c/li\u003e\n\u003cli\u003eYokom DW, Stewart J, Alimohamed NS, Winquist E, Berry S et al. (2018) Prognostic and predictive clinical factors in patients with metastatic castration-resistant prostate cancer treated with cabazitaxel. Canadian Urological Association journal = Journal de l\u0026apos;Association des urologues du Canada 12 (8):E365-e372. doi:10.5489/cuaj.5108\u003c/li\u003e\n\u003cli\u003eTerada N, Kamoto T, Tsukino H, Mukai S, Akamatsu S et al. (2019) The efficacy and toxicity of cabazitaxel for treatment of docetaxel-resistant prostate cancer correlating with the initial doses in Japanese patients. BMC cancer 19 (1):156. doi:10.1186/s12885-019-5342-9\u003c/li\u003e\n\u003cli\u003eSartor O, de Bono J, Chi KN, Fizazi K, Herrmann K et al. (2021) Lutetium-177-PSMA-617 for Metastatic Castration-Resistant Prostate Cancer. The New England journal of medicine 385 (12):1091-1103. doi:10.1056/NEJMoa2107322\u003c/li\u003e\n\u003cli\u003eCorn PG, Heath EI, Zurita A, Ramesh N, Xiao L et al. (2019) Cabazitaxel plus carboplatin for the treatment of men with metastatic castration-resistant prostate cancers: a randomised, open-label, phase 1-2 trial. The Lancet Oncology 20 (10):1432-1443. doi:10.1016/s1470-2045(19)30408-5\u003c/li\u003e\n\u003cli\u003eHalabi S, Lin CY, Small EJ, Armstrong AJ, Kaplan EB et al. (2013) Prognostic model predicting metastatic castration-resistant prostate cancer survival in men treated with second-line chemotherapy. Journal of the National Cancer Institute 105 (22):1729-1737. doi:10.1093/jnci/djt280\u003c/li\u003e\n\u003cli\u003eSternberg CN, Petrylak DP, Sartor O, Witjes JA, Demkow T et al. (2009) Multinational, double-blind, phase III study of prednisone and either satraplatin or placebo in patients with castrate-refractory prostate cancer progressing after prior chemotherapy: the SPARC trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 27 (32):5431-5438. doi:10.1200/jco.2008.20.1228\u003c/li\u003e\n\u003cli\u003eDuran GE, Derdau V, Weitz D, Philippe N, Blankenstein J et al. (2018) Cabazitaxel is more active than first-generation taxanes in ABCB1(+) cell lines due to its reduced affinity for P-glycoprotein. Cancer chemotherapy and pharmacology 81 (6):1095-1103. doi:10.1007/s00280-018-3572-1\u003c/li\u003e\n\u003cli\u003eTerada N, Sawada A, Kawanishi H, Fujimoto T, Magaribuchi T et al. (2023) The efficacy of sequential therapy with docetaxel and cabazitaxel for castration-resistant prostate cancer: A retrospective multi-institutional study in Japan. International journal of urology : official journal of the Japanese Urological Association 30 (2):227-234. doi:10.1111/iju.15097\u003c/li\u003e\n\u003cli\u003eKosaka T, Hongo H, Watanabe K, Mizuno R, Kikuchi E et al. (2018) No significant impact of patient age and prior treatment profile with docetaxel on the efficacy of cabazitaxel in patient with castration-resistant prostate cancer. Cancer chemotherapy and pharmacology 82 (6):1061-1066. doi:10.1007/s00280-018-3698-1\u003c/li\u003e\n\u003cli\u003eDelanoy N, Robbrecht D, Eisenberger M, Sartor O, de Wit R et al. (2021) Pain Progression at Initiation of Cabazitaxel in Metastatic Castration-Resistant Prostate Cancer (mCRPC): A Post Hoc Analysis of the PROSELICA Study. Cancers 13 (6). doi:10.3390/cancers13061284\u003c/li\u003e\n\u003cli\u003eCarrot A, Oudard S, Colomban O, Fizazi K, Maillet D et al. (2024) Prognostic Value of the Modeled Prostate-Specific Antigen KELIM Confirmation in Metastatic Castration-Resistant Prostate Cancer Treated With Taxanes in FIRSTANA. JCO clinical cancer informatics 8:e2300208. doi:10.1200/cci.23.00208\u003c/li\u003e\n\u003cli\u003eConteduca V, Castro E, Wetterskog D, Scarpi E, Jayaram A et al. (2019) Plasma AR status and cabazitaxel in heavily treated metastatic castration-resistant prostate cancer. European journal of cancer (Oxford, England : 1990) 116:158-168. doi:10.1016/j.ejca.2019.05.007\u003c/li\u003e\n\u003cli\u003eSieuwerts AM, Onstenk W, Kraan J, Beaufort CM, Van M et al. (2019) AR splice variants in circulating tumor cells of patients with castration-resistant prostate cancer: relation with outcome to cabazitaxel. Molecular oncology 13 (8):1795-1807. doi:10.1002/1878-0261.12529\u003c/li\u003e\n\u003cli\u003eOnstenk W, Sieuwerts AM, Kraan J, Van M, Nieuweboer AJ et al. (2015) Efficacy of Cabazitaxel in Castration-resistant Prostate Cancer Is Independent of the Presence of AR-V7 in Circulating Tumor Cells. European urology 68 (6):939-945. doi:10.1016/j.eururo.2015.07.007\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Patient characteristics of entire cohort\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"472\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003en=345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eObservation periods, median (range), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.7-87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge, median (range), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(48-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBMI, median (range), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(12.6-42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eECOG-PS, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(22.3)\u003c/p\u003e\n \u003cp\u003e(66.1)\u003c/p\u003e\n \u003cp\u003e(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePSA, median (range), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e36.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(1.0-8379)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePSADT, median (range), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.4-92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVisceral metastasis, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lung\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12.6)\u003c/p\u003e\n \u003cp\u003e(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePrior local therapy, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RP\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12.8)\u003c/p\u003e\n \u003cp\u003e(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNumber of prior ARSI regimens, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(24.8)\u003c/p\u003e\n \u003cp\u003e(57.4)\u003c/p\u003e\n \u003cp\u003e(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003erPD during DOC treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePFS during DOC treatment, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(5.7-7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHb, median (range), g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(4.3-15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eLDH, median (range), U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(115-2231)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003eBMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; RP, radical prostatectomy; RT, radiation therapy; ARSI, androgen receptor signaling inhibitor; PFS, progression-free survival; DOC, docetaxel; rPD, radiological progression disease; Hb, hemoglobin; LDH, lactate dehydrogenase.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Patient characteristics of development and validation cohort\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"719\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003en=345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDevelopment cohort (n=230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eValidation cohort (n=115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eObservation periods, median (range), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.7-87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(1.6-71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge, median (range), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(48-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(52-87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBMI, median (range), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(12.6-42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(16.5-34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eECOG-PS, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(22.6)\u003c/p\u003e\n \u003cp\u003e(65.7)\u003c/p\u003e\n \u003cp\u003e(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(21.7)\u003c/p\u003e\n \u003cp\u003e(67.0)\u003c/p\u003e\n \u003cp\u003e(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePSA, median (range), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(1.0-8379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(1.1-975.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePSADT, median (range), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(1.1-26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.4-92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVisceral metastasis, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lung\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12.6)\u003c/p\u003e\n \u003cp\u003e(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(10.4)\u003c/p\u003e\n \u003cp\u003e(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePrior local therapy, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RP\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12.8)\u003c/p\u003e\n \u003cp\u003e(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(14.8)\u003c/p\u003e\n \u003cp\u003e(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNumber of prior ARSI regimens, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(24.8)\u003c/p\u003e\n \u003cp\u003e(57.4)\u003c/p\u003e\n \u003cp\u003e(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(31.3)\u003c/p\u003e\n \u003cp\u003e(48.7)\u003c/p\u003e\n \u003cp\u003e(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003erPD during DOC treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHb, median (range), g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(4.3-15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(8.3-15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eLDH, median (range), U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(115-2231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(115-737)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 719px;\"\u003e\n \u003cp\u003eBMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; RP, radical prostatectomy; RT, radiation therapy; ARSI, androgen receptor signaling inhibitor; PFS, progression-free survival; DOC, docetaxel; rPD, radiological progression disease; Hb, hemoglobin; LDH, lactate dehydrogenase.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Univariate and multivariate analyses of factors associated with the overall survival in mCRPC patients treated with cabazitaxel\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"984\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 291px;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 291px;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003en=230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eAge (\u0026ge;75 vs. \u0026lt;75), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e(0.98-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eBMI (\u0026lt;22 vs. \u0026ge;22), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(0.85-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eECOG-PS (\u0026ge;2 vs. 0 or 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003cp\u003e(1.28-2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003cp\u003e(1.26-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003ePresence of liver metastasis (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003cp\u003e(1.09-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003cp\u003e(1.04-2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003ePrior curative local therapy (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003cp\u003e(0.51-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eiPSA (\u0026ge;30 vs. \u0026lt;30), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003cp\u003e(1.87-3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003cp\u003e(1.49-2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003ePSADT (\u0026le;3.0 vs. \u0026gt;3.0), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003cp\u003e(1.50-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003cp\u003e(1.25-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eNumber of prior ARSI regimens (2 vs. 0 or 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.71-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003erPD during DOC (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003cp\u003e(1.41-2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e(1.18-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003ePFS during DOC (\u0026lt;6 vs. \u0026ge;6), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003cp\u003e(1.20-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(0.90-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eHb (\u0026le;12 vs. \u0026gt;12), g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003cp\u003e(1.17-2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(1.20-2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eLDH (\u0026ge;250 vs. \u0026lt;250), U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003cp\u003e(1.71-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003cp\u003e(1.27-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 984px;\"\u003e\n \u003cp\u003eBMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; RP, radical prostatectomy; RT, radiation therapy; ARSI, androgen receptor signaling inhibitor; DOC, docetaxel; rPD, radiological progression disease; PFS, progression-free survival; CI, confidence interval; Hb, hemoglobin; LDH, lactate dehydrogenase.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Cox hazard model used for developing nomogram\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"645\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eECOG-PS (\u0026ge;2 vs. 0 or 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(1.24-2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003ePresence of liver metastasis (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003cp\u003e(1.02-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eiPSA (\u0026ge;30 vs. \u0026lt;30), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003cp\u003e(1.46-2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003ePSADT (\u0026le;3.0 vs. \u0026gt;3.0), months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003cp\u003e(1.28-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003erPD during DOC (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(1.07-2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eHb (\u0026le;12 vs. \u0026gt;12), g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003cp\u003e(1.10-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 356px;\"\u003e\n \u003cp\u003eLDH (\u0026ge;250 vs. \u0026lt;250), U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003cp\u003e(1.26-2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 645px;\"\u003e\n \u003cp\u003eECOG-PS, Eastern Cooperative Oncology Group Performance Status, PSADT, PSA doubling time; DOC, docetaxel; rPD, radiological progression disease; PFS, progression-free survival; CI, confidence interval; Hb, hemoglobin; LDH, lactate dehydrogenase.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6693954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6693954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCabazitaxel (CBZ) is the mainstay of treatment for metastatic castration-resistant prostate cancer (mCRPC). In the present study, we developed a nomogram to predict the individual survival probability after CBZ treatment in patients with mCRPC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe retrospectively analyzed 345 patients with mCRPC who started CBZ treatment between September 2019 and March 2024 and randomly divided them into a development cohort (n=230) and a validation cohort (n=115). We investigated several potential risk factors for a poor overall survival (OS) using the Cox proportional hazard model and developed a nomogram to predict the 1-year survival probability. The accuracy and discrimination ability of the nomograms were evaluated according to Harrell's concordance index (C-index) and calibration plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe developed a nomogram predicting the 1-year survival probability with predictors including ECOG-PS ≥2, presence of liver metastasis, an initial PSA ≥30 ng/mL, a PSADT ≤3 months, radiological progression of disease during docetaxel, Hb ≤12 g/dL, and LDH ≥250 U/L. C-indices of our Cox hazard model at internal validation and external validation were 0.72 and 0.67, respectively. The model was adequately calibrated, and their predictions were correlated with the observed outcomes in both cohorts. The OS was significantly different among the risk groups defined by the total points calculated from the nomogram in both cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our validated nomogram, which is predictive of the survival outcome after CBZ treatment in patients with mCRPC, may help in individual clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Nomogram for predicting the survival outcome of cabazitaxel treatment in patients with metastatic castration-resistant prostate cancer: A multi-institutional analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 01:51:09","doi":"10.21203/rs.3.rs-6693954/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cade0e09-9bd2-4e8b-9aab-d86cc132514f","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T00:44:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 01:51:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6693954","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6693954","identity":"rs-6693954","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}