Establishment and Validation of a Novel Predictive Model to Quantify the Risk of Bone Metastasis in Patients with Prostate Cancer

Overview

Journal Transl Androl Urol

Date 2021 Feb 3

PMID 33532320

Citations 6

Authors

Yu-Jie Lu

Wei-Ming Duan

Affiliations

Soon will be listed here.

Abstract

Background: Patients with prostate cancer (PCa) commonly suffer from bone metastasis during disease progression. This study aims to construct and validate a nomogram to quantify bone metastasis risk in patients with PCa.

Methods: Clinicopathological data of patients diagnosed with PCa between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Predictors for bone metastasis were identified by logistic regression analyses to establish a nomogram. The concordance index (c-index) and calibration plots were generated to assess the nomogram's discrimination, and the area under the receiver operating characteristic curve (AUC) was used to compare the precision of the nomogram with routine staging systems. The nomogram's clinical performance was evaluated by decision curve analysis (DCA) and clinical impact curves (CIC). Independent prognostic factors were identified by Cox regression analysis.

Results: A total of 168,414 eligible cases were randomly assigned to the training cohort or validation cohort at a ratio of 1:1. The nomogram, which was established based on independent factors, showed good accuracy, with c-indexes of 0.911 in the training set and 0.910 in the validation set. Calibration plots also approached 45 degrees. After other distant metastatic sites were included in the predictive model, the new nomogram displayed superior prediction performance. The AUCs and net benefit of the nomograms were both higher than those of other routine staging systems. Furthermore, bone metastasis prediction points were shown to be a new risk factor for overall survival.

Conclusions: Novel validated nomograms can effectively predict the risk of bone metastasis in patients with PCa and help clinicians improve cancer management.

Citing Articles

Predicting the risk of bone metastases in patients with newly diagnosed prostate cancer - Development and validation of a nomogram model.

Xu W, Zhu G, Wang X, Yan X, Wang F, Li S PLoS One. 2025; 20(1):e0318051.

PMID: 39883745 PMC: 11781626. DOI: 10.1371/journal.pone.0318051.

Development and validation of a nomogram for predicting survival in intermediate- and high-risk neuroblastoma of the Children's Oncology Group risk stratification.

Feng L, Kan Y, Wang W, Wang C, Zhang H, Xie P J Cancer Res Clin Oncol. 2023; 149(18):16377-16390.

PMID: 37702807 DOI: 10.1007/s00432-023-05398-3.

A novel prognostic model for patients with colon adenocarcinoma.

Yin C, Wang W, Cao W, Chen Y, Sun X, He K Front Endocrinol (Lausanne). 2023; 14:1133554.

PMID: 36923226 PMC: 10009111. DOI: 10.3389/fendo.2023.1133554.

Development and validation of a nomogram for predicting the overall survival of prostate cancer patients: a large population-based cohort study.

Zhou Z, Pu J, Wei X, Huang Y, Lin Y, Wang L Transl Androl Urol. 2022; 11(9):1325-1335.

PMID: 36217401 PMC: 9547153. DOI: 10.21037/tau-22-498.

Nomogram for predicting the overall survival of patients with early-onset prostate cancer: A population-based retrospective study.

Hu Y, Qi Q, Zheng Y, Wang H, Zhou J, Hao Z Cancer Med. 2022; 11(17):3260-3271.

PMID: 35322943 PMC: 9468440. DOI: 10.1002/cam4.4694.

References

Pencina M, DAgostino R . Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004; 23(13):2109-23. DOI: 10.1002/sim.1802. View

Zhang W, Mao N, Wang Y, Xie H, Duan S, Zhang X . A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients. Eur J Radiol. 2020; 128:109020. DOI: 10.1016/j.ejrad.2020.109020. View

Sekiguchi A, Ishiyama H, Satoh T, Tabata K, Komori S, Tsumura H . 125Iodine monotherapy for Japanese men with low- and intermediate-risk prostate cancer: outcomes after 5 years of follow-up. J Radiat Res. 2013; 55(2):328-33. PMC: 3951075. DOI: 10.1093/jrr/rrt113. View

Gillessen S, Attard G, Beer T, Beltran H, Bossi A, Bristow R . Management of Patients with Advanced Prostate Cancer: The Report of the Advanced Prostate Cancer Consensus Conference APCCC 2017. Eur Urol. 2017; 73(2):178-211. DOI: 10.1016/j.eururo.2017.06.002. View

DOronzo S, Coleman R, Brown J, Silvestris F . Metastatic bone disease: Pathogenesis and therapeutic options: Up-date on bone metastasis management. J Bone Oncol. 2019; 15:004-4. PMC: 6429006. DOI: 10.1016/j.jbo.2018.10.004. View

Zhao F, Wang J, Chen M, Chen D, Ye S, Li X . Sites of synchronous distant metastases and prognosis in prostate cancer patients with bone metastases at initial diagnosis: a population-based study of 16,643 patients. Clin Transl Med. 2019; 8(1):30. PMC: 6884608. DOI: 10.1186/s40169-019-0247-4. View

Calais J, Cao M, Nickols N . The Utility of PET/CT in the Planning of External Radiation Therapy for Prostate Cancer. J Nucl Med. 2018; 59(4):557-567. PMC: 6910632. DOI: 10.2967/jnumed.117.196444. View

Vaarala M, Hirvikoski P, Kauppila S, Paavonen T . Identification of androgen-regulated genes in human prostate. Mol Med Rep. 2012; 6(3):466-72. PMC: 3493087. DOI: 10.3892/mmr.2012.956. View

Zaman M, Fatima N, Sajjad Z . Metastasis on bone scan with low prostate specific antigen (≤20 ng/ml) and Gleason's score (<8) in newly diagnosed Pakistani males with prostate cancer: should we follow Western guidelines?. Asian Pac J Cancer Prev. 2011; 12(6):1529-32. View

10.

Paner G, Stadler W, Hansel D, Montironi R, Lin D, Amin M . Updates in the Eighth Edition of the Tumor-Node-Metastasis Staging Classification for Urologic Cancers. Eur Urol. 2018; 73(4):560-569. DOI: 10.1016/j.eururo.2017.12.018. View

11.

Wu Y, Yang W, Hu Y, Hsieh C, Yang K, Lai I . Definitive Radiotherapy for Older Patients with Prostate Cancer: Experience of a Medical Center in Taiwan. Sci Rep. 2017; 7(1):13880. PMC: 5655670. DOI: 10.1038/s41598-017-13119-3. View

12.

Kerr K, Brown M, Zhu K, Janes H . Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use. J Clin Oncol. 2016; 34(21):2534-40. PMC: 4962736. DOI: 10.1200/JCO.2015.65.5654. View

13.

Kan C, Vargas G, Pape F, Clezardin P . Cancer Cell Colonisation in the Bone Microenvironment. Int J Mol Sci. 2016; 17(10). PMC: 5085707. DOI: 10.3390/ijms17101674. View

14.

Jiang W, Yuan P . Development and validation of prognostic nomograms for patients with metastatic prostate cancer. Int Urol Nephrol. 2019; 51(10):1743-1753. DOI: 10.1007/s11255-019-02224-z. View

15.

Balachandran V, Gonen M, Smith J, DeMatteo R . Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015; 16(4):e173-80. PMC: 4465353. DOI: 10.1016/S1470-2045(14)71116-7. View

16.

He C, Mao Y, Wang J, Duan F, Lin X, Li S . Nomograms predict long-term survival for patients with periampullary adenocarcinoma after pancreatoduodenectomy. BMC Cancer. 2018; 18(1):327. PMC: 5870913. DOI: 10.1186/s12885-018-4240-x. View

17.

Shou J, Zhang Q, Wang S, Zhang D . The prognosis of different distant metastases pattern in prostate cancer: A population based retrospective study. Prostate. 2018; 78(7):491-497. DOI: 10.1002/pros.23492. View

18.

Vickers A, Cronin A, Elkin E, Gonen M . Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008; 8:53. PMC: 2611975. DOI: 10.1186/1472-6947-8-53. View

19.

Wilczak W, Wittmer C, Clauditz T, Minner S, Steurer S, Buscheck F . Marked Prognostic Impact of Minimal Lymphatic Tumor Spread in Prostate Cancer. Eur Urol. 2018; 74(3):376-386. DOI: 10.1016/j.eururo.2018.05.034. View

20.

Dhom G . Classification and grading of prostatic carcinoma. Recent Results Cancer Res. 1977; (60):14-26. DOI: 10.1007/978-3-642-81095-4_3. View