Predictive value of a nomogram model constructed based on imaging and serological characteristics for prostate biopsy positivity in patients with PSA levels of 4-10 ng mL(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第12期
- Page:
- 1494-1500
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Predictive value of a nomogram model constructed based on imaging and serological characteristics for prostate biopsy positivity in patients with PSA levels of 4-10 ng mL
- Author(s):
- LIANG Jianchang1; WU Jinfeng2; ZHANG Yongxin2; 5; SHEN Junxin3; CHEN Zhenjie2; TAN Jian2; ZHONG Rui2; ZHAO Wei4; LU Yangbai2; YUAN Runqiang2
- 1. Department of Clinical Laboratory, Yunfu Hospital of TCM, Yunfu 527300, China 2.Department of Urology, Zhongshan City Peoples Hospital, Zhongshan 528403, China 3. The First Clinical Medical College, Guangdong Medical University, Zhanjiang 524023, China 4. School of Public Health, Southern Medical University, Guangzhou 510515, China 5. Imaging Center,Zhongshan City Peoples Hospital, Zhongshan 528403, China
- Keywords:
- Keywords: prostate cancer nomogram model prostate specific antigen predictive model imaging serology
- PACS:
- R318；R737.25
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.12.006
- Abstract:
- Abstract: Objective To investigate the predictive value of a nomogram model constructed based on imaging combined with prostate-specific antigen (PSA) and its related parameters for biopsy in patients with PSA levels of 4-10 ng/mL. Methods The serological and imaging data of 191 patients who were detected for PSA and related indicators and underwent the first biopsy of prostate by transrectal ultrasound at Zhongshan City Peoples Hospital and/or Yunfu Hospital of TCM from January 2018 to December 2023 were analyzed retrospectively. Multivariate Logistic regression identified independent risk factors for prostate cancer, and a nomogram model was developed for patients with PSA levels of 4-10 ng/mL. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results The multivariate Logistic regression analysis showed that free PSA, prostate volume, transition zone volume, PSA density, and the prostate imaging-reporting and data system (PI-RADS v2.1) score were independent risk factors for prostate cancer. The model incorporating these significant variables demonstrated the best performance, with an area under the curve (AUC) of 0.750 (95% CI: 0.678-0.821), sensitivity of 72.7%, specificity of 77.2%, and accuracy of 74.9%. The calibration curve indicated good agreement between the predicted probability and the actual probability of prostate cancer and the decision curve analysis further confirmed that the model had high clinical utility. Conclusion The constructed nomogram prediction model can effectively estimate the preoperative risk of prostate cancer in patients with PSA levels of 4-10 ng/mL, providing clinicians with an intuitive tool to adjust treatment plans based on the assessed risk, thereby optimizing patient outcomes.
Last Update: 2024-12-20