Developing a new score system for patients with PSA ranging from 4 to 20 ng/mL to improve the accuracy of PCa detection-Reference-Cited by-同舟云学术

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Developing a new score system for patients with PSA ranging from 4 to 20 ng/mL to improve the accuracy of PCa detection

Published:2016-09-05 Issue:1 Volume:5 Page:
ISSN:2193-1801
Container-title:SpringerPlus
language:en
Short-container-title:SpringerPlus

Author:

Zheng Yuxiao,Huang Yuan,Cheng Gong,Zhang Cheng,Wu Jie,Qin Chao,Hua Lixin,Wang Zengjun

Funder

Priority Academic Program Development of Jiangsu Higher Education Institution(PAPD)

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://link.springer.com/content/pdf/10.1186/s40064-016-3176-3.pdf

Reference13 articles.

1. Carlson GD, Calvanese CB, Partin AW (1998) An algorithm combining age, total prostate-specific antigen (PSA), and percent free PSA to predict prostate cancer: results on 4298 cases. Urology 52(3):455–461

2. Catalona WJ, Smith DS, Wolfert RL (1995) Evaluation of percentage of free serum prostate-specific antigen to improve specificity of prostate cancer screening. JAMA 274(15):1214–1220

3. Chavan PR, Chavan SV, Chavan NR (2009) Detection rate of prostate cancer using prostate specific antigen in patients presenting with lower urinary tract symptoms: a retrospective study. J Postgrad Med 55:17–21

4. Eastham JA, May R, Robertson JL (1999) Development of a nomogram that predicts the probability of a positive prostate biopsy in men with an abnormal digital rectal examination and a prostate-specific antigen between 0 and 4 ng/mL. Urology 54(4):709–713

5. Littrup PJ, Goodman AC, Mettlin CJ (1994) Cost analyses of prostate cancer screening: frameworks for discussion. Investigators of the American Cancer Society-National Prostate Cancer Detection project. J Urol 152(5 Pt 2):1873–1877

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1. Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone;Frontiers in Oncology;2023-06-08

2. Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis;Frontiers in Oncology;2022-08-18

3. The development and validation of a diagnostic scoring system to differentiate pulmonary tuberculosis from non-tuberculosis pulmonary infections in HIV-infected patients with severe immune suppression;BMC Infectious Diseases;2021-08-23

4. Combining Prostate Imaging Reporting and Data System, Histogram Analysis, and Prostate-Specific Antigen Density to Determine the Risk of Prostate Cancer in Patients With Prostate-Specific Antigen of 4 to 20 ng/mL;Journal of Computer Assisted Tomography;2019

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