Evidence map›Paper›PMID 26471350›Full record

ArticleScientific reports2015

Development and external validation of a prostate health index-based nomogram for predicting prostate cancer.

Yao Zhu, Cheng-Tao Han, Gui-Ming Zhang, Fang Liu, Qiang Ding, Jian-Feng Xu, Adriana C Vidal, Stephen J Freedland, Chi-Fai Ng, Ding-Wei Ye

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Trial
  2. Article
  3. The influence of age on prostate cancer screening index.Journal of clinical laboratory analysis · 2022
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yao ZhuDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China.
Cheng-Tao HanDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China.
Gui-Ming ZhangDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China.
Fang LiuState Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, P.R. China.
Qiang DingFudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, P.R. China.
Jian-Feng XuState Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, P.R. China.
Adriana C VidalDepartment of Surgery, Center for Integrated Research on Cancer and Lifestyle, Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, CA.
Stephen J FreedlandDepartment of Surgery, Center for Integrated Research on Cancer and Lifestyle, Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, CA.
Chi-Fai NgSH Ho Urology Center, Department of Surgery, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P.R. China.
Ding-Wei YeDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China.

Funding

Midcareer Investigator AwardK24CA160653 · NCI · DUKE UNIVERSITY · PI FREEDLAND, STEPHEN JAY · 2012 to 2016
$877k
NCI NIH HHS K24 CA160653
6 · The paper itself

Abstract

To develop and externally validate a prostate health index (PHI)-based nomogram for predicting the presence of prostate cancer (PCa) at biopsy in Chinese men with prostate-specific antigen 4-10 ng/mL and normal digital rectal examination (DRE). 347 men were recruited from two hospitals between 2012 and 2014 to develop a PHI-based nomogram to predict PCa. To validate these results, we used a separate cohort of 230 men recruited at another center between 2008 and 2013. Receiver operator curves (ROC) were used to assess the ability to predict PCa. A nomogram was derived from the multivariable logistic regression model and its accuracy was assessed by the area under the ROC (AUC). PHI achieved the highest AUC of 0.839 in the development cohort compared to the other predictors (p < 0.001). Including age and prostate volume, a PHI-based nomogram was constructed and rendered an AUC of 0.877 (95% CI 0.813-0.938). The AUC of the nomogram in the validation cohort was 0.786 (95% CI 0.678-0.894). In clinical effectiveness analyses, the PHI-based nomogram reduced unnecessary biopsies from 42.6% to 27% using a 5% threshold risk of PCa to avoid biopsy with no increase in the number of missed cases relative to conventional biopsy decision.

Indexed as

NomogramsAgedArea Under CurveAsian PeopleChinaCohort StudiesDigital Rectal ExaminationHumansLogistic ModelsMaleMiddle AgedMultivariate AnalysisProstateProstate-Specific AntigenProstatic NeoplasmsRiskProstate-Specific Antigen

Identifiers

PMID26471350
PMCPMC4607975

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.