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Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Related Experiment Video

Updated: Apr 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
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Combined predictive model for prostate cancer screening: Development and validation study.

Yu Li1,2, Fang Yang3, Xuebin Liu1

  • 1Department of Ultrasound, Beijing Anzhen Hospital of Capital Medical University Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, Sichuan 637000, China.

European Journal of Radiology Open
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

Transrectal ultrasound (TRUS) features, combined with clinical data, significantly improve prostate cancer (PCa) detection. A developed nomogram offers a practical tool for guiding biopsy decisions with high accuracy.

Keywords:
Digital rectal examinationNomogramProstate cancerProstate-specific antigenScreeningTransrectal ultrasound

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Area of Science:

  • Urology
  • Medical Imaging
  • Oncology

Background:

  • Prostate cancer (PCa) early detection is challenging due to low specificity of prostate-specific antigen (PSA) testing and digital rectal examination (DRE).
  • Transrectal ultrasound (TRUS) is primarily used for biopsy guidance, with its potential in PCa screening underexplored.
  • Improving PCa risk stratification requires novel diagnostic approaches beyond current methods.

Purpose of the Study:

  • To evaluate TRUS-derived morphological features for PCa detection.
  • To develop a predictive nomogram integrating clinical and TRUS characteristics for enhanced PCa risk stratification.
  • To assess the diagnostic performance of the developed nomogram in independent patient cohorts.

Main Methods:

  • A cohort of patients with suspected PCa was enrolled across two tertiary centers, divided into training (n=154) and validation (n=51) groups.
  • Data collected included demographics, PSA indices (PSA density), and TRUS parameters assessed by blinded sonographers.
  • A multivariate logistic regression model was used to construct a predictive nomogram, which was then externally validated.

Main Results:

  • Independent predictors of PCa included elevated PSA density, abnormal DRE, TRUS-defined ill-defined zone boundaries, and hyper-enhancement.
  • The nomogram demonstrated strong discrimination, with a C-index of 0.933 in the training cohort and 0.907 in the validation cohort.
  • The model achieved high accuracy (86.4%), sensitivity (84.7%), and specificity (87.8%), with significant pathological concordance (kappa=0.726).

Conclusions:

  • TRUS-derived features, such as ill-defined zones and hyper-enhancement, significantly improve PCa detection when combined with clinical parameters.
  • The developed nomogram serves as a practical, visual tool to aid in biopsy decision-making.
  • The nomogram exhibits robust performance, offering a valuable enhancement for PCa risk stratification and diagnosis.