JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Development and validation of a nomogram incorporating multi-parametric MRI and hematological indicators for discriminating benign from malignant central prostatic nodules: a retrospective analysis

  • 0Department of Imaging, Lianyungang First People's Hospital Lianyungang 570311, Jiangsu, China.
American Journal of Translational Research +

|

August 8, 2024

|

August 8, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new nomogram model combining MRI and blood tests can help distinguish prostate cancer from benign nodules. This tool uses apparent diffusion coefficient (ADC), standard deviation (StDev), neutrophil to lymphocyte ratio (NLR), and prostate specific antigen (PSA) for improved accuracy.

Area Of Science

  • Urology
  • Radiology
  • Oncology

Background

  • Prostate cancer (PCa) is a significant health concern for men.
  • Differentiating benign prostatic hyperplasia (BPH) from PCa is crucial for effective patient management.
  • Multi-parametric MRI and hematological values offer potential for improved diagnostic accuracy.

Purpose Of The Study

  • To develop and validate a nomogram model for differentiating benign and malignant prostatic nodules.
  • To integrate multi-parametric MRI data with hematological lab values for enhanced diagnostic capability.
  • To assess the generalizability of the developed model using an external validation set.

Main Methods

  • Retrospective analysis of 310 patients with MRI-confirmed prostatic nodules.
  • Data split into training (260 cases) and external validation (50 cases) sets.
  • Univariate and multivariate logistic regression to identify key differentiating indicators, integrated into a nomogram.

Main Results

  • Key indicators identified: apparent diffusion coefficient (ADC), standard deviation (StDev), neutrophil to lymphocyte ratio (NLR), and prostate specific antigen (PSA).
  • Nomogram achieved an area under the curve (AUC) of 0.844 in the training set and 0.818 in the external validation set.
  • The model demonstrated good calibration and clinical utility via decision curve analysis.

Conclusions

  • The developed nomogram model, incorporating ADC, StDev, NLR, and PSA, shows promise in distinguishing PCa from BPH.
  • This integrated approach offers a potentially valuable tool for clinical decision-making in prostate nodule assessment.
  • Further validation in diverse populations may enhance its clinical applicability.

Keywords: