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Related Experiment Video

Updated: Feb 7, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
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Preoperative Multiparametric MRI-Based Tumour-Periprostatic Adipose Tissue Interface Characterisation for

Subo Zhang1,2,3, Leiming Huo1,2,3, Zhitao Zhu1,2,3

  • 1Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, Jiangsu, China.

Cancer Medicine
|February 5, 2026
PubMed
Summary
This summary is machine-generated.

Multiparametric MRI (mpMRI) features of the tumour-periprostatic adipose tissue (PPAT) interface can predict extraprostatic extension (EPE) in prostate cancer. These simplified features improve risk stratification without adding imaging complexity.

Keywords:
extraprostatic extensioninterface featuresmultiparametric magnetic resonance imagingperiprostatic adipose tissueprediction modelprostate cancer

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Accurate preoperative staging of prostate cancer is crucial for treatment planning.
  • Extraprostatic extension (EPE) is a key factor influencing treatment decisions and outcomes.
  • Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for prostate cancer assessment.

Purpose of the Study:

  • To evaluate the predictive value of tumour-periprostatic adipose tissue (PPAT) interface features on mpMRI for EPE.
  • To compare the diagnostic performance and clinical utility of a model incorporating these features against a baseline clinical model.

Main Methods:

  • Retrospective analysis of 240 patients undergoing radical prostatectomy with preoperative mpMRI.
  • Measurement of five simplified tumour-PPAT interface features on a single axial MRI slice.
  • Development of a baseline clinical model and a combined model including interface features.
  • Internal validation using bootstrap resampling and assessment of discrimination (AUC) and clinical net benefit (decision curve analysis).

Main Results:

  • The combined model demonstrated a significantly improved bias-corrected AUC of 0.823 compared to the baseline model's AUC of 0.744.
  • The combined model showed higher net clinical benefit across a range of clinically relevant threshold probabilities.
  • EPE prevalence in the cohort was 34.2%.

Conclusions:

  • Simplified tumour-PPAT interface features on mpMRI are independent predictors of EPE in prostate cancer.
  • Incorporating these features enhances preoperative risk stratification accuracy without increasing imaging complexity.
  • This approach offers improved discrimination and clinical value for surgical planning.