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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following

Linda My Huynh1,2,3, Benjamin Bonebrake2, Joshua Tran3

  • 1Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA.

Journal of Clinical Medicine
|December 9, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a multiparametric magnetic resonance imaging (mpMRI)-derived radiomic model to predict prostate cancer (PC) recurrence after surgery. The novel model demonstrated superior accuracy in identifying patients at high risk for PC recurrence compared to existing methods.

Keywords:
machine learningprostate cancerradiomicsrecurrence

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Multiparametric magnetic resonance imaging (mpMRI) offers potential for noninvasive biomarkers.
  • Radiomics can identify subvisual characteristics linked to poor oncologic outcomes.
  • Predicting prostate cancer (PC) recurrence post-radical prostatectomy (RP) is crucial for patient management.

Purpose of the Study:

  • To develop and validate an MRI-derived radiomic model for predicting PC recurrence after RP.
  • To assess the performance of this radiomic model against established risk assessment scores.
  • To establish imaging-based biomarkers for identifying patients with poor oncologic outcomes.

Main Methods:

  • mpMRI data from 251 patients undergoing RP across two institutions were analyzed.
  • 924 radiomic features were extracted from prostate mpMRI, with stability tested via intraclass correlation coefficient (ICC).
  • A radiomic model was built using 14 stable, nonredundant features and validated using cross-validation and a separate test set.

Main Results:

  • The radiomic model achieved an AUC of 0.89 ± 0.04 in the training set, significantly outperforming UCSF-CAPRA (0.66 ± 0.05) and MSKCC (0.67 ± 0.05) nomograms (p < 0.01).
  • In the test set (n=26), the model showed an AUC of 0.78, with 81% overall accuracy.
  • Key performance metrics in the test set included 60% sensitivity, 86% specificity, 52% PPV, and 89% NPV.

Conclusions:

  • An MRI-derived radiomic model can accurately predict prostate cancer recurrence post-radical prostatectomy.
  • This imaging-based approach offers a noninvasive tool superior to current clinical nomograms for risk stratification.
  • Radiomics holds significant promise for identifying subvisual imaging biomarkers indicative of poor oncologic outcomes in PC.