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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 3, 2013
Development and Internal Validation of a Prediction Model For Biochemical Recurrence Following Radical Prostatectomy
Yubin Li1, Qizhou Zhang1, Xiaohong Li2
1Department of Nuclear Medicine, First Affiliated Hospital of Xinjiang Medical University.
Journal of Visualized Experiments : Jove
|July 27, 2026
Summary
A new predictive model accurately identifies biochemical recurrence (BCR) risk after prostate cancer surgery. This model integrates clinical, pathological, and imaging data for better patient stratification.
Area of Science:
- Urology
- Oncology
- Radiology
- Medical Imaging
Background:
- Radical prostatectomy is a primary treatment for prostate cancer.
- Predicting biochemical recurrence (BCR) post-surgery is crucial for patient management.
- Existing models may not fully integrate diverse predictive factors.
Purpose of the Study:
- To develop and validate a predictive model for BCR after radical prostatectomy.
- To incorporate clinical, pathological, inflammatory, and 18F-PSMA-1007 PET/CT imaging parameters.
- To enhance individualized postoperative risk stratification for prostate cancer patients.
Main Methods:
- Retrospective analysis of 240 prostate cancer patients undergoing radical prostatectomy.
- Collection of clinical, pathological, systemic immune-inflammation index (SII), and 18F-PSMA-1007 PET/CT data (SUVmax, PET-positive lesions).
- Development and internal validation of a multivariable predictive model using logistic regression and a 70/30 split.
Main Results:
- Independent predictors of BCR included advanced pathological stage, lymph node metastasis, SII, SUVmax, and PET-positive lesions.
- The multivariable model demonstrated excellent discrimination with AUCs of 0.952 (training) and 0.927 (validation).
- The model showed good calibration and superior net clinical benefit compared to standard strategies.
Conclusions:
- An integrated predictive model combining clinical, pathological, inflammatory, and PSMA PET/CT data effectively predicts BCR after radical prostatectomy.
- The model offers high accuracy and clinical utility for individualized risk assessment.
- This approach supports tailored postoperative management strategies for prostate cancer patients.
