Development and Internal Validation of a Prediction Model For Biochemical Recurrence Following Radical Prostatectomy

Summary

This retrospective study developed and internally validated a predictive model for biochemical recurrence after radical prostatectomy in 240 patients. Pathological stage, lymph node metastasis, positron emission tomography–positive lesions, and SUV_max were independent predictors. The model demonstrated strong discrimination, calibration, and clinical utility for postoperative risk stratification.

Abstract

This study aimed to develop and validate a predictive model for biochemical recurrence (BCR) after radical prostatectomy in patients with prostate cancer, incorporating clinical, pathological, inflammatory, and 18F-PSMA-1007 positron emission tomography/computed tomography (PET/CT) imaging parameters. This retrospective study included 240 patients with histopathologically confirmed prostate adenocarcinoma who underwent radical prostatectomy between June 2022 and July 2025. BCR was defined as a postoperative serum prostate-specific antigen (≥0.2 ng/mL). Preoperative clinical variables, systemic immune-inflammation index (SII), PET/CT lesion status, and maximum standardized uptake value (SUV_max) were collected along with postoperative pathological staging. Patients were divided into BCR (n = 64) and non-BCR (n = 176) groups. Univariate and multivariate logistic regression analyses identified independent predictors of BCR. A multivariable predictive model was developed and internally validated using a 70/30 training–validation split. Model performance was evaluated using receiver operating characteristic curves, area under the curve (AUC), calibration, and decision curve analysis. Patients with BCR showed more advanced pathological stage (pT3–4: 96.9% vs. 52.3%, p < 0.001), higher lymph node metastasis rates (59.4% vs. 29.5%, p < 0.001), higher SII (682.45 ± 118.23 vs. 637.08 ± 92.40, p = 0.002), and higher SUV_max values (6.59 ± 1.34 vs. 4.92 ± 1.49, p < 0.001). Multivariate analysis identified pathological stage (OR = 36.814, p < 0.001), lymph node status (OR = 7.286, p < 0.001), SUV_max (OR = 2.732, p < 0.001), and PET-positive lesions (OR = 27.929, p < 0.001) as independent predictors of BCR. The combined predictive model achieved excellent discrimination in training (AUC = 0.952) and validation cohorts (AUC = 0.927). Calibration curves showed agreement between predicted and observed outcomes, and decision curve analysis demonstrated superior net clinical benefit compared with treat-all and treat-none strategies. The integrated model demonstrates excellent discrimination and clinical utility, supporting individualized postoperative risk stratification.

Introduction

Prostate cancer remains one of the most common malignancies affecting men worldwide, and radical prostatectomy represents a key treatment option for patients with localized disease^1,2. Despite improvements in surgical techniques and patient selection, biochemical recurrence (BCR) occurs in approximately 20%–40% of cases, indicating persistent or recurrent disease and increased risks of metastasis and mortality^2,3. Accurate prediction of BCR after prostatectomy remains challenging, necessitating more effective prognostic tools that combine clinica....

Protocol

This study protocol was reviewed and approved by the Ethics Committee of First Affiliated Hospital of Xinjiang Medical University in accordance with the Declaration of Helsinki (Approval No.: 230608-7). Informed consent was waived for this retrospective study due to the exclusive use of de-identified patient data, which posed no potential harm or impact on patient care.

Study Design and Patient Selection
This retrospective study included 240 patients with histopathologically confirmed prostate adenocarcinoma who underwent radical prostatectomy between June 2022 and July 2025. Patients were identified through a systemati....

Results

Baseline Characteristics
Patients with BCR exhibited significantly more advanced pathological tumor stages (pT3–4: 96.9% vs. 52.3%, p < 0.001), higher rates of lymph node metastasis (59.4% vs. 29.5%, p < 0.001), elevated SII values (682.45 ± 118.23 vs. 637.08 ± 92.40, p = 0.002), and higher maximum standardized uptake values (SUV_max) on PET/CT imaging (6.59 ± 1.34 vs. 4.92 ± 1.49, p < 0.001) compared with patients without BCR. PET-positive lesio.......

Discussion

In this study, we successfully developed a comprehensive predictive model for BCR following radical prostatectomy in prostate cancer patients, integrating the SII, pathological tumor stage, lymph node status, and preoperative 18F-PSMA-1007 PET/CT imaging findings. The model demonstrated excellent discriminative performance, with an AUC of 0.95, underscoring the benefit of combining multiple biological and imaging markers for precise risk stratification. Pathological tumor stage emerged as a powerful and independent predi.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
18F-PSMA-1007	Shaanxi Zhengze Biotechnology Co., Ltd., China	18F-PSMA-1007	Radiotracer used for PET/CT imaging of prostate cancer lesions
Advantage Workstation software	GE Healthcare, Chicago, IL, USA	Version 4.7	Used for PET/CT image processing, region-of-interest definition, and SUVmax quantification
Blood analyzer	Mindray Bio-Medical Electronics Co., Ltd., China	6000PLUS	Used for measurement of neutrophil, lymphocyte, and platelet counts for SII calculation
PET/CT scanner	GE Healthcare, Chicago, IL, USA	Discovery VCT	Used for whole-body PET/CT image acquisition
ROI/image analysis software	GE Healthcare, Chicago, IL, USA	Advantage Workstation software (version 4.7)	Used for image review and quantitative lesion analysis
R software environment	R Foundation for Statistical Computing, Vienna, Austria	Version 4.2.2	Used for statistical analysis, ROC analysis, and model calibration
SPSS statistical software	IBM Corp., Armonk, NY, USA	Version 26.0	Used for statistical analysis