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Updated: Jun 2, 2026
An Orthotopic Murine Model of Human Prostate Cancer Metastasis
Published on: September 18, 2013
Enhancing bone metastasis prediction in prostate cancer using quantitative mpMRI features, ISUP grade and PSA
Hasan Gündoğdu1, Kemal Panç2, Sümeyye Sekmen3
1Samsun University, Samsun, Turkey.
Purpose:
Bone metastasis is a critical complication in prostate cancer, significantly impacting patient prognosis and quality of life. This study aims to enhance bone metastasis prediction using machine learning (ML) techniques by integrating dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion features, International Society of Urological Pathology (ISUP) grade, and prostate-specific antigen (PSA) density.
Materials And Methods:
A retrospective analysis was conducted on 122 patients with histopathologically confirmed prostate cancer who underwent multiparametric prostate magnetic resonance imaging (mpMRI). Quantitative mpMRI features, PSA density, and ISUP grades were extracted and normalized. The dataset was balanced using oversampling and divided into training (70%) and test (30%) sets. Various ML models were developed and evaluated using area under the curve (AUC) metrics.
Results:
Bone metastases were present in 26 patients (21.3%) at diagnosis. IAUGC and MaxSlope showed a statistically significant association with bone metastasis (p = 0.035, p = 0.050 respectively). The optimal PSA density cut-off value of 0.24 yielded a sensitivity of 0.88, specificity of 0.60, and AUC of 0.77. Machine learning models were developed using the dataset created with IAUGC, MaxSlope, ISUP grade, and PSA density values. Among the ML models, XGBoost demonstrated superior performance with validation and test AUCs of 91.5% and 92.6%, respectively, along with high precision (93.3%) and recall (93.1%).
Conclusion:
Integrating quantitative mpMRI features, ISUP grade, and PSA density through machine learning algorithms, particularly XGBoost, significantly improves the accuracy of bone metastasis prediction in prostate cancer patients. This approach can potentially reduce the need for additional imaging modalities and associated radiation exposure.
