Quantitative imaging in medicine and surgery · 2026
Evaluation of MRI-based risk models versus PI-RADS version 2.1 and arterial spin labeling perfusion imaging strategy for clinically significant prostate cancer.
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Wang Wanwan, Zheng Xin, Wang Jing, Liu Nian, Cai Shengxian, Xu Yunjun
Аннотация
BACKGROUND: Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) technique that enables quantitative assessment of prostate blood flow (PBF). Although the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 is widely used for risk stratification of clinically significant prostate cancer (csPCa), whether combining ASL with the PI-RADS system can improve diagnostic accuracy remains to be explored. This study aimed to evaluate the risk of csPCa by integrating non-invasive ASL-derived PBF measurements with PI-RADS v2.1 scores in a clinical-imaging model.
METHODS: Of 233 patients with suspected prostate cancer (PCa) who underwent biparametric magnetic resonance imaging (bp-MRI) and ASL, 133 had pathologically confirmed benign or malignant lesions. The analysis included PBF measurements, PI-RADS v2.1 scores, and relevant clinical data from this cohort. This study employed both univariable and multivariable logistic regression to sequentially identify prognostic features. The diagnostic performance of PBF, PI-RADS v2.1 scores, and their combination in detecting csPCa was assessed using receiver operating characteristic (ROC) analysis. Bootstrap resampling was used for internal validation to ensure model stability. A nomogram was constructed to visualize the predictive model for csPCa risk, and decision curve analysis (DCA) was performed to evaluate its clinical utility.
RESULTS: Independent risk factors for csPCa included maximum prostate blood flow (PBF-max), PI-RADS v2.1 score, total prostate-specific antigen (tPSA), and prostate volume (PV). The diagnostic performance for csPCa, measured by the area under the curve (AUC), was 0.872 for PBF-max alone and 0.916 for the PI-RADS v2.1 score alone. Their combination significantly improved accuracy, yielding an AUC of 0.965. Furthermore, PI-RADS v2.1 scores demonstrated a strong positive correlation with PBF-max (r=0.62).
CONCLUSIONS: The non-invasive quantification of PBF via ASL enhances the diagnostic capability of PI-RADS v2.1 scores for the detection of csPCa.
METHODS: Of 233 patients with suspected prostate cancer (PCa) who underwent biparametric magnetic resonance imaging (bp-MRI) and ASL, 133 had pathologically confirmed benign or malignant lesions. The analysis included PBF measurements, PI-RADS v2.1 scores, and relevant clinical data from this cohort. This study employed both univariable and multivariable logistic regression to sequentially identify prognostic features. The diagnostic performance of PBF, PI-RADS v2.1 scores, and their combination in detecting csPCa was assessed using receiver operating characteristic (ROC) analysis. Bootstrap resampling was used for internal validation to ensure model stability. A nomogram was constructed to visualize the predictive model for csPCa risk, and decision curve analysis (DCA) was performed to evaluate its clinical utility.
RESULTS: Independent risk factors for csPCa included maximum prostate blood flow (PBF-max), PI-RADS v2.1 score, total prostate-specific antigen (tPSA), and prostate volume (PV). The diagnostic performance for csPCa, measured by the area under the curve (AUC), was 0.872 for PBF-max alone and 0.916 for the PI-RADS v2.1 score alone. Their combination significantly improved accuracy, yielding an AUC of 0.965. Furthermore, PI-RADS v2.1 scores demonstrated a strong positive correlation with PBF-max (r=0.62).
CONCLUSIONS: The non-invasive quantification of PBF via ASL enhances the diagnostic capability of PI-RADS v2.1 scores for the detection of csPCa.