Abdominal radiology (New York) · 2026
Evaluating the efficacy of virtual magnetic resonance elastography and continuous-time random walk models in predicting clinically significant prostate cancer.
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Zhang Meng, Zhao Endian, Li Xuekun, Li Zhong, Duan Jinhui, Qin Ruijing, Yan Ruifang
Аннотация
OBJECTIVE: To evaluate the predictive performance of virtual magnetic resonance elastography (vMRE) and continuous-time random walk (CTRW) models for clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4).
METHODS: A total of 107 patients with suspected prostate lesions were retrospectively enrolled. Quantitative parameters from vMRE (µ), CTRW (α, β, Dm), conventional DWI (ADC), and clinical indicators were compared between csPCa and non-csPCa groups. Logistic regression was used to identify independent predictors and construct a combined model, with efficacy validated by receiver operating characteristic curve (AUC) and DeLong test.
RESULTS: The csPCa group had higher µ, PSAD, PI-RADS, and TPSA, and lower α, β, Dm, and ADC. Multivariate analysis identified µ, α, Dm, and PSAD as independent predictors. The combined model integrating these four parameters yielded the optimal diagnostic performance with an AUC of 0.98, which was significantly superior to single imaging modalities and individual variables, including vMRE (µ), CTRW (α + β + Dm), DWI (ADC), α, β, Dm, PSAD, PI-RADS, and TPSA (AUC = 0.85, 0.92, 0.78, 0.87, 0.74, 0.82, 0.65, 0.84, 0.76, and 0.80, respectively; all P < 0.05).
CONCLUSION: Both vMRE and CTRW are superior to conventional DWI in differentiating csPCa. The combined µ + α + Dm + PSAD panel serves as a non-invasive, high-performance biomarker for accurate csPCa prediction, which may help reduce unnecessary prostate biopsies and optimize clinical decision-making.
METHODS: A total of 107 patients with suspected prostate lesions were retrospectively enrolled. Quantitative parameters from vMRE (µ), CTRW (α, β, Dm), conventional DWI (ADC), and clinical indicators were compared between csPCa and non-csPCa groups. Logistic regression was used to identify independent predictors and construct a combined model, with efficacy validated by receiver operating characteristic curve (AUC) and DeLong test.
RESULTS: The csPCa group had higher µ, PSAD, PI-RADS, and TPSA, and lower α, β, Dm, and ADC. Multivariate analysis identified µ, α, Dm, and PSAD as independent predictors. The combined model integrating these four parameters yielded the optimal diagnostic performance with an AUC of 0.98, which was significantly superior to single imaging modalities and individual variables, including vMRE (µ), CTRW (α + β + Dm), DWI (ADC), α, β, Dm, PSAD, PI-RADS, and TPSA (AUC = 0.85, 0.92, 0.78, 0.87, 0.74, 0.82, 0.65, 0.84, 0.76, and 0.80, respectively; all P < 0.05).
CONCLUSION: Both vMRE and CTRW are superior to conventional DWI in differentiating csPCa. The combined µ + α + Dm + PSAD panel serves as a non-invasive, high-performance biomarker for accurate csPCa prediction, which may help reduce unnecessary prostate biopsies and optimize clinical decision-making.