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Purpose <p>To investigate the prognostic value of apparent diffusion coefficient (ADC) metrics, MRI characteristics, and clinicopathological parameters in predicting non-muscle-invasive bladder urothelial carcinoma (UCB) recurrence, and to develop a novel multiparametric risk stratification framework.</p> Methods <p>This retrospective single-center study (<i>n</i> = 135) with histologically confirmed non-muscle-invasive UCB diagnosed between January 2015 and March 2023. ADC values, vesical imaging reporting and data system (VI-RADS) scores, and clinicopathological variables were analyzed with recurrence-free survival (RFS) as the primary endpoint. Prognostic determinants were identified using univariate and multivariate Cox proportional hazard regression models. An advanced risk stratification system was developed using independent predictors and validated against the European Association of Urology (EAU) risk classification using concordance index (C-index).</p> Results <p>Multivariate analysis identified three independent predictors: ADC values (HR = 0.104, 95% confidence interval (CI) 0.025–0.436), hemoglobin levels (HR = 0.463, 95% CI 0.223–0.960), and pathological grade (HR = 2.079, 95% CI 1.098–3.936). The combined model incorporating these parameters demonstrated moderate predictive accuracy (C-index = 0.724, 95% CI 0.655–0.794). Notably, VI-RADS scores showed no independent prognostic value. Risk stratification based on ADC (≤ 1343.22 × 10⁻⁶ mm²/s), hemoglobin (&lt; 113.2&#xa0;g/L), and pathological grade demonstrated superior discriminative capacity compared to EAU criteria (C-index: 0.667 vs. 0.605).</p> Conclusion <p>A multidimensional prognostic framework integrating quantitative ADC metrics, hemoglobin levels, and pathological grading significantly outperforms conventional EAU stratification in predicting non-muscle-invasive UCB recurrence, providing clinically actionable thresholds for personalized risk stratification and UCB management.</p>

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Recurrence risk prediction for non-muscle-invasive bladder urothelial carcinoma using diffusion and clinicopathology features

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • Xiaoxian Zhang,
  • Jinxia Guo,
  • Lifeng Wang,
  • Yuedi Ma,
  • Liuqing Kang,
  • Dong Yang,
  • Xuejun Chen,
  • Chunmiao Xu

摘要

Purpose

To investigate the prognostic value of apparent diffusion coefficient (ADC) metrics, MRI characteristics, and clinicopathological parameters in predicting non-muscle-invasive bladder urothelial carcinoma (UCB) recurrence, and to develop a novel multiparametric risk stratification framework.

Methods

This retrospective single-center study (n = 135) with histologically confirmed non-muscle-invasive UCB diagnosed between January 2015 and March 2023. ADC values, vesical imaging reporting and data system (VI-RADS) scores, and clinicopathological variables were analyzed with recurrence-free survival (RFS) as the primary endpoint. Prognostic determinants were identified using univariate and multivariate Cox proportional hazard regression models. An advanced risk stratification system was developed using independent predictors and validated against the European Association of Urology (EAU) risk classification using concordance index (C-index).

Results

Multivariate analysis identified three independent predictors: ADC values (HR = 0.104, 95% confidence interval (CI) 0.025–0.436), hemoglobin levels (HR = 0.463, 95% CI 0.223–0.960), and pathological grade (HR = 2.079, 95% CI 1.098–3.936). The combined model incorporating these parameters demonstrated moderate predictive accuracy (C-index = 0.724, 95% CI 0.655–0.794). Notably, VI-RADS scores showed no independent prognostic value. Risk stratification based on ADC (≤ 1343.22 × 10⁻⁶ mm²/s), hemoglobin (< 113.2 g/L), and pathological grade demonstrated superior discriminative capacity compared to EAU criteria (C-index: 0.667 vs. 0.605).

Conclusion

A multidimensional prognostic framework integrating quantitative ADC metrics, hemoglobin levels, and pathological grading significantly outperforms conventional EAU stratification in predicting non-muscle-invasive UCB recurrence, providing clinically actionable thresholds for personalized risk stratification and UCB management.