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Purpose <p>This study aimed to develop a predictive model that integrates parameters derived from preoperative multiparametric magnetic resonance imaging (mpMRI) and [<sup>18</sup>F]PSMA-1007 PET/CT for reliably predicting pathological upgrading from systematic biopsy (SB) to radical prostatectomy (RP) specimens.</p> Methods <p>We ultimately retrospectively analyzed 163 patients with biopsy-confirmed localized prostate cancer (PCa) who underwent preoperative mpMRI and [<sup>18</sup>F]PSMA-1007 PET/CT scans between January 2019 and June 2022. Clinical and imaging characteristics were compared between patients with and without pathological upgrading. Predictive factors for pathological upgrading were evaluated through univariate and multivariable analyses. Predictive models were constructed based on the identified parameters. Receiver operating characteristic (ROC) curves were utilized to determine optimal cutoff values and to evaluate model performance. Additionally, patients from two external centers were selected as a validation cohort.</p> Results <p>A total of 55 (33.7%) cases experienced pathological upgrading. Multivariate analysis revealed that ADCmean - ADCmin (<i>P</i> = 0.035); SUVmax (<i>P</i> = 0.003); highest tumor grade at SB, ISUP grade group (ISUP GG) 1 vs. 2 (<i>P</i> = 0.001), ISUP GG 1 vs. 3 (<i>P</i> &lt; 0.001), ISUP GG 1 vs. 4 (<i>P</i> &lt; 0.001); and multifocality on [<sup>18</sup>F]PSMA-1007 PET/CT (<i>P</i> = 0.007) were independent predictors for pathological upgrading. The combined model achieved an area under the curve (AUC) of 0.803 (95% CI: 0.734 to 0.861), indicating robust discriminative power. External validation confirmed the model’s reliability and predictive ability.</p> Conclusion <p>Our predictive model, integrating mpMRI and [<sup>18</sup>F]PSMA-1007 PET/CT parameters, effectively forecasts pathological upgrading in PCa, allowing for more precise treatment risk stratification.</p>

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The predictive value of multiparametric MRI combined with [18F]PSMA-1007 PET/CT for the pathological upgrade in prostate cancer: a multicenter study

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Jian Xu,
  • Haisong Chen,
  • Lixuan Chen,
  • Tiancheng Li,
  • Heng Lin,
  • Shuying Bian,
  • Qi Lin,
  • Yuandi Zhuang,
  • Yingnan Xue,
  • Yunjun Yang,
  • Xinhui Su,
  • Fei Yao

摘要

Purpose

This study aimed to develop a predictive model that integrates parameters derived from preoperative multiparametric magnetic resonance imaging (mpMRI) and [18F]PSMA-1007 PET/CT for reliably predicting pathological upgrading from systematic biopsy (SB) to radical prostatectomy (RP) specimens.

Methods

We ultimately retrospectively analyzed 163 patients with biopsy-confirmed localized prostate cancer (PCa) who underwent preoperative mpMRI and [18F]PSMA-1007 PET/CT scans between January 2019 and June 2022. Clinical and imaging characteristics were compared between patients with and without pathological upgrading. Predictive factors for pathological upgrading were evaluated through univariate and multivariable analyses. Predictive models were constructed based on the identified parameters. Receiver operating characteristic (ROC) curves were utilized to determine optimal cutoff values and to evaluate model performance. Additionally, patients from two external centers were selected as a validation cohort.

Results

A total of 55 (33.7%) cases experienced pathological upgrading. Multivariate analysis revealed that ADCmean - ADCmin (P = 0.035); SUVmax (P = 0.003); highest tumor grade at SB, ISUP grade group (ISUP GG) 1 vs. 2 (P = 0.001), ISUP GG 1 vs. 3 (P < 0.001), ISUP GG 1 vs. 4 (P < 0.001); and multifocality on [18F]PSMA-1007 PET/CT (P = 0.007) were independent predictors for pathological upgrading. The combined model achieved an area under the curve (AUC) of 0.803 (95% CI: 0.734 to 0.861), indicating robust discriminative power. External validation confirmed the model’s reliability and predictive ability.

Conclusion

Our predictive model, integrating mpMRI and [18F]PSMA-1007 PET/CT parameters, effectively forecasts pathological upgrading in PCa, allowing for more precise treatment risk stratification.