The impact of integrating PRIMARY score or SUVmax with MRI-based risk models for the detection of clinically significant prostate cancer

IF 8.6 1区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Nuclear Medicine and Molecular Imaging Pub Date : 2024-09-12 DOI:10.1007/s00259-024-06916-2

Shikuan Guo, Jing Ren, Qingze Meng, Boyuan Zhang, Jianhua Jiao, Donghui Han, Peng Wu, Shuaijun Ma, Jing Zhang, Nianzeng Xing, Weijun Qin, Fei Kang, Jingliang Zhang

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Abstract

Purpose

An MRI-based risk calculator (RC) has been recommended for diagnosing clinically significant prostate cancer (csPCa). PSMA PET/CT can detect lesions that are not visible on MRI, and the addition of PSMA PET/CT to MRI may improve diagnostic performance. The aim of this study was to incorporate the PRIMARY score or SUVmax derived from [⁶⁸Ga]Ga-PSMA-11 PET/CT into the RC and compare these models with MRI-based RC to assess whether this can further reduce unnecessary biopsies.

Methods

A total of 683 consecutive biopsy-naïve men who underwent both [⁶⁸Ga]Ga-PSMA-11 PET/CT and MRI before biopsy were temporally divided into a development cohort (n = 552) and a temporal validation cohort (n = 131). Three logistic regression RCs were developed and compared: MRI-RC, MRI-SUVmax-RC and MRI-PRIMARY-RC. Discrimination, calibration, and clinical utility were evaluated. The primary outcome was the clinical utility of the risk calculators for detecting csPCa and reducing the number of negative biopsies.

Results

The prevalence of csPCa was 47.5% (262/552) in the development cohort and 41.9% (55/131) in the temporal validation cohort. In the development cohort, the AUC of MRI-PRIMARY-RC was significantly higher than that of MRI-RC (0.924 vs. 0.868, p < 0.001) and MRI-SUVmax-RC (0.924 vs. 0.904, p = 0.002). In the temporal validation cohort, MRI-PRIMARY-RC also showed the best discriminative ability with an AUC of 0.921 (95% CI: 0.873–0.969). Bootstrapped calibration curves revealed that the model fit was acceptable. MRI-PRIMARY-RC exhibited near-perfect calibration within the range of 0–40%. DCA showed that MRI-PRIMARY-RC had the greatest net benefit for detecting csPCa compared with MRI-RC and MRI-SUVmax-RC at a risk threshold of 5–40% for csPCa in both the development and validation cohorts.

Conclusion

The addition of the PRIMARY score to MRI-based multivariable model improved the accuracy of risk stratification prior to biopsy. Our novel MRI-PRIMARY prediction model is a promising approach for reducing unnecessary biopsies and improving the early detection of csPCa.

Abstract Image

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将 PRIMARY 评分或 SUVmax 与基于 MRI 的风险模型相结合对检测具有临床意义的前列腺癌的影响

目的建议使用基于核磁共振成像的风险计算器（RC）来诊断有临床意义的前列腺癌（csPCa）。PSMA PET/CT 可检测出核磁共振成像（MRI）上不可见的病灶，在核磁共振成像（MRI）上增加 PSMA PET/CT 可提高诊断效果。本研究旨在将[68Ga]Ga-PSMA-11 PET/CT 得出的 PRIMARY 评分或 SUVmax 纳入 RC，并将这些模型与基于 MRI 的 RC 进行比较，以评估这是否能进一步减少不必要的活检。开发并比较了三种逻辑回归 RC：MRI-RC、MRI-SUVmax-RC 和 MRI-PRIMARY-RC。对辨别、校准和临床实用性进行了评估。主要结果是风险计算器在检测 csPCa 和减少阴性活检次数方面的临床实用性。结果在开发队列中，csPCa 的患病率为 47.5%（262/552），在时间验证队列中为 41.9%（55/131）。在发展队列中，MRI-PRIMARY-RC 的 AUC 明显高于 MRI-RC（0.924 vs. 0.868，p < 0.001）和 MRI-SUVmax-RC （0.924 vs. 0.904，p = 0.002）。在时间验证队列中，MRI-PRIMARY-RC 也显示出最佳的分辨能力，其 AUC 为 0.921（95% CI：0.873-0.969）。Bootstrapped校准曲线显示模型拟合是可以接受的。MRI-PRIMARY-RC 在 0-40% 的范围内显示出近乎完美的校准。DCA 显示，在开发队列和验证队列中，当 csPCa 的风险阈值为 5-40% 时，与 MRI-RC 和 MRI-SUVmax-RC 相比，MRI-PRIMARY-RC 在检测 csPCa 方面的净获益最大。我们的新型 MRI-PRIMARY 预测模型是减少不必要的活检和提高 csPCa 早期检测率的有效方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Nuclear Medicine and Molecular Imaging 医学-核医学

CiteScore

15.60

自引率

9.90%

发文量

392

审稿时长

3 months

期刊介绍： The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.

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