Purpose

Prostate Imaging-Reporting and Data System (PI-RADS) assists in evaluating lesions on multiparametric magnetic resonance imaging (mpMRI), but there are still ongoing efforts in improving the predictive value for the presence of clinically significant PCa (csPCa) with a Gleason grade group ≥ 2 on Fusion-Biopsy. This pilot study intends to propose an easily implementable method for augmenting predictability of csPCa for PI-RADS.

Methods

A cohort of 151 consecutive patients underwent mpMRI Fusion and random US Biopsy as a result of having at least one PI-RADS lesion grade 3–5 between January 1, 2019 and December 31, 2022. A single radiologist reads all films in this study applying PI-RADS V2.

Results

Of the 151 consecutive patients, 49 had a highest lesion of PI-RADS 3, 82 had a highest lesion of PI-RADS 4, and 20 had a highest lesion of PI-RADS 5. For each respective group, 12, 42, and 18 patients had proven csPCa. Two predictive models for csPCa were created by employing a logistical regression with parameters readily available to providers. The models had an AUC of 0.8133 and 0.8206, indicating promising effective models.

Conclusion

PI-RADS classification has relevant predictability problems for grades 3 and 4. By applying the presented risk calculators, patients with PI-RADS 3 and 4 are better stratified, and thus, a significant number of patients can be spared biopsies with potential complications, such as infection and bleeding. The presented predictive models may be a valuable diagnostic tool, adding additional information in the clinical decision-making process for biopsies.

中文翻译：

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新型临床风险计算器可提高前列腺 mpMRI 融合活检的癌症预测能力

目的

前列腺成像报告和数据系统 (PI-RADS) 有助于通过多参数磁共振成像 (mpMRI) 评估病变，但仍在努力提高格里森分级的临床意义 PCa (csPCa) 的预测价值融合活检组≥2。该试点研究旨在提出一种易于实施的方法，以增强 PI-RADS 的 csPCa 的可预测性。

方法

2019 年 1 月 1 日至 2022 年 12 月 31 日期间，由于至少有一个 3-5 级 PI-RADS 病变，一组连续 151 名患者接受了 mpMRI 融合和随机超声活检。一名放射科医生读取了本研究中的所有胶片，应用PI-RADS V2。

结果

在 151 名连续患者中，49 名患者具有最高 PI-RADS 3 病变，82 名患者具有最高 PI-RADS 4 病变，20 名患者具有最高 PI-RADS 5 病变。对于每个组，分别有 12 名、42 名和18 名患者确诊为 csPCa。通过采用逻辑回归创建了两个 csPCa 预测模型，其中的参数可供提供商随时使用。这些模型的 AUC 分别为 0.8133 和 0.8206，表明模型很有前景。

结论

PI-RADS 分类对于 3 级和 4 级存在相关的可预测性问题。通过应用所提供的风险计算器，PI-RADS 3 级和 4 级患者可以更好地分层，因此，大量患者可以避免进行具有潜在并发症的活检，例如如感染和出血。所提出的预测模型可能是一种有价值的诊断工具，为活检的临床决策过程添加了额外的信息。