Background

Prostate cancer risk prediction models and multiparametric magnetic resonance imaging (mpMRI) are used for individualised pre-biopsy risk assessment. However, biparametric MRI (bpMRI) has emerged as a simpler, more rapid MRI approach (fewer scan sequences, no intravenous contrast-media) to reduce costs and facilitate a more widespread clinical implementation. It is unknown how bpMRI and risk models perform conjointly. Therefore, the objective was to develop a predictive model for significant prostate cancer (sPCa) in biopsy-naive men based on bpMRI findings and clinical parameters.

Methods

Eight hundred and seventy-six biopsy-naive men with clinical suspicion of prostate cancer (prostate-specific antigen, <50 ng/mL; tumour stage, <T3) underwent pre-biopsy prostate bpMRI (T2-weighted and diffusion-weighted) followed by 10-core standard biopsies (all men) and MRI-transrectal ultrasound fusion targeted biopsies of bpMRI-suspicious lesions (suspicion score, ≥3). Prediction models based on bpMRI scores and clinical parameters (age, tumour stage, prostate-specific-antigen [PSA] level, prostate_volume, and PSA_density) were created to detect sPCa (any biopsy-core with Gleason grade-group, ≥2) and compared by analysing the areas under the curves and decision curves.

Results

Overall, sPCa was detected in 350/876 men (40%) with median (inter-quartile range) age and PSA level of 65 years (60–70) and 7.3 ng/mL (5.5–10.6), respectively. The model defined by bpMRI scores, age, tumour stage, and PSA_density had the highest discriminatory power (area under the curve, 0.89), showed good calibration on internal bootstrap validation, and resulted in the greatest net benefit on decision curve analysis. Applying a biopsy risk threshold of 20% meant that 42% of men could avoid a biopsy, 50% fewer insignificant cancers were diagnosed, and only 7% of significant cancers (grade-group, ≥2) were missed.

Conclusions

A predictive model based on bpMRI scores and clinical parameters significantly improved risk stratification for sPCa in biopsy-naïve men and could be used for clinical decision-making and counselling men prior to prostate biopsies.

中文翻译：

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基于双参数磁共振成像和临床参数的预测模型，用于改进风险评估和选择未接受过活检的男性进行前列腺活检。

背景

前列腺癌风险预测模型和多参数磁共振成像 (mpMRI) 用于个体化活检前风险评估。然而，双参数 MRI (bpMRI) 已成为一种更简单、更快速的 MRI 方法（扫描序列更少，无需静脉内造影剂），以降低成本并促进更广泛的临床实施。不知道 bpMRI 和风险模型如何联合执行。因此，目标是根据 bpMRI 发现和临床参数，在未接受过活检的男性中开发出显着前列腺癌 (sPCa) 的预测模型。

方法

876 名临床怀疑为前列腺癌（前列腺特异性抗原，<50 ng/mL；肿瘤分期，<T3）的初次活检男性接受活检前前列腺 bpMRI（T2 加权和弥散加权）通过 10 核标准活检（所有男性）和 MRI 经直肠超声融合靶向 bpMRI 可疑病变活检（可疑评分≥3）。创建了基于 bpMRI 评分和临床参数（年龄、肿瘤分期、前列腺特异性抗原 [PSA] 水平、前列腺_体积和 PSA_密度）的预测模型来检测 sPCa（任何具有 Gleason 分级组的活检核心，≥2 ) 并通过分析曲线和决策曲线下的面积进行比较。

结果

总体而言，在 350/876 名男性（40%）中检测到 sPCa，中位（四分位距）年龄和 PSA 水平分别为 65 岁（60-70）和 7.3 ng/mL（5.5-10.6）。由 bpMRI 评分、年龄、肿瘤分期和 PSA_密度定义的模型具有最高的区分能力（曲线下面积，0.89），在内部引导验证中显示出良好的校准，并在决策曲线分析中产生了最大的净收益。应用 20% 的活检风险阈值意味着 42% 的男性可以避免活检，诊断出的不重要癌症减少了 50%，只有 7% 的重要癌症（分级组，≥2）被漏诊。

结论

基于 bpMRI 评分和临床参数的预测模型显着改善了未进行活检的男性 sPCa 的风险分层，并可用于前列腺活检前的临床决策和咨询男性。