Department of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, China.
Department of Immunology, Nantong Center for Disease Control and Prevention, Nantong, China.
Prostate. 2024 Dec;84(16):1490-1500. doi: 10.1002/pros.24789. Epub 2024 Sep 12.
This study was to construct a nomogram utilizing shear wave elastography and assess its efficacy in detecting clinically significant prostate cancer (csPCa).
290 elderly people with suspected PCa who received prostate biopsy and shear wave elastography (SWE) imaging were respectively registered from April 2022 to December 2023. The elderly participants were stratified into two groups: those with csPCa and those without csPCa, which encompassed cases of clinically insignificant prostate cancer (cisPCa) and non-prostate cancer tissue, as determined by pathology findings. The LASSO algorithm, known as the least absolute shrinkage and selection operator, was utilized to identify features. Logistic regression analysis was utilized to establish models. Receiver operating characteristic (ROC) and calibration curves were utilized to evaluate the discriminatory ability of the nomogram. Bootstrap (1000 bootstrap iterations) was employed for internal validation and comparison with two models. A decision curve and a clinical impact curve were employed to assess the clinical usefulness.
Our nomogram, which contained Emean, ΔEmean, prostate volume, prostate-specific antigen density (PSAD), and transrectal ultrasound (TRUS), showed better discrimination (AUC = 0.89; 95% CI: 0.83-0.94), compared to the clinical model without SWE parameters (p = 0.0007). Its accuracy, sensitivity and specificity were 0.83, 0.89 and 0.78, respectively. Based on the analysis of decision curve, the thresholds ranged from 5% to 90%. According to our nomogram, biopsying patients at a 20% probability threshold resulted in a 25% reduction in biopsies without missing any csPCa. The clinical impact curve demonstrated that the nomogram's predicted outcome is closer to the observed outcome when the probability threshold reaches 20% or greater.
Our nomogram demonstrates efficacy in identifying elderly individuals with clinically significant prostate cancer, thereby facilitating informed clinical decision-making based on diagnostic outcomes and potential clinical benefits.
本研究旨在构建利用剪切波弹性成像（SWE）检测临床显著前列腺癌（csPCa）的列线图，并评估其效能。
2022 年 4 月至 2023 年 12 月，分别从接受前列腺活检和 SWE 成像的 290 名疑似前列腺癌的老年人中登记。将老年参与者分为两组：csPCa 组和非 csPCa 组，后者包括临床意义不显著的前列腺癌（cisPCa）和非前列腺癌组织，由病理检查结果确定。利用最小绝对收缩和选择算子（LASSO）算法识别特征。利用逻辑回归分析建立模型。利用受试者工作特征（ROC）和校准曲线评估列线图的判别能力。采用 Bootstrap（1000 次 bootstrap 迭代）进行内部验证，并与两种模型进行比较。采用决策曲线和临床影响曲线评估临床实用性。
我们的列线图包含 Emean、ΔEmean、前列腺体积、前列腺特异性抗原密度（PSAD）和经直肠超声（TRUS），与不包含 SWE 参数的临床模型相比，具有更好的判别能力（AUC=0.89；95%CI：0.83-0.94；p=0.0007）。其准确性、敏感度和特异度分别为 0.83、0.89 和 0.78。基于决策曲线分析，阈值范围为 5%至 90%。根据我们的列线图，在概率阈值为 20%时，对患者进行活检可减少 25%的活检，而不会遗漏任何 csPCa。临床影响曲线表明，当概率阈值达到 20%或更高时，列线图预测的结果更接近观察结果。
我们的列线图在识别患有临床显著前列腺癌的老年人方面具有良好的效果，从而有助于根据诊断结果和潜在临床获益做出明智的临床决策。
