Predicting clinically significant prostate cancer in elderly patients: A nomogram approach with shear wave elastography

The Prostate Pub Date : 2024-09-12 DOI:10.1002/pros.24789
Xiang Liu, Jia Zhu, Meng‐Qi Shi, Yong‐Sheng Pan, Xin‐Yu Cao, Zhong‐Xin Zhang
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Abstract

PurposeThis study was to construct a nomogram utilizing shear wave elastography and assess its efficacy in detecting clinically significant prostate cancer (csPCa).Methods290 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.ResultsOur 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.ConclusionOur 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.
预测老年患者中具有临床意义的前列腺癌:剪切波弹性成像的提名图方法
方法 对2022年4月至2023年12月期间分别接受前列腺活检和剪切波弹性成像(SWE)检查的290名疑似前列腺癌老年人进行登记。老年参与者被分为两组:有前列腺癌(csPCa)和无前列腺癌(csPCa),其中包括临床症状不明显的前列腺癌(cisPCa)病例和根据病理结果确定的非前列腺癌组织。利用 LASSO 算法(即最小绝对缩小和选择算子)来识别特征。利用逻辑回归分析建立模型。利用接收者操作特征曲线(ROC)和校准曲线来评估提名图的鉴别能力。采用引导法(1000 次引导迭代)进行内部验证,并与两个模型进行比较。结果与不含 SWE 参数的临床模型相比,我们的提名图(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))显示出更好的辨别能力(AUC = 0.89;95% CI:0.83-0.94)(p = 0.0007)。其准确性、灵敏度和特异性分别为 0.83、0.89 和 0.78。根据决策曲线分析,阈值范围为 5%-90%。根据我们的提名图,在 20% 概率阈值下对患者进行活检可减少 25% 的活检次数,且不会漏检任何 csPCa。临床影响曲线显示,当概率阈值达到 20% 或更高时,提名图的预测结果更接近观察结果。
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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