UNASSIGNED:基于人群的前列腺癌(PCa)筛查计划的趋势有望增加对活检前成像的需求。这项研究假设用于三维多参数经直肠前列腺超声(3DmpUS)的机器学习图像分类算法可以准确检测PCa。
UNASSIGNED:这是一项2期前瞻性多中心诊断准确性研究。总共将包括715名患者,为期约2年。如果怀疑有前列腺活检的PCa或活检证实的PCa将进行根治性前列腺切除术(RP),则患者符合条件。排除标准是PCa的先前治疗或超声造影剂(UCA)的禁忌症。
未经评估:研究参与者将接受3DMPUS,由3D灰度组成,4D超声造影,和三维剪切波弹性成像(SWE)。整装RP组织病理学将提供训练图像分类算法的基本事实。在前列腺活检之前包括的患者将用于随后的初步验证。有一个小,与UCA管理相关的参与者的预期风险。在参与研究之前必须给予知情同意。和(严重)不良事件将被报告。
UNASSIGNED:主要结果将是用于在每个体素和每个微区水平上检测临床上有意义的PCa(csPCa)的算法的诊断性能。诊断性能将报告为接收器工作特性曲线下的面积。临床显著PCa定义为国际泌尿外科学会≥2级组。全装RP组织病理学将用作参考标准。次要结果将是敏感性,特异性,负预测值,和CSPCa在每个患者水平上的阳性预测值,在前列腺活检前对患者进行评估,使用活检结果作为参考标准。将对算法区分低,中介-,和高危肿瘤。
UNASSIGNED:本研究旨在开发一种基于超声的成像模态,用于PCa检测。必须进行随后的磁共振成像头对头验证试验,以确定其在临床实践中对怀疑患有PCa的患者进行风险分层的作用。
UNASSIGNED: The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This
study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately.
UNASSIGNED: This is a phase 2 prospective multicenter diagnostic accuracy
study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs).
UNASSIGNED: Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to
study participation, and (serious) adverse events will be reported.
UNASSIGNED: The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors.
UNASSIGNED: This
study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.