Prostate Cancer Diagnosis by Multiparametric Ultrasound

前列腺癌(PCA)是美国男性中最常见的确诊癌症，占美国男性癌症的20% 新的癌症诊断，但PCA只对9%的癌症相关死亡负有直接责任。PSA筛查 有助于检测前列腺癌，但PSA筛查发现的许多癌症为低度至中度 分级，导致非侵袭性前列腺癌过度治疗。最近的研究表明，迫切需要有选择地 确定具有“临床意义”的PCa，这将导致成本效益高的PCa治疗。在过去几年里， 多参数磁共振成像(MP-MRI)已成为诊断侵袭性前列腺癌的标准。尽管MP- MRI引导下的活检可以选择性地检测较高级别的PCa病变，最近的几项研究承认 MP-MRI遗漏了相当数量的临床重要病变。此外，鉴于成本较高， 核磁共振成像，核磁共振成像系统可用性的地理变异性，用于配准和融合的不精确方法 核磁共振结合超声进行活检，并对肾功能不全患者使用Gd的危险，有 需要一种更具成本效益的替代方案来检测高级别的PCA。最近的研究表明， 超声造影(CEUS)可以选择性地检测出具有“临床意义”的前列腺癌患者。新的 已经提出了增强包括次谐波在内的PCA的常规超声检测的方法 成像(SHI)、对比增强超声弥散成像(CUDI)和粘弹性成像。目标是 该项目的目的是开发一种能够多参数组合超声技术(MP-R)的系统 美国)用于检测“有临床意义的”前列腺癌。我们将比较3D MP-US和MP-MRI以确定有意义的 Pca(定义为：格里森评分≥7，受累50%的单个核心，或25%的活检核心阳性 PCa或Gleason分级组≥2、PSA&GT；10或临床分期cT2b或更差)。研究的第一年将 致力于在商用3D经直肠探头上实施我们的MP-US技术，以及 使基于MRI的融合/配准系统适应MP-US。在第二年，我们将优化MP-US 采用机器学习方法，对50例前列腺癌根治术患者进行病理对照研究。 这项研究的最后3年将致力于一项临床试验，300名参与者被怀疑患有前列腺癌。 每位参与者将接受最多3个可疑区域的活检形式的实验性干预 经直肠超声鉴定为经直肠超声诊断的前列腺癌。作为比较，最多3个 还将根据MP-MRI(临床干预)从每个参与者那里获得有针对性的活检核心。 在定向活检之后，每个参与者还将接受系统的活检(护理标准)，包括 6个横向导向活检芯和6个内侧导向活检芯。这项研究的目的是证明 基于MP-US的靶向活检并不逊色于基于MP-MRI的靶向活检。

Prostate cancer (PCa) is the most frequently diagnosed cancer among American males, accounting for 20% of new cancer diagnoses, but PCa is directly responsible for only 9% of cancer related deaths. PSA screening facilitates the detection of PCa, but many of the cancers detected by PSA screening are low to intermediate grade, resulting in overtreatment of non-aggressive PCa. Recent studies suggest an urgent need to selectively identify “clinically significant” PCa that will result in cost-effective treatment of PCa. In the past few years, multiparametric MRI (mp-MRI) has become the standard of care for diagnosis of aggressive PCa. Although mp- MRI guided biopsy can selectively detect higher grade PCa lesions, several recent studies acknowledge that a considerable number of clinically important lesions are missed by mp-MRI. Furthermore, given the high cost of MRI, the geographic variability in the availability of MRI systems, the inexact methods used to register and fuse MRI with ultrasound for biopsy, and the dangers of Gadolinium in patients with renal insufficiency, there is a need for a more cost-effective, alternative to detect high-grade PCa. Recent studies have demonstrated that contrast-enhanced ultrasound (CEUS) can selectively detect patients with “clinically significant” PCa. New methods have been proposed to enhance conventional ultrasound detection of PCa, including subharmonic imaging (SHI), contrast-enhanced ultrasound dispersion imaging (CUDI) and viscoelastography. The objective of this project is to develop a system capable of a multiparametric combination of ultrasound techniques (mp- US) for detection of “clinically significant” PCa. We will compare 3D mp-US and mp-MRI to identify significant PCa (defined as: Gleason score ≥ 7, a single core with > 50% involvement, or > 25% of biopsy cores positive for PCa or as Gleason grade group ≥ 2, PSA > 10 or clinical stage of cT2b or worse). The first year of the study will be dedicated to implementation of our mp-US technique on a commercially available 3D transrectal probe, and to adapting a MRI-based fusion/registration system to mp-US. During the second year, we will optimize mp-US with a machine learning approach, based upon pathologic correlation with 50 radical prostatectomy patients. The final 3 years of the study will be dedicated to a clinical trial with 300 participants suspected of having PCa. Each participant will receive an experimental intervention in the form of biopsy of up to 3 suspicious areas identified by transrectal ultrasound evaluation of the prostate with mp-US. For comparison, a maximum of 3 targeted biopsy cores will also be obtained from each participant, based on mp-MRI (the clinical intervention). Following the targeted biopsy, each participant will also receive a systematic biopsy (standard of care) consisting of 6 laterally directed biopsy cores and 6 medially directed biopsy cores. The study is powered to demonstrate that targeted biopsy based upon mp-US is not inferior to targeted biopsy based upon mp-MRI.