Detecting Prostate Cancer using multi-protocol 3 Tesla in vivo MRI and MRS

简介（由申请人提供）：PI: Madabhushi， Anant前列腺腺癌（CAP）的早期检测为治愈它提供了最大的希望。目前，明确诊断CAP的唯一方法是通过对切除组织的组织学分析，通过盲六分仪经直肠超声（TRUS）定向活检获得。然而，对于PSA水平升高，TRUS活检的准确性仅为20-25%。最近，高分辨率磁共振成像（MRI）已被证明在前列腺结构的可视化优于超声。计算机辅助诊断（CAD）是指利用计算机程序协助放射科医生从放射图像中检测异常。本项目的目的是开发一个CAD系统，通过多协议3特斯拉体内MRI和磁共振光谱成像（MRSI）检测前列腺癌。与MRI相关的初始较高费用将被不必要的活组织检查的减少和通过活组织检查发现的癌症数量的增加所抵消。这项工作的长期目标是激励CAD与高分辨率MRI/MRSI的使用：- 1。早期发现中高风险患者的前列腺癌，从而减少不必要的活检和根治性前列腺切除术。2. 用cad辅助MRI引导前列腺活检代替TRUS提高癌症检出率。3. 提高前列腺治疗靶剂量的一致性。4. 用于评估抗癌治疗的定量反应。拟议的工作共包括3个具体目标和10项任务。将获得总共40个匿名患者数据集，包括前列腺切除术前的多方案3特斯拉(T)体内MRI/MRSI扫描和前列腺切除术后的多方案3 T离体MRI/MRSI扫描以及随附的整个组织学切片（目的1）。每个患者研究将获得四种不同的MR方案——T1、t2加权、动态对比增强和扩散成像。纳入组织学数据将允许通过H&E染色精确确定CAP区域。为了训练CAD系统在体内MRI上自动识别CAP，需要准确地确定体内肿瘤的空间位置。目的2涉及通过首先将整个组织切片与相应的离体MRI图像进行注册来确定体内CAP（肿瘤基底真值）的空间范围。一旦在离体MRI上确定了CAP的空间位置，这些图像就会与相应的体内MRI研究相匹配。目的3涉及建立和评估用于在高分辨率体内MRI和MRSI研究中检测CAP的CAD系统。该项目将由罗格斯大学和宾夕法尼亚大学（UPENN）的研究人员合作完成。目标1（数据生成）将在UPENN进行，而目标2（肿瘤基础真相生成）和目标3 （CAD模型）将在罗格斯大学进行。1 PI: Madabhushi，前列腺腺癌（CAP）是男性中第二大最常见的恶性肿瘤，2004年美国估计有23万新病例。在美国，目前诊断CAP的方案是让前列腺特异性抗原（PSA）水平升高的患者接受经直肠超声（TRUS）指导的活检。然而，一些研究报道(1)单独使用PSA作为筛查试验会导致大量不必要的活检；(2)与TRUS相关的假阴性率很高。本研究的目的是将计算机辅助诊断（CAD）与多方案高分辨率体内磁共振成像（MRI）和磁共振光谱成像（MRSI）相结合，以提高前列腺癌检测的敏感性和特异性。通过将治疗导向CAD确定为肿瘤的特定区域，所提出的工作具有翻译意义（尿道和直肠壁）。

DESCRIPTION (provided by applicant): PI: Madabhushi, Anant Early detection of prostatic adenocarcinoma (CAP) offers the best hope of curing it. Currently, the only way to definitively diagnose CAP is through histological analysis of excised tissue, obtained via blind sextant trans-rectal ultrasound (TRUS)-directed biopsies. However, the accuracy of TRUS biopsies is only 20-25% for elevated PSA levels. Recently, high-resolution magnetic resonance (MR) imaging (MRI) has been shown to be superior to ultrasound in visualizing prostatic structures. Computer-aided diagnosis (CAD) refers to the use of computer programs in assisting radiologists detect abnormalities from radiological images. The aim of this project is to develop a CAD system to detect prostate cancer from multi-protocol 3 Tesla in vivo MRI and Magnetic Resonance Spectroscopic (MRS) Imaging (MRSI). The initial higher costs associated with MRI will be offset by a reduction in unnecessary biopsies and an increase in the number of cancers detected via biopsy. The long term goals of this work are to motivate use of CAD with high resolution MRI/MRSI:- 1. For early detection of CAP in moderate to high risk patients and thereby reduce the number of unnecessary biopsies and radical prostatectomies. 2. Improving cancer detection rates by replacing TRUS with CAD-assisted MRI guided prostate biopsies. 3. For improved target dose conformality in prostate therapy. 4. For assessing quantitative response to anti-cancer therapy. The proposed work comprises a total of 3 specific aims and 10 tasks. A total of 40 anonymised patient data sets comprising multi-protocol 3 Tesla (T) in vivo MRI/MRSI scans before and multi-protocol 3 T ex vivo MRI/MRSI scans with accompanying whole mount histological sections after prostatectomy will be obtained (Aim 1). Four different MR protocols will be obtained for each patient study -- T1-, T2-weighted, dynamic contrast-enhanced, and diffusion imaging. The inclusion of histological data will allow for precise determination of CAP regions via H&E staining. In order to train the CAD system to automatically identify CAP on in vivo MRI accurate determination of spatial location of cancer in vivo is required. Aim 2 involves determination of spatial extent of CAP (tumor ground truth) in vivo by first registering the whole mount histological sections with the corresponding ex vivo MRI images. Once the spatial location of CAP has been thus determined on ex vivo MRI, these images are then registered with the corresponding in vivo MRI studies. Aim 3 involves building and evaluating the CAD system for detecting CAP on the high resolution in vivo MRI and MRSI studies. This project will be a collaborative effort between investigators at Rutgers University and the University of Pennsylvania (UPENN). Aim 1 (Data Generation) will be carried out at UPENN while Aims 2 (Tumor ground truth generation) and 3 (CAD model) will be done at Rutgers. 1 PI: Madabhushi, Anant Prostatic adenocarcinoma (CAP) is the second most common malignancy among men with an estimated 230,000 new cases in the USA in 2004. The current protocol to diagnose CAP in the USA is to have patients with elevated prostate specific antigen (PSA) levels undergo a trans-rectal ultrasound (TRUS)-directed biopsy. However, several studies have reported (1) that using PSA alone as a screening test results in a large number of unnecessary biopsies and (2) there is a high false negative rate associated with TRUS. The goal of this work is to integrate computer-aided diagnosis (CAD) with multi-protocol high resolution in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopic Imaging (MRSI) in order to increase the sensitivity and specificity of prostate cancer detection. The proposed work has translational implications in, structures (urethra and rectal wall) by directing therapy towards specific areas determined as tumor by CAD.