A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).
A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).
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DOI：
10.1118/1.3180955
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发表时间：
2009-09
期刊：
影响因子：
3.8
通讯作者：
P. Tiwari;M. Rosen;A. Madabhushi
中科院分区：
文献类型：
--
作者：
P. Tiwari;M. Rosen;A. Madabhushi
磁共振波谱(MRS)已被证明在前列腺癌(CAP)的检测中作为磁共振成像的补充具有巨大的临床潜力。MRS以特定代谢物(包括胆碱、肌酸和柠檬酸)的相对浓度变化的形式提供功能信息，可用于识别CAP的潜在区域。为了帮助放射科医生解释和分析MRS数据，一些研究人员已经开始开发计算机辅助检测(CAD)方案，用于从光谱学中识别帽子。大多数这些方案都集中在识别和整合代谢物峰下的区域，然后用来计算相对代谢物比率。然而，由于低信噪比、基线不规则性、峰重叠和峰失真，人工识别MR谱上的代谢物峰是一个具有挑战性的问题，尤其是通过CAD。在这篇文章中，作者提出了一种新的CAD方案，该方案将非线性降维(NLDR)与无监督的层次聚类算法相结合，利用MRS自动识别前列腺上的可疑区域，从而避免了显式识别代谢物峰的需要。该方法包括两个阶段。在第一阶段中，使用分层光谱聚类算法来区分包膜外光谱和前列腺光谱，以便定位对应于前列腺的感兴趣区域(ROI)。一旦定位了前列腺ROI，在阶段2，NLDR方案与复制的聚类算法相结合被用于自动区分三类光谱(正常出现、可疑出现和不确定)。该方法在总共18个1.5T活体前列腺T2加权(W)和MRS研究中进行了定量和定性的评估，这些研究来自多点、多机构的美国放射学会(ACRIN)试验。在大多数ACRIN研究中，由于缺乏MR成像上帽状范围的准确基础事实，概率定量指标是基于对肿瘤象限位置和大小的部分了解而定义的。根据这一部分基本事实对该方案进行评估时，发现CAP检测的灵敏度为89.33%，特异度为79.79%。随机三次和五次交叉验证的结果表明，与常用的MRS分析方法如z Score和PCA相比，基于NLDR的聚类方法具有更高的帽检测精度。此外，该方案对系统参数的变化具有较强的鲁棒性。对于18项研究中的6项，放射科专家根据五分制对每个单独的光谱进行了艰苦的标记，其中1/2代表专家认为正常的光谱，3/4/5代表专家认为可疑的光谱。在这些专家注释的数据集上进行评估时，CAD系统产生的平均灵敏度(对应于可疑光谱的簇被识别为CAP类)和特异度分别为81.39%和64.71%。
Magnetic resonance spectroscopy (MRS) has been shown to have great clinical potential as a supplement to magnetic resonance imaging in the detection of prostate cancer (CaP). MRS provides functional information in the form of changes in the relative concentration of specific metabolites including choline, creatine, and citrate which can be used to identify potential areas of CaP. With a view to assisting radiologists in interpretation and analysis of MRS data, some researchers have begun to develop computer-aided detection (CAD) schemes for CaP identification from spectroscopy. Most of these schemes have been centered on identifying and integrating the area under metabolite peaks which is then used to compute relative metabolite ratios. However, manual identification of metabolite peaks on the MR spectra, and especially via CAD, is a challenging problem due to low signal-to-noise ratio, baseline irregularity, peak overlap, and peak distortion. In this article the authors present a novel CAD scheme that integrates nonlinear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify suspicious regions on the prostate using MRS and hence avoids the need to explicitly identify metabolite peaks. The methodology comprises two stages. In stage 1, a hierarchical spectral clustering algorithm is used to distinguish between extracapsular and prostatic spectra in order to localize the region of interest (ROI) corresponding to the prostate. Once the prostate ROI is localized, in stage 2, a NLDR scheme, in conjunction with a replicated clustering algorithm, is used to automatically discriminate between three classes of spectra (normal appearing, suspicious appearing, and indeterminate). The methodology was quantitatively and qualitatively evaluated on a total of 18 1.5 T in vivo prostate T2-weighted (w) and MRS studies obtained from the multisite, multi-institutional American College of Radiology (ACRIN) trial. In the absence of the precise ground truth for CaP extent on the MR imaging for most of the ACRIN studies, probabilistic quantitative metrics were defined based on partial knowledge on the quadrant location and size of the tumor. The scheme, when evaluated against this partial ground truth, was found to have a CaP detection sensitivity of 89.33% and specificity of 79.79%. The results obtained from randomized threefold and fivefold cross validation suggest that the NLDR based clustering scheme has a higher CaP detection accuracy compared to such commonly used MRS analysis schemes as z score and PCA. In addition, the scheme was found to be robust to changes in system parameters. For 6 of the 18 studies an expert radiologist laboriously labeled each of the individual spectra according to a five point scale, with 1/2 representing spectra that the expert considered normal and 3/4/5 being spectra the expert deemed suspicious. When evaluated on these expert annotated datasets, the CAD system yielded an average sensitivity (cluster corresponding to suspicious spectra being identified as the CaP class) and specificity of 81.39% and 64.71%, respectively.