Tiwari Pallavi, Madabhushi Anant, Rosen Mark
Department of Biomedical Engineering, Rutgers University, USA.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):278-86. doi: 10.1007/978-3-540-75759-7_34.
Magnetic Resonance Spectroscopy (MRS) along with MRI has emerged as a promising tool in diagnosis and potentially screening for prostate cancer. Surprisingly little work, however, has been done in the area of automated quantitative analysis of MRS data for identifying likely cancerous areas in the prostate. In this paper we present a novel approach that integrates a manifold learning scheme (spectral clustering) with an unsupervised hierarchical clustering algorithm to identify spectra corresponding to cancer on prostate MRS. Ground truth location for cancer on prostate was determined from the sextant location and maximum size of cancer available from the ACRIN database, from where a total of 14 MRS studies were obtained. The high dimensional information in the MR spectra is non linearly transformed to a low dimensional embedding space and via repeated clustering of the voxels in this space, non informative spectra are eliminated and only informative spectra retained. Our scheme successfully identified MRS cancer voxels with sensitivity of 77.8%, false positive rate of 28.92%, and false negative rate of 20.88% on a total of 14 prostate MRS studies. Qualitative results seem to suggest that our method has higher specificity compared to a popular scheme, z-score, routinely used for analysis of MRS data.
磁共振波谱（MRS）与磁共振成像（MRI）一起，已成为诊断和潜在筛查前列腺癌的一种有前景的工具。然而，在对MRS数据进行自动定量分析以识别前列腺中可能的癌性区域方面，所做的工作出奇地少。在本文中，我们提出了一种新颖的方法，该方法将流形学习方案（谱聚类）与无监督层次聚类算法相结合，以识别前列腺MRS上与癌症对应的波谱。前列腺癌的真实位置是根据ACRIN数据库中可用的癌症六分仪位置和最大尺寸确定的，从该数据库中总共获得了14项MRS研究。MR波谱中的高维信息被非线性转换到低维嵌入空间，并通过对该空间中的体素进行重复聚类，消除无信息的波谱，仅保留有信息的波谱。在总共14项前列腺MRS研究中，我们的方案成功识别出MRS癌性体素，灵敏度为77.8%，假阳性率为28.92%，假阴性率为20.88%。定性结果似乎表明，与通常用于分析MRS数据的流行方案z分数相比，我们的方法具有更高的特异性。
