A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from Magnetic Resonance Spectroscopy (MRS)

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.