Elham Karami^{1,2,3}, Wilfred Lam^{2}, Wendy Oakden^{2}, Margaret Koletar^{2}, Leedan Murray^{2}, Stanley Liu^{1,4,5,6}, Ali Sadeghi Naini^{1,2,3,6}, and Greg Stanisz^{1,2,7}

Chemical exchange saturation transfer (CEST) is a promising MR contrast mechanism that has been shown to correlate with cancer metabolism and reveal regions of active tumour metabolism. However, the acquisition of CEST-weighted images is time consuming. In this study, computational methods including unsupervised learning were adapted to find the minimum number of CEST images required to segment the intra-tumour distinct metabolic regions accurately, and to find the number of different cell groups existing within a tumour. The results indicate that four intra-tumour regions can be segmented accurately using only CEST images acquired at 3.5 ppm and 2.0 ppm.

* Animal model:*
The model included 13 tumours grown in the hind limb of nude mice from two cell
lines: DU145 human prostate
adenocarcinoma (ATCC, Manassas, VA) and a radiation-resistant line generated by
treatment of the former cell line with radiation mimicking a clinical treatment
schedule

** Dataset:** Z-spectra for
each tumour were acquired using magnetization transfer-prepared FLASH with
steady-state saturation B

** Data analysis:** The optimal number of data points required for
accurate segmentation of intra-tumour metabolic regions, as well as the optimal
number of clusters was computed through minimizing the Bayesian information
criterion (BIC) for clustering, and using the elbow method

1. Lam WW, Oakden W, Murray L, et al. Differentiation of normal and radioresistant prostate cancer xenografts using magnetization transfer-prepared MRI. Sci Rep. 2018. doi:10.1038/s41598-018-28731-0

2. Kodinariya TM, Makwana PR. Review on determining number of Cluster in K-Means Clustering. IJARCSMS. 2013.

3. Hyvärinen A, Oja E. Independent component analysis: Algorithms and applications. Neural Networks. 2000. doi:10.1016/S0893-6080(00)00026-5

4. Reynolds D. Gaussian Mixture Models. In: Encyclopedia of Biometrics. ; 2015. doi:10.1007/978-1-4899-7488-4_196

Figure 1. Using the elbow method to determine the optimal number of
clusters among all the tumours using spherical, tied, diagonal, and full
kernels for the Gaussian mixture model (GMM). The Bayesian information
criterion (BIC) was used as the metric, and the asterisk denotes the selected
model.

Figure 2. Z-spectra (B_{1}
= 2 µT) averaged over all tumours for each cluster.

Figure 3. (a), (b) CEST at 3.5,
and 2.0 ppm. (c), (d) Independent components (IC) of the CEST data. (e) T_{2}
map. (f) Clusters. (g) H&E stained tissue section.