Saturation transfer properties of tumour xenografts derived from prostate cancer cell lines 22Rv1 and DU145.

Histopathology is currently the most reliable tool in assessing the aggressiveness and prognosis of solid tumours. However, developing non-invasive modalities for tumour evaluation remains crucial due to the side effects and complications caused by biopsy procedures. In this study, saturation transfer MRI was used to investigate the microstructural and metabolic properties of tumour xenografts in mice derived from the prostate cancer cell lines 22Rv1 and DU145, which express different aggressiveness. The magnetization transfer (MT) and chemical exchange saturation transfer (CEST) effects, which are associated with the microstructural and metabolic properties in biological tissue, respectively, were analyzed quantitatively and compared amongst different tumour types and regions. Histopathological staining was performed as a reference. Higher cellular density and metabolism expressed in more aggressive tumours (22Rv1) were associated with larger MT and CEST effects. High collagen content in the necrotic regions might explain their higher MT effects compared to tumour regions.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.

Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.

Differentiation of Normal and Radioresistant Prostate Cancer Xenografts Using Magnetization Transfer-Prepared MRI.

The ability of MRI to differentiate between normal and radioresistant cancer was investigated in prostate tumour xenografts in mice. Specifically, the process of magnetization exchange between water and other molecules was studied. It was found that magnetization transfer from semisolid macromolecules (MT) and chemical exchange saturation transfer (CEST) combined were significantly different between groups (p < 0.01). Further, the T2 relaxation of the semisolid macromolecular pool (T2,B), a parameter specific to MT, was found to be significantly different (p < 0.01). Also significantly different were the rNOE contributions associated with methine groups at -0.9 ppm with a saturation B1 of 0.5 µT (p < 0.01) and with other aliphatic groups at -3.3 ppm with 0.5 and 2 µT (both p < 0.05). Independently, using a live-cell metabolic assay, normal cells were found to have a greater metabolic rate than radioresistant ones. Thus, MRI provides a novel, in vivo method to quantify the metabolic rate of tumours and predict their radiosensitivity.