P-based referencing for correcting tissue artifacts in laser ablation-inductively coupled plasma-mass spectrometry imaging of cancer samples.

A referencing strategy based on the element P is presented to compensate for cryosectioning tissue artifacts in laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) data. The study examines how the gadolinium-based contrast agent Gadofosveset is distributed in murine cancer tissue, and illustrates how referenced images can compensate for tissue artifacts like folds, overlaps, and density variations. Compared to non-referenced images that provide information on the absolute distribution of the analyte, referenced images allow for the representation of the analyte distribution relative to the amount of material introduced into the instrument, which in this case is correlated to the P signal. Tissue artifacts were corrected in referenced images for both Gadofosveset and endogenous elements, such as Fe and Zn. Additionally, the referencing approach provides valuable information on the Gd uptake relative to the tissue density in necrotic compared to vital tumor areas, which is not obtained from in vivo magnetic resonance imaging (MRI) data. However, validation of in vivo MRI and ex vivo LA-ICP-MS methods was possible by establishing a mean ratio of necrotic to vital tumor areas in the T1-weighted image post Gadofosveset injection and the non-referenced LA-ICP-MS image of Gd. In summary, P-based correction of LA-ICP-MS imaging data allows for a more accurate spatial representation of certain elements, including endogenous and exogenous elements such as injected contrast agents.

Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer.

Objective: The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) approach. Methods: The highly metastatic 4T1 breast cancer model was compared to the non-metastatic 67NR model. Imaging was conducted on a 9.4 T small animal MRI. The protocol was used to characterize tumors regarding their structural composition, including heterogeneity, intratumoral edema and hemorrhage, as well as endothelial permeability using apparent diffusion coefficient (ADC), T1/T2 mapping and dynamic contrast-enhanced (DCE) imaging. Mice were assessed on either day three, six or nine, with an i.v. injection of the albumin-binding contrast agent gadofosveset. Ex vivo validation of the results was performed with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), histology, immunhistochemistry and electron microscopy. Results: Significant differences in tumor composition were observed over time and between 4T1 and 67NR tumors. 4T1 tumors showed distorted blood vessels with a thin endothelial layer, resulting in a slower increase in signal intensity after injection of the contrast agent. Higher permeability was further reflected in higher Ktrans values, with consecutive retention of gadolinium in the tumor interstitium visible in MRI. 67NR tumors exhibited blood vessels with a thicker and more intact endothelial layer, resulting in higher peak enhancement, as well as higher maximum slope and area under the curve, but also a visible wash-out of the contrast agent and thus lower Ktrans values. A decreasing accumulation of gadolinium during tumor progression was also visible in both models in LA-ICP-MS. Tissue composition of 4T1 tumors was more heterogeneous, with intratumoral hemorrhage and necrosis and corresponding higher T1 and T2 relaxation times, while 67NR tumors mainly consisted of densely packed tumor cells. Histogram analysis of ADC showed higher values of mean ADC, histogram kurtosis, range and the 90th percentile (p90), as markers for the heterogenous structural composition of 4T1 tumors. Principal component analysis (PCA) discriminated well between the two tumor models. Conclusions: Multiparametric MRI as presented in this study enables for the estimation of malignant potential in the two studied tumor models via the assessment of certain tumor features over time.