Correlation of In Vivo and Ex Vivo ADC and T2 of In Situ and Invasive Murine Mammary Cancers

Assessing the Therapeutic Efficacy of Proton Transport Inhibitors in a Triple-Negative Breast Cancer Murine Model with Magnetic Resonance Imaging-Chemical Exchange Saturation Transfer Tumor pH Imaging

Proton transporters play a key role in maintaining the acidic tumor microenvironment; hence, their inhibition has been proposed as a new therapeutic treatment, although few methods can accurately assess their effect in vivo. In this study, we investigated whether MRI-CEST (Magnetic Resonance Imaging-Chemical Exchange Saturation Transfer) tumor pH imaging can be a useful tool to evaluate in vivo the therapeutic efficacy of several Proton Pump Inhibitors (PPIs) in breast cancer. Cell viability and extracellular pH assays were carried out in breast cancer cells cultured at physiological pH (7.4) or acid-adapted (pH of 6.5 and 6.8) following the exposure to inhibitors of V-ATPase (Lansoprazole, Esomeprazole) or NHE1 (Amiloride, Cariporide) at several concentrations. Next, triple-negative breast cancer 4T1 tumor-bearing mice were treated with Lansoprazole or Amiloride and MRI-CEST tumor pH imaging was utilized to assess the in vivo efficacy. Only Lansoprazole induced, in addition to breast cancer cell toxicity, a significant inhibition of proton extrusion. A significant reduction in tumor volume, prolonged survival, and increase in extracellular tumor pH after 1 and 2 weeks were observed after Lansoprazole treatment, whereas no significant changes were detected upon Amiloride treatment. Our results suggested that MRI-CEST tumor pH imaging can monitor the therapeutic efficacy of PPIs in breast cancer murine models.

Higher-order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

PURPOSE

Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher-order diffusion MRI models accounting for non-Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging.

METHODS

The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM-Kurtosis.

RESULTS

Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM-Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm² /ms) and malignant (1.02 ± 0.41 μm² /ms, P = .03) nodes. IVIM-Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T₂ -weighted imaging.

CONCLUSION

Higher-order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.

Retrieved cerebral thrombi studied by T2 and ADC mapping: preliminary results

Background

Recent advances in MRI technology makes it increasingly more competitive to CT also in the field of interventions. Multi-parametric MRI offers a significant amount of data relevant for characterization of human cerebral thrombi.

Patients and methods

Cerebral thrombi of 17 patients diagnosed with acute stroke were acquired by mechanical thrombectomy. The thrombi were subsequently scanned using a high spatial-resolution 3D T₁-weighted MRI to obtain morphological characteristics of the thrombi and also by apparent diffusion coefficient (ADC) and transversal nuclear magnetic resonance (NMR) relaxation time (T₂) mapping. The MRI results were analysed for possible correlations between thrombectomy procedure parameters (recanalization time and number of passes) and MR-measurable parameters (sample-mean ADC and T₂, within-sample coefficient of variation of ADC and T₂, and thrombus length).

Results

Both MRI mapping techniques enabled a good discrimination among thrombi regions of different water mobility and compaction. Within-sample coefficient of variation of ADC was found most sensitive for discrimination between the thrombi where thrombectomy procedure was performed in a single pass and those where is was performed in two or more passes (p = 0.03). Interestingly, negative correlation was found between the recanalization time and thrombus length (ρ = -0.22).

Conclusions

Preliminary results of presented study shows that pretreatment MRI assessment of thrombi in stroke patients could potentially ease stroke treatment planning. In this study it is shown that within-sample coefficient of variation of ADC could serve for prediction of possible complications during thrombectomy procedures.

Multiparametric High-Resolution MRI as a Tool for Mapping of Hypoxic Level in Tumors

Hypoxia is a condition, common to most malignant tumors, where oxygen tension in the tissue is below the physiological level. Among consequences of tumor hypoxia is also altered cancer cell metabolism that contributes to cancer therapy resistance. Therefore, precise assessment of tumor hypoxia is important for monitoring the tumor treatment progression. In this study, we propose a simple model for prediction of hypoxic level in tumors based on multiparametric magnetic resonance imaging. The study was performed on B16F1 murine melanoma tumors ex vivo that were first magnetic resonance scanned and then analyzed for hypoxic level using hypoxia-inducable factor 1-alpha antibody staining. Each tumor was analyzed in identical sections and in identical regions of interest for pairs of hypoxic level and magnetic resonance values (apparent diffusion coefficient and T₂). This was followed by correlation analysis between hypoxic level and respective magnetic resonance values. A moderate correlation was found between hypoxic level and apparent diffusion coefficient (ρ = 0.56, P < .00001) and lower between hypoxic level and T₂ (ρ = 0.38, P < .00001). The data were analyzed further to obtain simple predictive models based on the multiple linear regression analysis of the measured hypoxic level (dependent variable) and apparent diffusion coefficient and T₂ (independent variables). Among the hypoxic level models, the most efficient was the 3-parameter model given by relation (HL = k_ADCADC + k_T2T₂ + b), where k_ADC = 26%/µm²/ms, k_T2 = 0.8%/ms, and b = −32%. The model can be used for calculation of the predicted hypoxic level map based on magnetic resonance–measured apparent diffusion coefficient and T₂ maps. Similar prediction models, based on tumor apparent diffusion coefficient and T₂ maps, can be done also for other tumor types in vivo and can therefore help in assessment of tumor treatment as well as to better understand the role of hypoxia in cancer progression.