Magnetic resonance elastography with guided pressure waves.

Magnetic resonance elastography aims to non-invasively and remotely characterize the mechanical properties of living tissues. To quantitatively and regionally map the shear viscoelastic moduli in vivo, the technique must achieve proper mechanical excitation throughout the targeted tissues. Although it is straightforward, ante manibus, in close organs such as the liver or the breast, which practitioners clinically palpate already, it is somewhat fortunately highly challenging to trick the natural protective barriers of remote organs such as the brain. So far, mechanical waves have been induced in the latter by shaking the surrounding cranial bones. Here, the skull was circumvented by guiding pressure waves inside the subject's buccal cavity so mechanical waves could propagate from within through the brainstem up to the brain. Repeatable, reproducible and robust displacement fields were recorded in phantoms and in vivo by magnetic resonance elastography with guided pressure waves such that quantitative mechanical outcomes were extracted in the human brain.

Multifrequency magnetic resonance elastography for elasticity quantitation and optimal tissue discrimination: A two-platform liver fibrosis mimicking phantom study.

In the framework of algebraic inversion, magnetic resonance elastography (MRE) repeatability, reproducibility and robustness were evaluated on extracted shear velocities (or elastic moduli). The same excitation system was implemented at two sites equipped with clinical MR scanners of 1.5 and 3 T. A set of four elastic, isotropic, homogeneous calibrated phantoms of distinct elasticity representing the spectrum of liver fibrosis severity was mechanically characterized. The repeatability of the measurements and the reproducibility between the two platforms were found to be excellent with mean coefficients of variations of 1.62% for the shear velocity mean values and 1.95% for the associated standard deviations. MRE velocities were robust to the amplitude and pattern variations of the displacement field with virtually no difference between outcomes from both magnets at identical excitation frequencies, even when the displacement field amplitude was six times smaller. However, MRE outcomes were very sensitive to the number of voxels per wavelength, s, of the recorded displacement field, with relative biases reaching 62% and precision loss by a factor of up to 23.5. For both magnetic field strengths, MRE accuracy and precision were largely degraded outside of established conditions of validity (6 ≲ s ≲ 9), resulting in estimated shear velocity values not significantly different between phantoms of increasing elasticity. When fulfilling the spatial sampling conditions, either prospectively in the acquisition or retrospectively before the reconstruction, MRE produced quantitative measurements that allowed to unambiguously discriminate, with infinitesimal p values, between the phantoms mimicking increasing severity of liver fibrosis.

Assessing Tumor Mechanics by MR Elastography at Different Strain Levels.

BACKGROUND: Malignant tumors are associated with increased tissue rigidity, which can be an indicator of tumor progression. MR elastography (MRE) has the potential to study the variations of tumor mechanical properties. ex vivo studies have shown the ability of MRE to assess increase of mechanical properties; nevertheless, it has not yet been observed in vivo. PURPOSE: To propose a method to assess the increase in mechanical properties of tumors in vivo under static external compression using MRE. STUDY TYPE: Prospective, experimental study. ANIMAL MODEL: Forty-six SCID mice with subcutaneous tumor implantation (patient-derived hepatocellular carcinoma xenografts, Model 1, n = 13, and Model 2, n = 33). FIELD STRENGTH/SEQUENCE: 7.0T; a spin echo sequence was used for anatomical images and a modified spin echo sequence for elastography acquisitions with a vibration frequency of 600 Hz. ASSESSMENT: An inflatable balloon was placed on the abdomen to apply a load to the tumor. MRE acquisitions were performed at the basal state and at increasing compression levels. Anatomical images were used to calculate the octahedral shear strain between the tumor at the basal strain state and each strain level. For six mice (Model 2), each static preloading scan was acquired twice consecutively without moving the mouse to evaluate repeatability. Statistical Tests: The Bland-Altman method was used to assess repeatability. Correlations between tumor stiffness and deformation were evaluated with Pearson correlation coefficients. RESULTS: For stiffness (G*), a good repeatability was obtained between the acquisitions; the limits of agreement of the Bland-Altman test were [-10.17%; 11.49%] with an absolute bias of 0.66%. A significant correlation between tumor stiffness and deformation was observed for both models (Model 1: r = 0.57, P < 0.0001 and Model 2: r = 0.31, P < 0.0001). DATA CONCLUSION: We establish that tumor mechanical properties can increase under mechanical compression. This increase can effectively be monitored using a proposed MRE setup. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1982-1989.

Radiomics: an Introductory Guide to What It May Foretell.

PURPOSE OF REVIEW: To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine. RECENT FINDINGS: Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.

Robustness of radiomics features on 0.35 T magnetic resonance imaging for magnetic resonance-guided radiotherapy.

Background and purpose: MR-guided radiotherapy adds the precision of magnetic resonance imaging (MRI) to the therapeutic benefits of a linear accelerator. Prior to each therapeutic session, an MRI generates a significant volume of imaging data ripe for analysis. Radiomics stands at the forefront of medical imaging and oncology research, dedicated to mining quantitative imaging attributes to forge predictive models. However, the robustness of these models is often challenged. Materials and methods: To assess the robustness of feature extraction, we conducted reproducibility studies using a 0.35 T MR-linac system, employing both a specialized phantom and patient-derived images, focusing on cases of pancreatic cancer. We extracted shape-based, first-order and textural features from patient-derived images and only first-order and textural features from phantom-derived images. The impact of the delay between simulation and first fraction images was also assessed with an equivalence test. Results: From 107 features evaluated, 58 (54 %) were considered as non-reproducible: 18 were uniformly inconsistent across both phantom and patient images, 9 were specific to phantom-based analysis, and 31 to patient-derived data. Conclusion: Our findings show that a significant proportion of radiomic features extracted from this dual dataset were unreliable. It is essential to discard these non-reproducible elements to refine and enhance radiomic model development, particularly for MR-guided radiotherapy in pancreatic cancer.

Radiomics in radiation oncology for gynecological malignancies: a review of literature.

Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.

Tumor Solid Stress: Assessment with MR Elastography under Compression of Patient-Derived Hepatocellular Carcinomas and Cholangiocarcinomas Xenografted in Mice.

Malignant tumors have abnormal biomechanical characteristics, including high viscoelasticity, solid stress, and interstitial fluid pressure. Magnetic resonance (MR) elastography is increasingly used to non-invasively assess tissue viscoelasticity. However, solid stress and interstitial fluid pressure measurements are performed with invasive methods. We studied the feasibility and potential role of MR elastography at basal state and under controlled compression in assessing altered biomechanical features of malignant liver tumors. MR elastography was performed in mice with patient-derived, subcutaneously xenografted hepatocellular carcinomas or cholangiocarcinomas to measure the basal viscoelasticity and the compression stiffening rate, which corresponds to the slope of elasticity versus applied compression. MR elastography measurements were correlated with invasive pressure measurements and digital histological readings. Significant differences in MR elastography parameters, pressure, and histological measurements were observed between tumor models. In multivariate analysis, collagen content and interstitial fluid pressure were determinants of basal viscoelasticity, whereas solid stress, in addition to collagen content, cellularity, and tumor type, was an independent determinant of compression stiffening rate. Compression stiffening rate had high AUC (0.87 ± 0.08) for determining elevated solid stress, whereas basal elasticity had high AUC for tumor collagen content (AUC: 0.86 ± 0.08). Our results suggest that MR elastography compression stiffening rate, in contrast to basal viscoelasticity, is a potential marker of solid stress in malignant liver tumors.

Assessing Histology Structures by Ex Vivo MR Microscopy and Exploring the Link Between MRM-Derived Radiomic Features and Histopathology in Ovarian Cancer.

The value of MR radiomic features at a microscopic scale has not been explored in ovarian cancer. The objective of this study was to probe the associations of MR microscopy (MRM) images and MRM-derived radiomic maps with histopathology in high-grade serous ovarian cancer (HGSOC). Nine peritoneal implants from 9 patients with HGSOC were imaged ex vivo with MRM using a 9.4-T MR scanner. All MRM images and computed pixel-wise radiomics maps were correlated with the slice-matched stroma and tumor proportion maps derived from whole histopathologic slide images (WHSI) of corresponding peritoneal implants. Automated MRM-derived segmentation maps of tumor and stroma were constructed using holdout test data and validated against the histopathologic gold standard. Excellent correlation between MRM images and WHSI was observed (Dice index = 0.77). Entropy, correlation, difference entropy, and sum entropy radiomic features were positively associated with high stromal proportion (r = 0.97,0.88, 0.81, and 0.96 respectively, p < 0.05). MR signal intensity, energy, homogeneity, auto correlation, difference variance, and sum average were negatively associated with low stromal proportion (r = -0.91, -0.93, -0.94, -0.9, -0.89, -0.89, respectively, p < 0.05). Using the automated model, MRM predicted stromal proportion with an accuracy ranging from 61.4% to 71.9%. In this hypothesis-generating study, we showed that it is feasible to resolve histologic structures in HGSOC using ex vivo MRM at 9.4 T and radiomics.

Acquisition and reconstruction conditions in silico for accurate and precise magnetic resonance elastography.

Magnetic resonance elastography (MRE) is a non invasive imaging modality, which holds the promise of absolute quantification of the mechanical properties of human tissues in vivo. MRE reconstruction with algebraic inversion of the Helmholtz equation upon the curl of the shear displacement field may theoretically be flawless. However, its performances are challenged by multiple experimental parameters, especially the frequency and the amplitude of the mechanical wave, the voxel size and the signal-to-noise ratio of the MRE acquisition. A point source excitation was simulated and realistic displacement fields were analytically computed to simulate MRE data sets in an isotropic, homogeneous, linearly-elastic, and half-space infinite medium. Acquisition and reconstruction methods were challenged and the joint influence of the aforementioned parameters was studied. For a given signal-to-noise ratio, the conditions on the number of voxels per wavelength were determined for optimizing voxel-wise accuracy and precision in MRE. It was shown that, once data are acquired, the reconstruction quality could even be improved by effective interpolation or decimation so data could eventually fulfill favorable conditions for mechanical characterization of the tissue. Finally, the overall outcome, which is usually computed from the three acquired motion-encoded directions, may further be improved by appropriate averaging strategies that are based on adapted curl of shear displacement field quality-weighting.