MR Elastography in Cancer

A preoperative nomogram with MR elastography in identifying cytokeratin 19 status of hepatocellular carcinoma

OBJECTIVES

Developing a nomogram integrating MR elastography (MRE)-based tumor stiffness and contrast-enhanced MRI in identifying cytokeratin 19 (CK19) status of hepatocellular carcinoma (HCC) preoperatively.

METHODS

120 CK19-negative HCC and 39 CK19-positive HCC patients undergoing curative resection were prospectively evaluated. All received MRE and contrast-enhanced MRI. Clinical and MRI tumor features were compared. Univariate and multivariate logistic regression analyses identified independent predictors for CK19 status. Receiver operating characteristic curve analysis evaluated diagnostic performance. A nomogram was established with calibration and decision curve analysis.

RESULTS

Multivariate analysis revealed serum alpha fetoprotein (AFP) level (P < 0.001), targetoid appearance (P = 0.007), and tumor stiffness (P = 0.011) as independent significant variables for CK19-positive HCC. The area under the curve for tumor stiffness was 0.729 (95% CI 0.653, 0.796). Combining these features, a nomogram-based model achieved an area under the curve value of 0.844 (95% CI 0.778, 0.897), with sensitivity, specificity, and accuracy of 76.92%, 85.00%, and 83.02%, respectively. Calibration and decision curve analyses demonstrated good agreement and optimal net benefit.

CONCLUSIONS

MRE-measured tumor stiffness aids in predicting CK19 status in HCC. The combined nomogram incorporating tumor stiffness, targetoid appearance, and AFP provides a reliable biomarker for CK19-positive HCC.

ADVANCES IN KNOWLEDGE

MRE-measured tumor stiffness can be used to predict CK19 status in HCC. The nomogram, which integrates tumor stiffness, targetoid appearance, and AFP levels, has shown enhanced diagnostic performance. It offers a comprehensive preoperative tool for clinical decision-making, further advancing personalized treatment strategies in HCC management.

MRI Tomoelastography to Assess the Combined Status of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma

BACKGROUND

Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking.

PURPOSE

To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC.

STUDY TYPE

Retrospective.

POPULATION

One hundred sixty-eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative-VM and positive-VM groups with mild or severe-VMs. Of them, 127 patients completed the follow-up (median: 26.1 months).

FIELD STRENGTH/SEQUENCE

3D multifrequency tomoelastography with a single-shot spin-echo echo-planar imaging sequence, and liver MRI including T1-weighted in-phase and opposed-phase gradient echo (GRE), T2-weighted turbo spin echo, diffusion-weighted imaging and dynamic contrast-enhanced T1-weighted GRE sequences at 3.0 T.

ASSESSMENT

Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features.

STATISTICAL TESTS

Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log-rank test. P < 0.05 was considered statistically significant.

RESULTS

Tumor-to-liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive-VM and severe-VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive-VM (0.84 [95% CI: 0.72-0.93] vs. 0.77 [95% CI: 0.64-0.88]) and severe-VM (0.86 [95% CI: 0.68-0.96] vs. 0.75 [95% CI: 0.55-0.89]). Patients with estimated positive-VM (9.3 months)/severe-VM (9.2 months) based on nomograms had shorter median recurrence-free survival than those with estimated negative-VM (>20.0 months)/mild-VM (18.0 months) in validation cohort.

DATA CONCLUSION

Tomoelastography based-nomograms showed good performance for noninvasively assessing VM status in patients with HCC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Multifrequency Magnetic Resonance Elastography Detects Small Abdominal Lymph Node Metastasis by High Stiffness

OBJECTIVES

Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 is a clinical and research standard for evaluating malignant tumors and lymph node metastasis. However, quantitative analysis of nodal status is limited to measurement of short axis diameter (SAD), and metastatic lymph nodes below 10 mm in SAD are often not detected. The purpose of this study was to evaluate the value of multifrequency magnetic resonance elastography (MRE) when added to RECIST 1.1 for detection of lymph node metastasis.

MATERIALS AND METHODS

Twenty-five benign and 82 metastatic lymph nodes were prospectively examined by multifrequency MRE at 1.5 T using tomoelastography postprocessing at 30, 40, 50, and 60 Hz (total scan time of 4 minutes). Shear wave speed as a surrogate of soft tissue stiffness was provided in m/s. Positron emission tomography-computed tomography was used as reference standard for identification of abdominal lymph node metastasis from histologically confirmed primary tumors. The diagnostic performance of MRE was compared with that of SAD according to RECIST 1.1 and evaluated by receiver operating characteristic curve analysis using generalized linear mixed models and binary logistic mixed models. Sensitivity, specificity, and predictive values were calculated for different cutoffs.

RESULTS

Metastatic lymph nodes (1.90 ± 0.57 m/s) were stiffer than benign lymph nodes (0.98 ± 0.20 m/s, P < 0.001). An area under the curve of 0.95 for a cutoff of 1.32 m/s was calculated. Using a conservative approach with 1.0 specificity, we found sensitivity (SAD/MRE/MRE + SAD, 0.56/0.84/0.88), negative predictive values (0.41/0.66/0.71), and overall accuracy (0.66/0.88/0.91) to be improved using MRE and even higher for combined MRE and SAD.

CONCLUSIONS

Multifrequency MRE improves metastatic abdominal lymph node detection by 25% based on higher tissue stiffness-even for lymph nodes with an SAD ≤10 mm. Stiffness information is quick to obtain and would be a promising supplement to RECIST.

Evaluating stiffness of gastric wall using laser resonance frequency analysis for gastric cancer

Tumor stiffness is drawing attention as a novel axis that is orthogonal to existing parameters such as pathological examination. We developed a new diagnostic method that focuses on differences in stiffness between tumor and normal tissue. This study comprised a clinical application of laser resonance frequency analysis (L-RFA) for diagnosing gastric cancer. L-RFA allows for precise and contactless evaluation of tissue stiffness. We used a laser to create vibrations on a sample's surface that were then measured using a vibrometer. The data were averaged and analyzed to enhance accuracy. In the agarose phantom experiments, a clear linear correlation was observed between the Young's modulus of the phantoms (0.34-0.71 MPa) and the summation of normalized vibration peaks (Score) in the 1950-4050 Hz range (R2 = 0.93145). Higher Young's moduli also resulted in lower vibration peaks at the excitation frequency, signal-to-noise (S/N) ratios, and harmonic peaks. We also conducted L-RFA measurements on gastric cancer specimens from two patients who underwent robot-assisted distal gastrectomy. Our results revealed significant waveform differences between tumor and normal regions, similar to the findings in agarose phantoms, with tumor regions exhibiting lower vibration peaks at the excitation frequency, S/N ratios, and harmonic peaks. Statistical analysis confirmed significant differences in the score between normal and tumor regions (p = 0.00354). L-RFA was able to assess tumor stiffness and holds great promise as a noninvasive diagnostic tool for gastric cancer.

In vivo characterization of brain tumor biomechanics: magnetic resonance elastography in intracranial B16 melanoma and GL261 glioma mouse models

Introduction

Magnetic Resonance Elastography (MRE) allows the non-invasive quantification of tumor biomechanical properties in vivo. With increasing incidence of brain metastases, there is a notable absence of appropriate preclinical models to investigate their biomechanical characteristics. Therefore, the purpose of this work was to assess the biomechanical characteristics of B16 melanoma brain metastases (MBM) and compare it to murine GL261 glioblastoma (GBM) model using multifrequency MRE with tomoelastography post processing.

Methods

Intracranial B16 MBM (n = 6) and GL261 GBM (n = 7) mouse models were used. Magnetic Resonance Imaging (MRI) was performed at set intervals after tumor implantation: 5, 7, 12, 14 days for MBM and 13 and 22 days for GBM. The investigations were performed using a 7T preclinical MRI with 20 mm head coil. The protocol consisted of single-shot spin echo-planar multifrequency MRE with tomoelastography post processing, contrast-enhanced T1- and T2-weighted imaging and diffusion-weighted imaging (DWI) with quantification of apparent diffusion coefficient of water (ADC). Elastography quantified shear wave speed (SWS), magnitude of complex MR signal (T2/T2*) and loss angle (φ). Immunohistological investigations were performed to assess vascularization, blood-brain-barrier integrity and extent of glucosaminoglucan coverage.

Results

Volumetric analyses displayed rapid growth of both tumor entities and softer tissue properties than healthy brain (healthy: 5.17 ± 0.48, MBM: 3.83 ± 0.55, GBM: 3.7 ± 0.23, [m/s]). SWS of MBM remained unchanged throughout tumor progression with decreased T2/T2* intensity and increased ADC on days 12 and 14 (p<0.0001 for both). Conversely, GBM presented reduced φ values on day 22 (p=0.0237), with no significant alterations in ADC. Histological analysis revealed substantial vascularization and elevated glycosaminoglycan content in both tumor types compared to healthy contralateral brain.

Discussion

Our results indicate that while both, MBM and GBM, exhibited softer properties compared to healthy brain, imaging and histological analysis revealed different underlying microstructural causes: hemorrhages in MBM and increased vascularization and glycosaminoglycan content in GBM, further corroborated by DWI and T2/T2* contrast. These findings underscore the complementary nature of MRE and its potential to enhance our understanding of tumor characteristics when used alongside established techniques. This comprehensive approach could lead to improved clinical outcomes and a deeper understanding of brain tumor pathophysiology.

Assessment of Tumor Cell Invasion and Radiotherapy Response in Experimental Glioma by Magnetic Resonance Elastography

BACKGROUND

Gliomas are highly invasive brain neoplasms. MRI is the most important tool to diagnose and monitor glioma but has shortcomings. In particular, the assessment of tumor cell invasion is insufficient. This is a clinical dilemma, as recurrence can arise from MRI-occult glioma cell invasion.

HYPOTHESIS

Tumor cell invasion, tumor growth and radiotherapy alter the brain parenchymal microstructure and thus are assessable by diffusion tensor imaging (DTI) and MR elastography (MRE).

STUDY TYPE

Experimental, animal model.

ANIMAL MODEL

Twenty-three male NMRI nude mice orthotopically implanted with S24 patient-derived glioma cells (experimental mice) and 9 NMRI nude mice stereotactically injected with 1 μL PBS (sham-injected mice).

FIELD STRENGTH/SEQUENCE

2D and 3D T2-weighted rapid acquisition with refocused echoes (RARE), 2D echo planar imaging (EPI) DTI, 2D multi-slice multi-echo (MSME) T2 relaxometry, 3D MSME MRE at 900 Hz acquired at 9.4 T (675 mT/m gradient strength).

ASSESSMENT

Longitudinal 4-weekly imaging was performed for up to 4 months. Tumor volume was assessed in experimental mice (n = 10 treatment-control, n = 13 radiotherapy). The radiotherapy subgroup and 5 sham-injected mice underwent irradiation (3 × 6 Gy) 9 weeks post-implantation/sham injection. MRI-/MRE-parameters were assessed in the corpus callosum and tumor core/injection tract. Imaging data were correlated to light sheet microscopy (LSM) and histology.

STATISTICAL TESTS

Paired and unpaired t-tests, a P-value ≤0.05 was considered significant.

RESULTS

From week 4 to 8, a significant callosal stiffening (4.44 ± 0.22 vs. 5.31 ± 0.29 kPa) was detected correlating with LSM-proven tumor cell invasion. This was occult to all other imaging metrics. Histologically proven tissue destruction in the tumor core led to an increased T2 relaxation time (41.65 ± 0.34 vs. 44.83 ± 0.66 msec) and ADC (610.2 ± 12.27 vs. 711.2 ± 13.42 × 10-6 mm2/s) and a softening (5.51 ± 0.30 vs. 4.24 ± 0.29 kPa) from week 8 to 12. Radiotherapy slowed tumor progression.

DATA CONCLUSION

MRE is promising for the assessment of key glioma characteristics.

EVIDENCE LEVEL

NA TECHNICAL EFFICACY: Stage 2.

Viscoelastic properties of colorectal liver metastases reflect tumour cell viability

BACKGROUND

Colorectal cancer is the third most common tumour entity in the world and up to 50% of the patients develop liver metastases (CRLM) within five years. To improve and personalize therapeutic strategies, new diagnostic tools are urgently needed. For instance, biomechanical tumour properties measured by magnetic resonance elastography (MRE) could be implemented as such a diagnostic tool. We postulate that ex vivo MRE combined with histological and radiological evaluation of CRLM could provide biomechanics-based diagnostic markers for cell viability in tumours.

METHODS

34 CRLM specimens from patients who had undergone hepatic resection were studied using ex vivo MRE in a frequency range from 500 Hz to 5300 Hz with increments of 400 Hz. Single frequency evaluation of shear wave speed and wave penetration rate as proxies for stiffness and viscosity was performed, along with rheological model fitting based on the spring-pot model and powerlaw exponent α, ranging between 0 (complete solid behaviour) and 1 (complete fluid behaviour). For histological analysis, samples were stained with H&E and categorized according to the degree of regression. Quantitative histologic analysis was performed to analyse nucleus size, aspect ratio, and density. Radiological response was assessed according to RECIST-criteria.

RESULTS

Five samples showed major response to chemotherapy, six samples partial response and 23 samples no response. For higher frequencies (> 2100 Hz), shear wave speed correlated significantly with the degree of regression (p ≤ 0.05) indicating stiffer properties with less viable tumour cells. Correspondingly, rheological analysis of α revealed more elastic-solid tissue properties at low cell viability and major response (α = 0.43 IQR 0.36, 0.47) than at higher cell viability and no response (α = 0.51 IQR 0.48, 0.55; p = 0.03). Quantitative histological analysis showed a decreased nuclear area and density as well as a higher nuclear aspect ratio in patients with major response to treatment compared to patients with no response (all p < 0.05).

DISCUSSION

Our results suggest that MRE could be useful in the characterization of biomechanical property changes associated with cell viability in CRLM. In the future, MRE could be applied in clinical diagnosis to support individually tailored therapy plans for patients with CRLM.

The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain

Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.

Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study

OBJECTIVES

Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours.

METHODS

This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed.

RESULTS

The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values.

CONCLUSION

Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

Changes in Tissue Fluidity Predict Tumor Aggressiveness In Vivo

Cancer progression is caused by genetic changes and associated with various alterations in cell properties, which also affect a tumor's mechanical state. While an increased stiffness has been well known for long for solid tumors, it has limited prognostic power. It is hypothesized that cancer progression is accompanied by tissue fluidization, where portions of the tissue can change position across different length scales. Supported by tabletop magnetic resonance elastography (MRE) on stroma mimicking collagen gels and microscopic analysis of live cells inside patient derived tumor explants, an overview is provided of how cancer associated mechanisms, including cellular unjamming, proliferation, microenvironment composition, and remodeling can alter a tissue's fluidity and stiffness. In vivo, state-of-the-art multifrequency MRE can distinguish tumors from their surrounding host tissue by their rheological fingerprints. Most importantly, a meta-analysis on the currently available clinical studies is conducted and universal trends are identified. The results and conclusions are condensed into a gedankenexperiment about how a tumor can grow and eventually metastasize into its environment from a physics perspective to deduce corresponding mechanical properties. Based on stiffness, fluidity, spatial heterogeneity, and texture of the tumor front a roadmap for a prognosis of a tumor's aggressiveness and metastatic potential is presented.