Dataset Information

Multi-modal magnetic resonance elastography for noninvasive assessment of ovarian tissue rigidity in vivo.

ABSTRACT: For centuries, physicians have relied on touch to palpate tissue and detect abnormalities throughout the body. While this time-tested method has provided a simple diagnostic examination for large, superficial abnormalities, it does not permit quantifiable measurements of stiffness in deeper, small organs. Advances in noninvasive imaging to measure tissue rigidity represent important extensions of manual palpation techniques. Tissue fibrosis occurs with age in many organs; in the ovary, it is thought to be a marker of polycystic ovary syndrome and age-related idiopathic infertility, although quantitative assessment of fibrosis in this deep, abdominal tissue has not been possible. We used noninvasive methods to quantify ovarian tissue rigidity and clarify the role of tissue stiffness in reproductive health. With proper validation against accepted standards, noninvasive imaging techniques may become the quantitative counterpart to interior probing palpation methods and invasive (surgical) diagnoses, with applications across many clinical settings, including evaluation of adolescent and young adult ovarian function.

SUBMITTER: Wood CD

PROVIDER: S-EPMC4295766 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Multi-modal magnetic resonance elastography for noninvasive assessment of ovarian tissue rigidity in vivo.

Wood Charles D CD Vijayvergia Mayank M Miller Frank H FH Carroll Timothy T Fasanati Charles C Shea Lonnie D LD Brinson L Catherine LC Woodruff Teresa K TK

Acta biomaterialia 20141120

For centuries, physicians have relied on touch to palpate tissue and detect abnormalities throughout the body. While this time-tested method has provided a simple diagnostic examination for large, superficial abnormalities, it does not permit quantifiable measurements of stiffness in deeper, small organs. Advances in noninvasive imaging to measure tissue rigidity represent important extensions of manual palpation techniques. Tissue fibrosis occurs with age in many organs; in the ovary, it is tho ...[more]

PMID: 25463483

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Project description:The current state-of-the-art diagnosis method for deep tissue injury in muscle, a subcategory of pressure ulcers, is palpation. It is recognized that deep tissue injury is frequently preceded by altered biomechanical properties. A quantitative understanding of the changes in biomechanical properties preceding and during deep tissue injury development is therefore highly desired. In this paper we quantified the spatial-temporal changes in mechanical properties upon damage development and recovery in a rat model of deep tissue injury. Deep tissue injury was induced in nine rats by two hours of sustained deformation of the tibialis anterior muscle. Magnetic resonance elastography (MRE), T2 -weighted, and T2 -mapping measurements were performed before, directly after indentation, and at several timepoints during a 14-day follow-up. The results revealed a local hotspot of elevated shear modulus (from 3.30 ± 0.14 kPa before to 4.22 ± 0.90 kPa after) near the center of deformation at Day 0, whereas the T2 was elevated in a larger area. During recovery there was a clear difference in the time course of the shear modulus and T2 . Whereas T2 showed a gradual normalization towards baseline, the shear modulus dropped below baseline from Day 3 up to Day 10 (from 3.29 ± 0.07 kPa before to 2.68 ± 0.23 kPa at Day 10, P < 0.001), followed by a normalization at Day 14. In conclusion, we found an initial increase in shear modulus directly after two hours of damage-inducing deformation, which was followed by decreased shear modulus from Day 3 up to Day 10, and subsequent normalization. The lower shear modulus originates from the moderate to severe degeneration of the muscle. MRE stiffness values were affected in a smaller area as compared with T2 . Since T2 elevation is related to edema, distributing along the muscle fibers proximally and distally from the injury, we suggest that MRE is more specific than T2 for localization of the actual damaged area.

Project description:Background & aimsMagnetic resonance imaging (MRI) techniques and ultrasound-based transient elastography (TE) can be used in noninvasive diagnosis of fibrosis and steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We performed a prospective study to compare the performance of magnetic resonance elastography (MRE) vs TE for diagnosis of fibrosis, and MRI-based proton density fat fraction (MRI-PDFF) analysis vs TE-based controlled attenuation parameter (CAP) for diagnosis of steatosis in patients undergoing biopsy to assess NAFLD.MethodsWe performed a cross-sectional study of 104 consecutive adults (56.7% female) who underwent MRE, TE, and liver biopsy analysis (using the histologic scoring system for NAFLD from the Nonalcoholic Steatohepatitis Clinical Research Network Scoring System) from October 2011 through May 2016 at a tertiary medical center. All patients received a standard clinical evaluation, including collection of history, anthropometric examination, and biochemical tests. The primary outcomes were fibrosis and steatosis. Secondary outcomes included dichotomized stages of fibrosis and nonalcoholic steatohepatitis vs no nonalcoholic steatohepatitis. Receiver operating characteristic curve analyses were used to compare performances of MRE vs TE in diagnosis of fibrosis (stages 1-4 vs 0) and MRI-PDFF vs CAP for diagnosis of steatosis (grades 1-3 vs 0) with respect to findings from biopsy analysis.ResultsMRE detected any fibrosis (stage 1 or more) with an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% confidence interval [CI], 0.74-0.91), which was significantly higher than that of TE (AUROC, 0.67; 95% CI, 0.56-0.78). MRI-PDFF detected any steatosis with an AUROC of 0.99 (95% CI, 0.98-1.00), which was significantly higher than that of CAP (AUROC, 0.85; 95% CI, 0.75-0.96). MRE detected fibrosis of stages 2, 3, or 4 with AUROC values of 0.89 (95% CI, 0.83-0.96), 0.87 (95% CI, 0.78-0.96), and 0.87 (95% CI, 0.71-1.00); TE detected fibrosis of stages 2, 3, or 4 with AUROC values of 0.86 (95% CI, 0.77-0.95), 0.80 (95% CI, 0.67-0.93), and 0.69 (95% CI, 0.45-0.94). MRI-PDFF identified steatosis of grades 2 or 3 with AUROC values of 0.90 (95% CI, 0.82-0.97) and 0.92 (95% CI, 0.84-0.99); CAP identified steatosis of grades 2 or 3 with AUROC values of 0.70 (95% CI, 0.58-0.82) and 0.73 (95% CI, 0.58-0.89).ConclusionsIn a prospective, cross-sectional study of more than 100 patients, we found MRE to be more accurate than TE in identification of liver fibrosis (stage 1 or more), using biopsy analysis as the standard. MRI-PDFF is more accurate than CAP in detecting all grades of steatosis in patients with NAFLD.

Project description:ObjectivesUsing maximum diameter of an abdominal aortic aneurysm (AAA) alone for management can lead to delayed interventions or unnecessary urgent repairs. Abdominal aortic aneurysm stiffness plays an important role in its expansion and rupture. In vivo aortic magnetic resonance elastography (MRE) was developed to spatially measure AAA stiffness in previous pilot studies and has not been thoroughly validated and evaluated for its potential clinical value. This study aims to evaluate noninvasive in vivo aortic MRE-derived stiffness in an AAA porcine model and investigate the relationships between MRE-derived AAA stiffness and (1) histopathology, (2) uniaxial tensile test, and (3) burst testing for assessing MRE's potential in evaluating AAA rupture risk.Materials and methodsAbdominal aortic aneurysm was induced in 31 Yorkshire pigs (n = 226 stiffness measurements). Animals were randomly divided into 3 cohorts: 2-week, 4-week, and 4-week-burst. Aortic MRE was sequentially performed. Histopathologic analyses were performed to quantify elastin, collagen, and mineral densities. Uniaxial tensile test and burst testing were conducted to measure peak stress and burst pressure for assessing the ultimate wall strength.ResultsMagnetic resonance elastography-derived AAA stiffness was significantly higher than the normal aorta. Significant reduction in elastin and collagen densities as well as increased mineralization was observed in AAAs. Uniaxial tensile test and burst testing revealed reduced ultimate wall strength. Magnetic resonance elastography-derived aortic stiffness correlated to elastin density (ρ = -0.68; P < 0.0001; n = 60) and mineralization (ρ = 0.59; P < 0.0001; n = 60). Inverse correlations were observed between aortic stiffness and peak stress (ρ = -0.32; P = 0.0495; n = 38) as well as burst pressure (ρ = -0.55; P = 0.0116; n = 20).ConclusionsNoninvasive in vivo aortic MRE successfully detected aortic wall stiffening, confirming the extracellular matrix remodeling observed in the histopathologic analyses. These mural changes diminished wall strength. Inverse correlation between MRE-derived aortic stiffness and aortic wall strength suggests that MRE-derived stiffness can be a potential biomarker for clinically assessing AAA wall status and rupture potential.

Project description:ObjectivesGliomas with comutations of isocitrate dehydrogenase (IDH) genes and telomerase reverse transcriptase (TERT) gene promoter (IDHmut pTERTmut) show distinct biological features and respond to first-line treatment differently in comparison with other gliomas. This study aimed to characterize the IDHmut pTERTmut gliomas in multimodal MRI using the radiomic method and establish a precise diagnostic model identifying this group of gliomas.MethodsA total of 140 patients with untreated primary gliomas were admitted between 2016 and 2020 to West China Hospital as a discovery cohort, including 22 IDHmut pTERTmut patients. Thirty-four additional cases from a different hospital were included in the study as an independent validation cohort. A total of 3654 radiomic features were extracted from the preoperative multimodal MRI images (T1c, FLAIR, and ADC maps) and filtered in a data-driven approach. The discovery cohort was split into training and test sets by a 4:1 ratio. A diagnostic model (multilayer perceptron classifier) for detecting the IDHmut pTERTmut gliomas was trained using an automatic machine-learning algorithm named tree-based pipeline optimization tool (TPOT). The most critical radiomic features in the model were identified and visualized.ResultsThe model achieved an area under the receiver-operating curve (AUROC) of 0.971 (95% CI, 0.902-1.000), the sensitivity of 0.833 (95% CI, 0.333-1.000), and the specificity of 0.966 (95% CI, 0.931-1.000) in the test set. The area under the precision-recall curve (AUCPR) was 0.754 (95% CI, 0.572-0.833) and the F1 score was 0.833 (95% CI, 0.500-1.000). In the independent validation set, the model reached 0.952 AUROC, 0.714 sensitivity, 0.963 specificity, 0.841 AUCPR, and 0.769 F1 score. MR radiomic features of the IDHmut pTERTmut gliomas represented homogenous low-complexity texture in three modalities.ConclusionsAn accurate diagnostic model was constructed for detecting IDHmut pTERTmut gliomas using multimodal radiomic features. The most important features were associated with the homogenous simple texture of IDHmut pTERTmut gliomas in MRI images transformed using Laplacian of Gaussian and wavelet filters.

Dataset Information

Multi-modal magnetic resonance elastography for noninvasive assessment of ovarian tissue rigidity in vivo.

Publications

Multi-modal magnetic resonance elastography for noninvasive assessment of ovarian tissue rigidity in vivo.

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