Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer's disease: A diagnostic study of DTI-derived data.

INTRODUCTION: For the past two decades, diffusion tensor imaging (DTI)-derived metrics allowed the characterization of Alzheimer's disease (AzD). Previous studies reported only a few parameters (most commonly fractional anisotropy, mean diffusivity, and axial and radial diffusivities measured at selected regions). We aimed to assess the diagnostic performance of 11 DTI-derived tensor metrics by using a global approach. MATERIALS AND METHODS: A prospective study performed in 34 subjects: 12 healthy elders, 11 mild cognitive impairment (MCI) patients, and 11 patients with AzD. Postprocessing of DTI magnetic resonance imaging allowed the calculation of 11 tensor metrics. Anisotropies included fractional (FA), and relative (RA). Diffusivities considered simple isotropic diffusion (p), simple anisotropic diffusion (q), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Tensors included the diffusion tensor total magnitude (L); and the linear (Cl), planar (Cp), and spherical tensors (Cs). We performed a multivariate discriminant analysis and diagnostic tests assessment. RESULTS: RD was the only variable selected to assemble a predictive model: Wilks' λ = 0.581, χ2 (2) = 14.673, P = 0.001. The model's overall accuracy was 64.5%, with areas under the curve of 0.81, 0.73 and 0.66 to diagnose AzD, MCI, and healthy brains, respectively. CONCLUSIONS: Global DTI-derived RD alone can discriminate between healthy elders, MCI, and AzD patients. Although this study proves evidence of a potential biomarker, it does not provide clinical guidance yet. Additional studies comparing DTI metrics might determine their usefulness to monitor disease progression, measure outcome in drug trials, and even perform the screening of pre-AzD subjects.

Global fractional anisotropy and mean diffusivity together with segmented brain volumes assemble a predictive discriminant model for young and elderly healthy brains: a pilot study at 3T.

Several parameters of brain integrity can be derived from diffusion tensor imaging. These include fractional anisotropy (FA) and mean diffusivity (MD). Combination of these variables using multivariate analysis might result in a predictive model able to detect the structural changes of human brain aging. Our aim was to discriminate between young and older healthy brains by combining structural and volumetric variables from brain MRI: FA, MD, and white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) volumes. This was a cross-sectional study in 21 young (mean age, 25.71±3.04 years; range, 21-34 years) and 10 elderly (mean age, 70.20±4.02 years; range, 66-80 years) healthy volunteers. Multivariate discriminant analysis, with age as the dependent variable and WM, GM and CSF volumes, global FA and MD, and gender as the independent variables, was used to assemble a predictive model. The resulting model was able to differentiate between young and older brains: Wilks' λ = 0.235, χ² (6) = 37.603, p = .000001. Only global FA, WM volume and CSF volume significantly discriminated between groups. The total accuracy was 93.5%; the sensitivity, specificity and positive and negative predictive values were 91.30%, 100%, 100% and 80%, respectively. Global FA, WM volume and CSF volume are parameters that, when combined, reliably discriminate between young and older brains. A decrease in FA is the strongest predictor of membership of the older brain group, followed by an increase in WM and CSF volumes. Brain assessment using a predictive model might allow the follow-up of selected cases that deviate from normal aging.

Partial correlation analyses of global diffusion tensor imaging-derived metrics in glioblastoma multiforme: Pilot study.

AIM: To determine existing correlates among diffusion tensor imaging (DTI)-derived metrics in healthy brains and brains with glioblastoma multiforme (GBM). METHODS: Case-control study using DTI data from brain magnetic resonance imaging of 34 controls (mean, 41.47; SD, ± 21.94 years; range, 21-80 years) and 27 patients with GBM (mean, SD; 48.41 ± 15.18 years; range, 18-78 years). Image postprocessing using FSL software calculated eleven tensor metrics: fractional (FA) and relative anisotropy; pure isotropic (p) and anisotropic diffusions (q), total magnitude of diffusion (L); linear (Cl), planar (Cp) and spherical tensors (Cs); mean (MD), axial (AD) and radial diffusivities (RD). Partial correlation analyses (controlling the effect of age and gender) and multivariate Mancova were performed. RESULTS: There was a normal distribution for all metrics. Comparing healthy brains vs brains with GBM, there were significant very strong bivariate correlations only depicted in GBM: [FA↔Cl (+)], [FA↔q (+)], [p↔AD (+)], [AD↔MD (+)], and [MD↔RD (+)]. Among 56 pairs of bivariate correlations, only seven were significantly different. The diagnosis variable depicted a main effect [F-value (11, 23) = 11.842, P ≤ 0.001], with partial eta squared = 0.850, meaning a large effect size; age showed a similar result. The age also had a significant influence as a covariate [F (11, 23) = 10.523, P < 0.001], with a large effect size (partial eta squared = 0.834). CONCLUSION: DTI-derived metrics depict significant differences between healthy brains and brains with GBM, with specific magnitudes and correlations. This study provides reference data and makes a contribution to decrease the underlying empiricism in the use of DTI parameters in brain imaging.

Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach.

BACKGROUND: Histological behavior of glioblastoma multiforme suggests it would benefit more from a global rather than regional evaluation. A global (whole-brain) calculation of diffusion tensor imaging (DTI) derived tensor metrics offers a valid method to detect the integrity of white matter structures without missing infiltrated brain areas not seen in conventional sequences. In this study we calculated a predictive model of brain infiltration in patients with glioblastoma using global tensor metrics. METHODS: Retrospective, case and control study; 11 global DTI-derived tensor metrics were calculated in 27 patients with glioblastoma multiforme and 34 controls: mean diffusivity, fractional anisotropy, pure isotropic diffusion, pure anisotropic diffusion, the total magnitude of the diffusion tensor, linear tensor, planar tensor, spherical tensor, relative anisotropy, axial diffusivity and radial diffusivity. The multivariate discriminant analysis of these variables (including age) with a diagnostic test evaluation was performed. RESULTS: The simultaneous analysis of 732 measures from 12 continuous variables in 61 subjects revealed one discriminant model that significantly differentiated normal brains and brains with glioblastoma: Wilks' λ = 0.324, χ(2) (3) = 38.907, p < .001. The overall predictive accuracy was 92.7%. CONCLUSIONS: We present a phase II study introducing a novel global approach using DTI-derived biomarkers of brain impairment. The final predictive model selected only three metrics: axial diffusivity, spherical tensor and linear tensor. These metrics might be clinically applied for diagnosis, follow-up, and the study of other neurological diseases.

Variability of apparent diffusion coefficients in metastatic small cell lung carcinoma: comparisons between-within normal tissue and liver metastases.

In recent years, the use of diffusion weighted MRI (DW-MRI) has increased for the diagnosis of focal liver lesions (FLLs). DW-MRI may help in the differentiation of benign and malignant FLLs by measuring the apparent diffusion coefficient (ADC) values. Unfortunately, liver metastases present different histopathologic features with variable MRI signals within each lesion; this histologic variability explains the intra- and inter-lesion variations of ADC measurements. We present the case of a 64-year-old female with diagnosis of liver metastasis from small cell lung carcinoma admitted to the emergency unit due to symptoms of inappropriate antidiuretic hormone secretion. Quantitative comparison of two liver MRI, on admission and 2-months after transcatheter arterial chemoembolization showed persistence of the hyperintense metastatic lesions with significant difference in the ADC values in the with-in metastatic lesions (p = 0.001) and between normal tissue and liver metastases only at the end of treatment (p < 0.001). Several publications state that DWMRI is capable to predict the response to chemotherapy in malignant tumors, the histologic variability of liver metastasis and their response to different treatments is reflected in intra- and inter-lesion variations of ADC measurements that might delay an accurate imaging diagnosis. We present evidence of this variability, which might encourage prospective clinical trials that would define better cut-off values, would help understand the ADC biological behaviour, and would reach consensus about the best acquisition parametersfor this promising quantitative biomarker.

Diagnostic performance of regional DTI-derived tensor metrics in glioblastoma multiforme: simultaneous evaluation of p, q, L, Cl, Cp, Cs, RA, RD, AD, mean diffusivity and fractional anisotropy.

OBJECTIVES: Almost a dozen diffusion tensor-imaging (DTI) variables have been used to evaluate brain tumours with scarce information about their diagnostic ability. We aimed to perform a comprehensive evaluation of tensor metrics reported in the last decade. METHODS: Retrospective case control study performed in 14 patients with glioblastoma multiforme (GBM) and 28 controls. Conventional brain MR sequences and image postprocessing of DTI allowed the calculation of: MD, FA, p, q, L, Cl, Cp, Cs, RA, RD and AD, classified into five regions: normal appearance white matter (NAWM), immediate and distant oedema, enhancing rim and cystic cavity. ANOVA and AUROC analyses were performed. RESULTS: ANOVA depicted a significant difference among all metrics (p < 0.05). RA had the highest performance in the NAWM and cystic cavity; immediate and distant zones of oedema were best diagnosed by RD and Cp respectively; q was the best biomarker of the enhancing rim zone; p < 0.001 for all metrics. CONCLUSIONS: FA and MD, accepted biomarkers of brain injury, were surpassed by other metrics. RA, together with Cs, Cl and CP, might be the new leaders in the evaluation of brain tumours. DTI tensor metrics depict different clinical applicability at each tumour region.