Quantifying Bone Marrow Fat Fraction and Iron by MRI for Distinguishing Aplastic Anemia from Myelodysplastic Syndromes.

BACKGROUND: Bone marrow of patients with aplastic anemia (AA) is different from that of patients with myelodysplastic syndrome (MDS) and is difficult to identify by blood examination. IDEAL-IQ (iterative decomposition of water and fat with echo asymmetry and least-squares estimation) imaging might be able to quantify fat fraction (FF) and iron content in bone tissues. PURPOSE: To determine if IDEAL-IQ measurements of bone marrow FF and iron content can distinguish between patients with AA and MDS. STUDY TYPE: Retrospective. POPULATION: Fifty-seven patients with AA, 21 patients with MDS, and 24 healthy controls. FIELD STRENGTH/SEQUENCE: 3.0 T, IDEAL-IQ sequence. ASSESSMENT: Three independent observers evaluated the IDEAL-IQ images and measured FF and R2* in the left posterior superior iliac spine. STATISTICAL TESTS: Kruskal-Wallis test, linear correlations, and Bland-Altman analysis were used. A P-value of <0.05 was considered statistically significant. RESULTS: The FF in patients with AA (79.46% ± 15.00%) was significantly higher than that in patients with MDS (42.78% ± 30.09%) and control subjects (65.50% ± 14.73%). However, there was no significant difference in FF between control subjects and patients with MDS (P = 0.439). The R2* value of AA, MDS, and controls was 145.38 ± 53.33, (171.13 ± 100.89, and 135.99 ± 32.41/second, respectively, with no significant difference between the three groups (P = 0.553). DATA CONCLUSION: Quantitative IDEAL-IQ magnetic resonance imaging may facilitate the diagnosis of AA and distinguish it from MDS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Application evaluation of DCE-MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer.

The present study aimed to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with quantitative analysis of diffusion weighted imaging (DWI) for the diagnosis of prostate cancer (PCa). A total of 81 patients with prostatic diseases, including PCa (n=44) and benign prostatic hyperplasia (BPH, n=37), were imaged with T1 weighted imaging (T1WI), T2 weighted imaging (T2WI), DWI and DCE-MRI. The blood vessel permeability parameters volume transfer rate constant (Ktrans), back flow rate constant (Kep), extravascular extracellular space volume fraction (Ve), plasma volume fraction (Vp) and apparent diffusion coefficient (ADC) were measured, and compared between the two groups. The efficiency of these tools for the diagnosis of PCa was analyzed by receiver operating characteristic curve analysis. The efficiency of ADC combined with blood vessel permeability parameters in the diagnosis of PCa was analyzed by logistic regression. The correlation between these parameters and the Gleason score was evaluated by Spearman correlation analysis in the PCa group. The results demonstrated that, compared with the BPH group, Ktrans, Kep, Ve and Vp were higher, and ADC was lower in the PCa group (P<0.05). The combination of Kep and ADC offered the highest diagnosis efficiency [area under the curve (AUC=0.939)]. However, the combination of three parameters did not significantly improve the diagnostic efficiency. A subtle improvement in diagnostic efficiency was observed when four parameters (Ktrans + Kep + Ve + ADC) were combined (AUC=0.940), which was significantly higher than with one parameter. The ADC value of the PCa group was negatively correlated with the primary Gleason pattern, secondary Gleason pattern and the total Gleason score in PCa (r=-0.665, -0.456 and -0.714, respectively; P<0.001). The Vp in the PCa group was slightly negatively correlated with the primary Gleason pattern of PCa (r=-0.385; P<0.05); however, no significant correlation was found with secondary Gleason pattern and the total Gleason score. The present study revealed that the combination of DCE-MRI quantitative analysis and DWI was efficient for PCa diagnosis. This may be because DCE-MRI and DWI can noninvasively detect water motility in tumor tissues and alterations in permeability during tumor neovascularization. The present study demonstrated that Kep and ADC values may be used as predictive parameters for PCa diagnosis, which may help differentiate benign from malignant prostate lesions.