Differentiation between fat-poor angiomyolipoma and clear cell renal cell carcinoma: qualitative and quantitative analysis using arterial spin labeling MR imaging

Can multiparametric MRI clear cell likelihood scores differentiate fat-Poor AML from CcRCC in subcentimeter lesions?

OBJECTIVE

To investigate the potential of multiparametric MRI clear cell likelihood scores (ccLS) for differentiating between fat-poor angiomyolipoma (AML) and clear cell renal cell carcinoma (ccRCC) in subcentimeter Lesions (1 cm or smaller).

MATERIALS AND METHODS

This retrospective study included consecutive patients with subcentimeter renal masses who underwent multiparametric MRI between September 2009 and September 2022 across three hospitals. Clinical and MRI findings were analyzed to differentiate between fat-poor AML and ccRCC. Lesions were categorized using the ccLS and receiver operating characteristic curve analysis was performed to assess ccLS performance.

RESULTS

Thirty-eight patients (mean age: 52 years ± 12; 19 women) with 39 lesions were included. Of the 39 lesions [mean size: 9.1 mm ± 1.0 (range, 6.0-10.0 mm)], 20 (51%) were ccRCC and 19 (49%) were fat-poor AML. Compared to the ccRCC, subcentimeter fat-poor AMLs were more likely to show hypointensity on T2WI (P < 0.001), homogeneous enhancement (P = 0.010), the presence of microscopic fat (P = 0.036), and the absence of a pseudocapsule (P = 0.020). The diagnostic percentage of fat-poor AML was 47% for a ccLS of 1 or 2, and ccRCC accounted for 75% in the ccLS 4 or 5 category. The AUC for discrimination was 0.846 (95% CI: 0.695-0.941, P < 0.001), with a sensitivity of 75.00% (95% CI: 50.9-91.3) and a specificity of 89.47% (95% CI: 66.9-98.7).

CONCLUSION

Multiparametric MRI clear cell likelihood scores can potentially be used to differentiate between fat-poor AML and ccRCC in lesions 1 cm or smaller.

MR Perfusion Imaging for Kidney Disease

Renal perfusion reflects overall function of a kidney. As an important indicator of kidney diseases, renal perfusion can be noninvasively measured by multiple methods of MR imaging, such as dynamic contrast-enhanced MR imaging, intravoxel incoherent motion analysis, and arterial spin labeling method. In this article we introduce the principle of the methods, review their recent technical improvements, and then focus on summarizing recent applications of the methods in assessing various renal diseases. By this review, we demonstrate the capability and clinical potential of the imaging methods, with the hope of accelerating their adoption to clinical practice.

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

ABSTRACT

To investigate the feasibility of arterial spin labeling (ASL) blood flow (BF) and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH).Sixty-three stage T1 NPC patients and benign NPLH patients underwent ASL on a 3.0-T magnetic resonance imaging system. BF histogram parameters were derived automatically, including the mean, median, maximum, minimum, kurtosis, skewness, and variance. Absolute values were obtained for skewness and kurtosis (absolute value of skewness [AVS] and absolute value of kurtosis [AVK], respectively). The Mann-Whitney U test, receiver operating characteristic curve, and multiple logistic regression models were used for statistical analysis.The mean, maximum, and variance of ASL BF values were significantly higher in early-stage NPC than in NPLH (all P < 0.0001), while the median and AVK values of early-stage NPC were also significantly higher than those of NPLH (all P < 0.001). No significant difference was found between the minimum and AVS values in early-stage NPC compared with NPLH (P = 0.125 and P = 0.084, respectively). The area under the curve (AUC) of the maximum was significantly higher than those of the mean and median (P < 0.05). The AUC of variance was significantly higher than those of the other parameters (all P < 0.05). Multivariate analysis showed that variance was the only independent predictor of outcome (P < 0.05).ASL BF and its histogram analysis could distinguish early-stage NPC from NPLH, and the variance value was a unique independent predictor.