Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models.

PURPOSE: Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models' ability to differentiate between high-grade and low-grade gliomas. METHODS: This retrospective study included 22 patients with diffuse gliomas (age, 23-74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses. RESULTS: The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. - 0.18 ± 0.63, 0.68 ± 0.74 vs. - 0.08 ± 0.66, 0.63 ± 0.72 vs. - 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96). CONCLUSION: The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas.

A deep learning-based approach for the diagnosis of adrenal adenoma: a new trial using CT.

OBJECTIVE: To develop and validate deep convolutional neural network (DCNN) models for the diagnosis of adrenal adenoma (AA) using CT. METHODS: This retrospective study enrolled 112 patients who underwent abdominal CT (non-contrast, early, and delayed phases) with 107 adrenal lesions (83 AAs and 24 non-AAs) confirmed pathologically and with 8 lesions confirmed by follow-up as metastatic carcinomas. Three patients had adrenal lesions on both sides. We constructed six DCNN models from six types of input images for comparison: non-contrast images only (Model A), delayed phase images only (Model B), three phasic images merged into a 3-channel (Model C), relative washout rate (RWR) image maps only (Model D), non-contrast and RWR maps merged into a 2-channel (Model E), and delayed phase and RWR maps merged into a 2-channel (Model F). These input images were prepared manually with cropping and registration of CT images. Each DCNN model with six convolutional layers was trained with data augmentation and hyperparameter tuning. The optimal threshold values for binary classification were determined from the receiver-operating characteristic curve analyses. We adopted the nested cross-validation method, in which the outer fivefold cross-validation was used to assess the diagnostic performance of the models and the inner fivefold cross-validation was used to tune hyperparameters of the models. RESULTS: The areas under the curve with 95% confidence intervals of Models A-F were 0.94 [0.90, 0.98], 0.80 [0.69, 0.89], 0.97 [0.94, 1.00], 0.92 [0.85, 0.97], 0.99 [0.97, 1.00] and 0.94 [0.86, 0.99], respectively. Model E showed high area under the curve greater than 0.95. CONCLUSION: DCNN models may be a useful tool for the diagnosis of AA using CT. ADVANCES IN KNOWLEDGE: The current study demonstrates a deep learning-based approach could differentiate adrenal adenoma from non-adenoma using multiphasic CT.

Production of the X( 3872) in B-meson decay by the coalescence of charm mesons.

If the recently discovered charmonium state X( 3872) is a loosely bound S-wave molecule of the charm mesons D0 D(*0) or D(*0) D0, it can be produced in B-meson decay by the coalescence of charm mesons. If this coalescence mechanism dominates, the ratio of the differential rate for B+ -->D(0) D(* 0)K+ near the D0 D(*0) threshold and the rate for B+ -->XK+ is a function of the D0 D(*0) invariant mass and hadron masses only. The identification of the X( 3872) as a D0 D(*0)/D(*0)D0 molecule can be confirmed by observing an enhancement in the D0 D(*0) invariant mass distribution near the threshold. An estimate of the branching fraction for B+ -->XK+ is consistent with observations if X has quantum numbers J(PC)=1(++ ) and if J/psi pi(+) pi(-) is one of its major decay modes.