Background:Rosai-Dorfman disease (RDD) is typically characterized by painless bilateral and symmetrical cervical lymphadenopathy, with associated fever and leukocytosis. The aim of the current study was to summarize the clinical features and imaging characteristics of RDD, in an effort to improve its diagnostic accuracy.Methods:The study was analyzed from 32 patients between January 2011 and December 2017; of these, 16 patients had pathologically diagnosed RDD, eight had pathologically diagnosed meningioma, and eight pathologically diagnosed lymphoma. All patients underwent computed tomography and magnetic resonance imaging (MRI). Clinical features and imaging characteristics of RDD were analyzed retrospectively. The mean apparent diffusion coefficient (ADC) values of lesions at different sites were measured, and one-way analysis of variance and the least significant difference t-test were used to compare the differences between groups and draw receiver operating characteristic curves. The tumors were excised for biopsy and analyzed using immunohistochemistry.Results:The mean ADCs were (0.81 ± 0.10) × 10−3 mm2/s for intercranial RDD, (0.73 ± 0.05) × 10−3 mm2/s for nasopharyngeal RDD, (0.74 ± 0.11) × 10−3 mm2/s for bone RDD, and (0.71 ± 0.04) × 10−3 mm2/s for soft-tissue RDD. The optimum ADC to distinguish intracranial RDD from lymphoma was 0.79 × 10−3 mm2/s (62.5% sensitivity and 100% specificity) and to distinguish meningioma from intracranial RDD was 0.92 × 10−3 mm2/s (62.5% sensitivity and 100% specificity). Levels of C-reactive protein, erythrocyte sediment rate and D-dimer were significantly elevated (81%, 87%, and 75%, respectively). On immunohistochemistry, RDD was positive for both S-100 and CD68 proteins but negative for CD1a.Conclusions:Conventional MRI, combined with diffusion-weighted imaging and ADC mapping, is an important diagnostic tool in evaluating RDD patients. An accurate diagnosis of RDD should consider the clinical features, imaging characteristics, and the pathological findings.

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ObjectivesWe aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).Materials and MethodsThis retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.ResultsThe multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967).ConclusionsRadiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance.ResultsThe classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical–radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819–0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707–0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively.ConclusionRadiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical–radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

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