12 patients who had histological proven ganglioneuromas were investigated by computed tomography (CT) and magnetic resonance (MR) imaging. CT scans (n = 11), conventional spin-echo MR images (n = 10) and dynamic MR images (n = 5) were acquired. All lesions showed a well defined, oval shape. Five lesions (42%) showed calcification which was punctate in four and coarse in one on CT. CT attenuation was predominantly low in three of 10 (30%) and intermediate in the remaining seven (70%). In all lesions MR signals were mainly of low intensity on T1 weighted images (T1WI) and of high intensity on T2 weighted images (T2WI). Dynamic MR studies in five cases showed a lack of early enhancement but gradual increasing enhancement. One case had a ganglioneuroblastoma component which showed soft-tissue density and coarse calcifications on CT scans, MR images with intermediate intensity on T1WI and T2WI and early enhancement and little washout on dynamic MR images. In conclusion, ganglioneuroma typically shows punctate calcification and low attenuation on CT and marked hyperintensity on T2WI with gradual increasing enhancement on dynamic MR images. If a ganglioneuroma has atypical CT and MR features, coexistence of a malignant component should be considered.

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Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.