Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy.

This paper offers a brief overview of common non-invasive techniques for body composition assessment methods, and of the way images extracted by these methods can be processed with artificial intelligence (AI) and radiomic analysis. These new techniques are becoming more and more appealing in the field of health care, thanks to their ability to treat and process a huge amount of data, suggest new correlations between extracted imaging biomarkers and traits of several diseases as well as lead to the possibility to realise an increasingly personalized medicine. The idea is to suggest the use of AI applications and radiomic analysis to search for features that may be extracted from medical images [computed tomography (CT) and magnetic resonance imaging (MRI)], and that may turn out to be good predictors of metabolic disorder diseases and cancer. This could lead to patient-specific treatments and management of several diseases linked with excessive body fat.

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This work describes the design of a quadrature surface coil constituted by a circular loop and a butterfly coil, employed in transmit/receive (TX/RX) mode for hyperpolarized 13C studies of pig heart with a clinical 3T scanner. The coil characterization is performed by developing an SNR model for coil performance evaluation in terms of coil resistance, sample‐induced resistance and magnetic field pattern. Experimental SNR‐vs.‐depth profiles, extracted from the [1–13C]acetate phantom chemical shift image (CSI), showed good agreement with the theoretical SNR‐vs.‐depth profiles. Moreover, the performance of the quadrature coil was compared with the single TX/RX circular and TX/RX butterfly coil, in order to verify the advantage of the proposed configuration over the single coils throughout the volume of interest for cardiac imaging in pig. Finally, the quadrature surface coil was tested by acquiring metabolic maps with hyperpolarized [1–13C]pyruvate injected i.v. in a pig. © 2013 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 43B: 69–77, 2013