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Background The new designation of arrhythmogenic cardiomyopathy defines a broader spectrum of disease phenotypes, which include right dominant, biventricular, and left dominant variants. We evaluated the relationship between electrocardiographic findings and contrast‐enhanced cardiac magnetic resonance phenotypes in arrhythmogenic cardiomyopathy. Methods and Results We studied a consecutive cohort of patients with a definite diagnosis of arrhythmogenic cardiomyopathy, according to 2010 International Task Force criteria, who underwent electrocardiography and contrast‐enhanced cardiac magnetic resonance. Both depolarization and repolarization electrocardiographic abnormalities were correlated with the severity of dilatation/dysfunction, either global or regional, of both ventricles and the presence and regional distribution of late gadolinium enhancement. The study population included 79 patients (60% men). There was a statistically significant relationship between the presence and extent of T‐wave inversion across a 12‐lead ECG and increasing values of median right ventricular ( RV ) end‐diastolic volume ( P <0.001) and decreasing values of RV ejection fraction ( P <0.001). The extent of T‐wave inversion to lateral leads predicted a more severe RV dilatation rather than a left ventricular involvement because of the leftward displacement of the dilated RV , as evidenced by contrast‐enhanced cardiac magnetic resonance. A terminal activation delay of >55 ms in the right precordial leads (V1‐V3) was associated with higher RV volume ( P =0.014) and lower RV ejection fraction ( P =0.053). Low QRS voltages in limb leads predicted the presence ( P =0.004) and amount ( P <0.001) of left ventricular late gadolinium enhancement. Conclusions The study results indicated that electrocardiographic abnormalities predict the arrhythmogenic cardiomyopathy phenotype in terms of severity of RV disease and left ventricular involvement, which are among the most important determinants of the disease outcome.

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Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P &lt; 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.