Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.

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Background: The purpose of the study was to determine whether contrast-enhanced CT texture features relate to, and can predict, the presence of specific genetic mutations involved in CRC carcinogenesis. Materials and methods: This retrospective study analyzed the pre-operative CT in the venous phase of patients with CRC, who underwent testing for mutations in the KRAS, NRAS, BRAF, and MSI genes. Using a specific software based on CT images of each patient, for each slice including the tumor a region of interest was manually drawn along the margin, obtaining the volume of interest. A total of 56 texture parameters were extracted that were compared between the wild-type gene group and the mutated gene group. A p-value of <0.05 was considered statistically significant. Results: The study included 47 patients with stage III-IV CRC. Statistically significant differences between the MSS group and the MSI group were found in four parameters: GLRLM RLNU (area under the curve (AUC) 0.72, sensitivity (SE) 77.8%, specificity (SP) 65.8%), GLZLM SZHGE (AUC 0.79, SE 88.9%, SP 65.8%), GLZLM GLNU (AUC 0.74, SE 88.9%, SP 60.5%), and GLZLM ZLNU (AUC 0.77, SE 88.9%, SP 65.8%). Conclusions: The findings support the potential role of the CT texture analysis in detecting MSI in CRC based on pre-treatment CT scans.

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Fontan-associated liver disease (FALD) is an arising clinical entity that can occur long after a successful Fontan operation for correction of single ventricle (SV) congenital heart disease (CHD). Occurrence of FALD is characterized by liver cirrhosis and other hepatic complications, and determinates an increased morbidity and mortality. Currently, there is no consensus on how to stage FALD. We report here our experience by an observational study in 52 patients with SV-CHD after Fontan operation that were recruited through a period of 36 ± 9.3 months. All cases underwent lab tests and liver and cardiac imaging evaluation, including liver stiffness (LS) measurement by transient elastography (TE) (FibroScan®). According to selective criteria for liver disease, we identified 23/43 (53.5%) cases with advanced FALD that showed: older age (p < 0.05), larger hepatic and cava veins diameter (p < 0.05), worsened NYHA class (p < 0.05), abnormal lymphocytes (p < 0.01), platelet count (p < 0.05), and GGT, prothrombin time (INR), albumin and cystatin C levels (p < 0.05), with respect to cases without advanced FALD. LS values were significantly increased in cases with advanced FALD, at cut-off values higher than 22 kPa (p < 0.001). LS, and its combined score with spleen diameter and platelet count (LSPS) successfully helped to detect 100% of cases with portal hypertension (p < 0.001). In conclusion, LS can be effective to stage FALD and to uncover cases with severe risk of complications, avoiding higher morbidity and mortality related to advanced FALD.