MRI Appearance of Focal Lesions in Liver Iron Overload

Liver iron overload is defined as an accumulation of the chemical element Fe in the hepatic parenchyma that exceeds the normal storage. When iron accumulates, it can be toxic for the liver by producing inflammation and cell damage. This can potentially lead to cirrhosis and hepatocellular carcinoma, as well as to other liver lesions depending on the underlying condition associated to liver iron overload. The correct assessment of liver iron storage is pivotal to drive the best treatment and prevent complication. Nowadays, magnetic resonance imaging (MRI) is the best non-invasive modality to detect and quantify liver iron overload. However, due to its superparamagnetic properties, iron provides a natural source of contrast enhancement that can make challenging the differential diagnosis between different focal liver lesions (FLLs). To date, a fully comprehensive description of MRI features of liver lesions commonly found in iron-overloaded liver is lacking in the literature. Through an extensive review of the published literature, we aim to summarize the MRI signal intensity and enhancement pattern of the most common FLLs that can occur in liver iron overload

State-of-the-art review on the correlations between pathological and magnetic resonance features of cirrhotic nodules

Hepatocellular carcinoma (HCC) has become the second greatest cause of cancer-related mortality worldwide and the newest advancements in liver imaging have improved the diagnosis of both overt malignancies and premalignant lesions, such as cirrhotic or dysplastic nodules, which is crucial to improve overall patient survival rate and to choose the best treatment options. The role of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) has grown in the last 20 years. In particular, the introduction of hepatospecific contrast agents has strongly increased the definition of precursor nodules and detection of highgrade dysplastic nodules and early HCCs. Nevertheless, the diagnosis of liver tumours in cirrhotic patients sometimes remains challenging for radiologists, thus, in doubtful cases, biopsy and histological analysis become critical in clinical practice. This current review briefly summarizes the history of imaging and histology for HCC, covering the newest techniques and their limits. Then, the article discusses the links between radiological and pathological characteristics of liver lesions in cirrhotic patients, by describing the multistep process of hepato carcinogenesis. Explaining the evolution of pathologic change from cirrhotic nodules to malignancy, the list of analyzed lesions provides regenerative nodules, lowgrade and high-grade dysplastic nodules, small HCC and progressed HCC, including common subtypes (steatohepatitic HCC, scirrhous HCC, macrotrabecular massive HCC) and more rare forms (clear cell HCC, chromophobe HCC, neutrophil-rich HCC, lymphocyterich HCC, fibrolamellar HCC). The last chapter covers the importance of the new integrated morphologicalmolecular classification and its association with radiological feature

An Apparent Diffusion Coefficient-based machine learning model can improve Prostate Cancer detection in the grey area of the PI-RADS 3 category: a single-centre experience

The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone non invasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis