sion, peritumoral enhancement, and other imaging features have been reported as predictors in HCC for MVI or posthepatectomy recurrence HCC [5][6][7]. However, independent validation of these features has not yet been performed, and these features are not yet applied widely.The Liver Imaging Reporting and Data System (LI-RADS) [8] was developed to standardized terminology, interpretation, and reporting of imaging for HCC diagnosis. The system addresses the full spectrum of liver lesions and pseudolesions with a 5-point scale reflecting the relative likelihood of HCC, from LR-1 (definitely benign) to LR-5 ( definitely HCC). LI-RADS also assigns category LR-M to observations considered probably or definitely malignant but lacking criteria specific for HCC and a separate category (LR-TIV

Liver fibrosis, as one of the leading causes of liver‐related morbidity and mortality, has no Food and Drug Administration (FDA)‐approved antifibrotic therapy yet. Although microRNA‐29b (miRNA‐29b) and microRNA‐122 (miRNA‐122) have great potential in treating liver fibrosis via regulating profibrotic genes in hepatic stellate cells (HSCs), it is still a challenge to achieve a HSC‐targeted and meanwhile noninvasively trackable delivery of miRNAs in vivo. Herein, a pH‐sensitive and vitamin A (VA)‐conjugated copolymer VA–polyethylene glycol–polyethyleneimine–poly( N ‐( N ′, N ′‐diisopropylaminoethyl)‐ co ‐benzylamino) aspartamide (T‐PBP) is synthesized and assembled into superparamagnetic iron oxide (SPIO)‐decorated cationic micelle for miRNA delivery. The T‐PBP micelle efficiently transports the miRNA‐29b and miRNA‐122 to HSC in a magnetic resonance imaging‐visible manner, resulting in a synergistic antifibrosis effect via downregulating the expression of fibrosis‐related genes, including collagen type I alpha 1, α‐smooth muscle actin, and tissue inhibitor of metalloproteinase 1. Consequently, the HSC‐targeted combination therapy with miRNA‐29b and miRNA‐122 demonstrates a prominent antifibrotic efficacy in terms of improving liver function and relieving hepatic fibrosis.

4 Technical Efficacy: Stage 5 J. MAGN. RESON. IMAGING 2018.

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Objectives: Liver resection is potentially curative for early-stage hepatocellular carcinoma (eHCC) in patients with well-preserved liver function. The prognosis of these patients after resection is still unsatisfactory because of frequent early recurrence (ER). Therefore, we investigated the role of preoperative dynamic contrast-enhanced 3.0-T MR imaging in predicting ER of eHCC after curative resection.Methods From May 2014 to October 2017, we retrospectively analyzed 82 patients with eHCC who underwent dynamic MR imaging and subsequently underwent curative resection. Liver Imaging Reporting and Data System (LI-RADS) v2018 major and ancillary imaging features, as well as two non-LI-RADS MR imaging features (irregular tumor margin and tumor number), were evaluated. A multivariate Cox regression analysis was used to identify independent predictors, and two models (preoperative and postoperative prediction models) were developed.Results ER was observed in 25 patients (25/82, 30.5%). In the univariate analyses, preoperative alpha-fetoprotein (AFP) level >200 ng/ml, three MR imaging features (multifocal tumors, corona enhancement, and irregular tumor margin), and microvascular invasion (MVI) were associated with ER. In the multivariate analysis, corona enhancement (hazard ratio [HR]: 2.970; p = 0.013) and irregular tumor margin (HR: 2.377; p = 0.048) were independent predictors in the preoperative prediction model, and preoperative AFP level >200 ng/ml (HR: 2.493; p = 0.044) plus corona enhancement (HR: 3.046; p = 0.014) were independent predictors in the postoperative prediction model (microvascular invasion [MVI] was not; p = 0.061). When combined with both predictors, the specificity for ER in the preoperative prediction model was 98.2% (56/57), which was comparable to that of the postoperative prediction model [96.7% (55/57)].Conclusions Our results demonstrated that preoperative MR imaging features (corona enhancement and irregular tumor margin) have the potential to preoperatively identify high-risk ER patients with eHCC, with a specificity >90%.

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Worldwide, cerebral cryptococcal infections caused by cryptococcus neoformans are mostly found in immunocompromised patients, but less found in immunocompetent patients with fewer related imaging reports in literatures. This pictorial essay describes some important MR imaging features in arriving at diagnosis for cerebral cryptococcosis in immunocompetent patients by way of five illustrative cases with intact MRI data.

BACKGROUND The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks. AIM To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT. METHODS A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN. RESULTS A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. CONCLUSION MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.