Most CT/MRI and CEUS LI-RADS major features had independent associations with hepatocellular carcinoma; arterial phase hyperenhancement and washout had the strongest associations whereas, for CT/MR... Background The Liver Imaging Reporting and Data System (LI-RADS) assigns a risk category for hepatocellular carcinoma (HCC) to imaging observations. Establishing the contributions of major features...

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.

Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic performance of dual-phase contrast-enhanced multiparametric DECT for predicting MTM HCC. Materials and Methods Patients with histopathologic examination-confirmed HCC who underwent contrast-enhanced DECT between June 2019 and June 2022 were retrospectively recruited from three independent centers (center 1, training and internal test data set; centers 2 and 3, external test data set). Radiologic features were visually analyzed and combined with clinical information to establish a clinical-radiologic model. Deep learning (DL) radiomics models were based on DL features and handcrafted features extracted from virtual monoenergetic images and material composition images on dual phase using binary least absolute shrinkage and selection operators. A DL radiomics nomogram was developed using multivariable logistic regression analysis. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), and the log-rank test was used to analyze recurrence-free survival. Results A total of 262 patients were included (mean age, 54 years ± 12 [SD]; 225 men [86%]; training data set, n = 146 [56%]; internal test data set, n = 35 [13%]; external test data set, n = 81 [31%]). The DL radiomics nomogram better predicted MTM than the clinical-radiologic model (AUC = 0.91 vs 0.77, respectively, for the training set [P < .001], 0.87 vs 0.72 for the internal test data set [P = .04], and 0.89 vs 0.79 for the external test data set [P = .02]), with similar sensitivity (80% vs 87%, respectively; P = .63) and higher specificity (90% vs 63%; P < .001) in the external test data set. The predicted positive MTM groups based on the DL radiomics nomogram had shorter recurrence-free survival than predicted negative MTM groups in all three data sets (training data set, P = .04; internal test data set, P = .01; and external test data set, P = .03). Conclusion A DL radiomics nomogram derived from multiparametric DECT accurately predicted the MTM subtype in patients with HCC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Hepatocellular carcinoma displays variable biologic aggressiveness, and emerging data suggest that imaging features may allow prediction of pathologic and molecular alterations and provide insights... Hepatocellular carcinoma (HCC) is a malignancy with variable biologic aggressiveness based on the tumor grade, presence or absence of vascular invasion, and pathologic and molecular classification....