Peri-tumoral hyperintensity on hepatobiliary phase of gadoxetic acid-enhanced MRI in hepatocellular carcinomas: correlation with peri-tumoral hyperplasia and its pathological features

Gd-EOB-DTPA-enhanced MR imaging features of hepatocellular carcinoma in non-cirrhotic liver

OBJECTIVE

To evaluate clinical, pathological and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) findings of hepatocellular carcinoma (HCC) in non-cirrhotic livers and compare with HCC in cirrhotic livers.

METHODS

This retrospective study included patients with pathologically confirmed HCC who underwent preoperative Gd-EOB-DTPA-enhanced MRI between January 2015 and October 2021. Propensity scores were utilized to match non-cirrhotic HCCs (NCHCCs) patients with cirrhotic HCCs (CHCCs) patients. The clinical, pathological and MR imaging features of NCHCCs were compared with CHCCs. Correlation between these features and the presence of NCHCCs were analyzed by logistic regression analysis. The predictive efficacy was evaluated using receiver operating characteristic (ROC) analysis. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs.

RESULTS

After propensity score matching (1:3), a total of 144 patients with HCCs (36 NCHCCs and 108 CHCCs) were included. NCHCCs were larger in tumor size than CHCCs (P < 0.001, Cohen's d = 0.737). NCHCCs were more common in patients who have hepatitis C (5.6 % vs 1.9 %, P > 0.05) or have no known liver disease (11.1 % vs 0.9 %, P = 0.004), while hepatitis B was more common in CHCC patients (83.3 % vs 97.2 %, P = 0.003). Compared with CHCCs, NCHCCs more frequently demonstrated non-smooth tumor margin (P = 0.001, Cramer's V = 0.273), peri-tumoral hyperintensity (P < 0.05, Cramer's V = 0.185), hyperintense and heterogeneous signals in hepatobiliary phase (HBP) (P < 0.05). CHCCs were more likely to have satellite nodules compared to NCHCCs (33.3 % vs 57.4 %, P < 0.05, Cramer's V = 0.209). Based on the univariate and multivariate logistic regression analysis, the tumor size, non-smooth tumor margin, heterogeneous intensity in HBP and satellite nodule were significantly correlated to NCHCCs (P all <0.05). ROC curve analysis demonstrated that tumor size and non-smooth tumor margin were potential imaging predictors for the diagnosis of NCHCC, with AUC values of 0.715 and 0.639, respectively. The combination of the two imaging features for identifying NCHCC achieved an AUC value of 0.761, with a sensitivity of 0.889 and a specificity of 0.630.

CONCLUSION

NCHCCs were more likely to show larger tumor size, non-smooth tumor margin, peri-tumoral hyperintensity, as well as hyperintense and heterogeneous signals in HBP at Gd-EOB-DTPA-enhanced MR imaging compared with NCHCCs. Tumor size and non-smooth tumor margin in HBP may help to discriminate NCHCCs.

Glutamine metabolic reprogramming in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a lethal disease with limited management strategies and poor prognosis. Metabolism alternations have been frequently unveiled in HCC, including glutamine metabolic reprogramming. The components of glutamine metabolism, such as glutamine synthetase, glutamate dehydrogenase, glutaminase, metabolites, and metabolite transporters, are validated to be potential biomarkers of HCC. Increased glutamine consumption is confirmed in HCC, which fuels proliferation by elevated glutamate dehydrogenase or upstream signals. Glutamine metabolism also serves as a nitrogen source for amino acid or nucleotide anabolism. In addition, more glutamine converts to glutathione as an antioxidant in HCC to protect HCC cells from oxidative stress. Moreover, glutamine metabolic reprogramming activates the mTORC signaling pathway to support tumor cell proliferation. Glutamine metabolism targeting therapy includes glutamine deprivation, related enzyme inhibitors, and transporters inhibitors. Together, glutamine metabolic reprogramming plays a pivotal role in HCC identification, proliferation, and progression.

Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data

PURPOSE

To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods.

METHODS

Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2). We extracted and selected clinical and radiomic features on baseline T2WI. Then we built LR models and DL models. Receiver operating characteristic (ROC) curves analysis was performed to assess predictive performance of models.

RESULTS

Eighty-nine patients were assigned to the training cohort, and 29 patients were assigned to the testing cohort. The area under receiver operating characteristics curve (AUC) of LR models, which were predictive of high sensitivity and pCR, were 0.853 and 0.866, respectively. Whereas the AUCs of DL models were 0.829 and 0.838, respectively. After 10 rounds of cross validation, the accuracy of the models in Group 1 is higher than in Group 2.

CONCLUSION

There was no significant difference between LR model and DL model. Artificial Intelligence-based radiomics biomarkers may have potential clinical implications for adaptive and personalized therapy.

Magnetic resonance imaging for characterization of hepatocellular carcinoma metabolism

With a better understanding of the pathophysiological and metabolic changes in hepatocellular carcinoma (HCC), multiparametric and novel functional magnetic resonance (MR) and positron emission tomography (PET) techniques have received wide interest and are increasingly being applied in preclinical and clinical research. These techniques not only allow for non-invasive detection of structural, functional, and metabolic changes in malignant tumor cells but also characterize the tumor microenvironment (TME) and the interactions of malignant tumor cells with the TME, which has hypoxia and low pH, resulting from the Warburg effect and accumulation of metabolites produced by tumor cells and other cellular components. The heterogeneity and complexity of the TME require a combination of images with various parameters and modalities to characterize tumors and guide therapy. This review focuses on the value of multiparametric magnetic resonance imaging and PET/MR in evaluating the structural and functional changes of HCC and in detecting metabolites formed owing to HCC and the TME.

MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

PURPOSE

Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value.

METHODS

Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features.

RESULTS

Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (>5 cm) (DOR = 5.2, 95% CI [3.0-9.0]), rim arterial enhancement (4.2, 95% CI [1.7-10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8-6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4-15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2-4.4]), multifocality (7.1, 95% CI [2.6-19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5-9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4-94.8%] and 93.3% [74.5-98.5%], respectively).

CONCLUSIONS

Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.

Spectrum of liver lesions hyperintense on hepatobiliary phase: an approach by clinical setting

Hepatobiliary MRI contrast agents are increasingly being used for liver imaging. In clinical practice, most focal liver lesions do not uptake hepatobiliary contrast agents. Less commonly, hepatic lesions may show variable signal characteristics on hepatobiliary phase. This pictorial essay reviews a broad spectrum of benign and malignant focal hepatic observations that may show hyperintensity on hepatobiliary phase in various clinical settings. In non-cirrhotic patients, focal hepatic observations that show hyperintensity in the hepatobiliary phase are usually benign and typically include focal nodular hyperplasia. In patients with primary or secondary vascular disorders, focal nodular hyperplasia-like lesions arise as a local hyperplastic response to vascular alterations and tend to be iso- or hyperintense in the hepatobiliary phase. In oncologic patients, metastases and cholangiocarcinoma are hypointense lesions in the hepatobiliary phase; however, occasionally they may show a diffuse, central and inhomogeneous hepatobiliary paradoxical uptake with peripheral rim hypointensity. Post-chemotherapy focal nodular hyperplasia-like lesions may be tricky, and their typical hyperintense rim in the hepatobiliary phase is very helpful for the differential diagnosis with metastases. In cirrhotic patients, hepatocellular carcinoma may occasionally appear hyperintense on hepatobiliary phase.

Patterns of enhancement in the hepatobiliary phase of gadoxetic acid-enhanced MRI

A variety of patterns of enhancement of liver lesions and liver parenchyma is observed in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI. It is becoming increasingly apparent that many lesions may exhibit HBP enhancement. Much of the literature regarding the role of gadoxetic acid-enhanced MRI in characterising liver lesions is dichotomous, focusing on whether lesions are enhancing or non-enhancing in the HBP, rather than examining the patterns of enhancement. We provide a pattern-based description of HBP enhancement of liver parenchyma and of liver lesions. The role of OATP1B3 transporters, hepatocyte function and lesion composition in influencing patterns of HBP hyperintensity are discussed.

Common pitfalls when using the Liver Imaging Reporting and Data System (LI-RADS): lessons learned from a multi-year experience

The goal of the Liver Imaging Reporting and Data System (LI-RADS) is to standardize the interpretation and reporting of liver observations on contrast-enhanced CT and MR imaging of patients at risk for hepatocellular carcinoma. Although LI-RADS represents a significant achievement in standardization of the diagnosis and management of cirrhotic patients, complexity and caveats to the algorithm may challenge correct application in clinical practice. The purpose of this paper is to discuss common pitfalls and potential solutions when applying LI-RADS in practice. Knowledge of the most common pitfalls may improve the diagnostic confidence and performance when using the LI-RADS system for the interpretation of CT and MR imaging of the liver.