Contrast Enhanced MRI in the Diagnosis of HCC. Diagnostics (Basel). 2015;5:383-398. [PMID: 26854161 PMCID: PMC4665604 DOI: 10.3390/diagnostics5030383]

Chip collection of hepatocellular carcinoma based on O2 heterogeneity from patient tissue

Hepatocellular carcinoma frequently recurs after surgery, necessitating personalized clinical approaches based on tumor avatar models. However, location-dependent oxygen concentrations resulting from the dual hepatic vascular supply drive the inherent heterogeneity of the tumor microenvironment, which presents challenges in developing an avatar model. In this study, tissue samples from 12 patients with hepatocellular carcinoma are cultured directly on a chip and separated based on preference of oxygen concentration. Establishing a dual gradient system with drug perfusion perpendicular to the oxygen gradient enables the simultaneous separation of cells and evaluation of drug responsiveness. The results are further cross-validated by implanting the chips into mice at various oxygen levels using a patient-derived xenograft model. Hepatocellular carcinoma cells exposed to hypoxia exhibit invasive and recurrent characteristics that mirror clinical outcomes. This chip provides valuable insights into treatment prognosis by identifying the dominant hepatocellular carcinoma type in each patient, potentially guiding personalized therapeutic interventions.

FI-CEUS: a solution to improve the diagnostic accuracy in MRI LI-RADS-indeterminate (LR-3/4) FLLs at risk for HCC

Objective

To evaluate the diagnostic accuracy of fusion imaging contrast-enhanced ultrasound (FI-CEUS) of magnetic resonance imaging (MRI) LI-RADS-indeterminate (LR-3/4) and conventional ultrasound undetected focal liver lesions (FLLs) in patients at risk for hepatocellular carcinoma (HCC).

Methods

Between February 2020 and July 2021, 71 FLLs in 63 patients were registered for diagnostic performance evaluation respectively for ultrasound-guided thermal ablation evaluation in this retrospective study. Diagnostic performance regarding FLLs was compared between FI-CEUS and contrast-enhanced MRI (CE-MRI).

Results

For diagnostic performance evaluation, among 71 lesions in 63 patients, the diagnostic efficacy of FI-CEUS with LI-RADS was significantly higher than that of CE-MRI (P < 0.05) in both overall and hierarchical comparison (except for the group with lesion diameter ≥2 cm). For malignant lesions, the proportion of arterial phase hyperenhancement (APHE) and washout on FI-CEUS was higher than that on CE-MRI (P < 0.05).

Conclusion

FI-CEUS has a high value in the precise qualitative diagnosis of small FLLs (<2 cm) of MRI LI-RADS-indeterminate diagnosis (LR-3/4) that are undetected by conventional ultrasound in patients at risk for HCC and can be a good supplementary CE-MRI diagnostic method for thermal ablation evaluation.

Molecular imaging of tumour-associated pathological biomarkers with smart nanoprobe: From "Seeing" to "Measuring"

Although the extraordinary progress has been made in molecular biology, the prevention of cancer remains arduous. Most solid tumours exhibit both spatial and temporal heterogeneity, which is difficult to be mimicked in vitro. Additionally, the complex biochemical and immune features of tumour microenvironment significantly affect the tumour development. Molecular imaging aims at the exploitation of tumour-associated molecules as specific targets of customized molecular probe, thereby generating image contrast of tumour markers, and offering opportunities to non-invasively evaluate the pathological characteristics of tumours in vivo. Particularly, there are no "standard markers" as control in clinical imaging diagnosis of individuals, so the tumour pathological characteristics-responsive nanoprobe-based quantitative molecular imaging, which is able to visualize and determine the accurate content values of heterogeneous distribution of pathological molecules in solid tumours, can provide criteria for cancer diagnosis. In this context, a variety of "smart" quantitative molecular imaging nanoprobes have been designed, in order to provide feasible approaches to quantitatively visualize the tumour-associated pathological molecules in vivo. This review summarizes the recent achievements in the designs of these nanoprobes, and highlights the state-of-the-art technologies in quantitative imaging of tumour-associated pathological molecules.

IOP Injection, A Novel Superparamagnetic Iron Oxide Particle MRI Contrast Agent for the Detection of Hepatocellular Carcinoma: A Phase II Clinical Trial

BACKGROUND

MRI is crucial in diagnosing hepatocellular carcinoma (HCC). Superparamagnetic iron oxide particles (SPIO) are liver-specific contrast agents which enhance lesions in T2 -weighted images. Iron oxide nano-particle m-PEG-silane (IOP) Injection, a newly developed SPIO, showed promising imaging effects and good safety profile in preclinical studies and in phase I clinical trial.

PURPOSE

To evaluate the safety and clinical validity of IOP Injection as MRI contrast agent in diagnosing HCC.

STUDY TYPE

Prospective.

SUBJECTS

A total of 52 subjects (61.6 ± 11.05 years, 45 males/7 females) with suspected HCC.

FIELD STRENGTH/SEQUENCE

1.5 T, T1 -weighted in/opposed phase, T2 *-weighted gradient echo, T2 -weighted fast spin echo, true fast imaging with steady-state free precession.

ASSESSMENT

Adverse effects and clinical monitoring were recorded throughout the 5-day study. Two independent readers (M.G.H. with 30 years of experience, S.P.K. with 26 years of experience) made the diagnosis. The diagnostic performance of IOP-enhanced MRI was evaluated with sensitivity and positive predictive value by comparing to the pathology reports from subsequent hepatic resection. The number of lesions with various sizes and degrees of differentiation detected by IOP-enhanced MRI was assessed. The relative change in signal intensities over time was indirectly measured from acquired images.

STATISTICAL TESTS

Sensitivity and positive predictive value were used to evaluate the diagnostic performance of IOP-enhanced MRI. Prevalence-adjusted and bias-adjusted 𝜅 coefficient was used to assess the interreader variability.

RESULTS

No serious adverse event related to IOP Injection was found. IOP Injection enhanced the lesion-to-liver contrast ratio in T2 *-weighted images by 50.1% ± 4.8%. IOP-enhanced MRI detected HCC with 100% sensitivity by subject and 96% sensitivity by lesion. IOP Injection visualized subtle vascular invasion as filling defect within vessels in true fast imaging with steady-state free precession (TrueFISP) images.

DATA CONCLUSION

IOP Injection was safe and efficacious as MRI contrast agent in diagnosing HCC in a limited group of subjects.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging

Introduction

The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature.

Methods

This study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary.

Results and discussion

The experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

Hepatocellular carcinoma: State of the art diagnostic imaging

Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.

MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.

An immune subtype-related prognostic signature of hepatocellular carcinoma based on single-cell sequencing analysis

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and is often associated with a poor prognosis. The main reason for this poor prognosis is that inconspicuous early symptoms lead to delayed diagnosis. Treatment options for advanced HCC remain limited and ineffective. In this context, the exploration of the immune microenvironment in HCC becomes attractive. In this study, we divided HCC into immune cell and non-immune cell subtypes, by single-cell sequencing analysis of GEO dataset GSE146115. We found differentially expressed genes in the two subtypes, which we used to construct a prognostic model for HCC through Cox and Lasso regressions. Our prognostic model can accurately evaluate the prognosis of HCC patients, and provide a reference for the design of immunotherapy for HCC.

Comparative the clinical value of contrast-enhanced ultrasonography, enhancement CT and MRI for diagnosing of liver lesions

BACKGROUND

To investigate the accuracy of contrast-enhanced ultrasonography, CT-enhancement and MRI in the diagnosis of liver-occupying lesions.

METHODS

176 patients with suspected liver lesions in our hospital were retrospectively studied from July 2014 to July 2016. All of the 176 patients were diagnosed by contrast-enhanced ultrasonography, enhanced CT and MRI, and the pathological examination was performed. The results of pathological examination were regarded as the results of the diagnosis. The diagnostic accuracywas then compared among contrast-enhanced ultrasound, enhanced CT and MRI of these patients.

RESULTS

The results of contrast-enhanced ultrasonography showed that 164 of the 176 patients had liver-occupying lesions, and the accuracy of the diagnosis was 95.35%, which was significantly higher than that of CT enhancement and MRI (80.23% 84.30%). The accuracy of contrast-enhanced ultrasonography, in the diagnosis of primary liver cancer was significantly higher than that of CT enhancement and MRI (P <  0.05), and the difference was significant difference (P <  0.05).

CONCLUSIONS

The examination of contrast-enhanced ultrasonography is relatively simple, and the patients can get duplicateexamination, so we should choose the contrast-enhanced ultrasonography as the preferred method of diagnosis in liver mass, especially primary liver cancer.

Imaging Features at the Periphery: Hemodynamics, Pathophysiology, and Effect on LI-RADS Categorization

Liver lesions have different enhancement patterns at dynamic contrast-enhanced imaging. The Liver Imaging Reporting and Data System (LI-RADS) applies the enhancement kinetic of liver observations in its algorithms for imaging-based diagnosis of hepatocellular carcinoma (HCC) in at-risk populations. Therefore, careful analysis of the spatial and temporal features of these enhancement patterns is necessary to increase the accuracy of liver mass characterization. The authors focus on enhancement patterns that are found at or around the margins of liver observations-many of which are recognized and defined by LI-RADS, such as targetoid appearance, rim arterial phase hyperenhancement, peripheral washout, peripheral discontinuous nodular enhancement, enhancing capsule appearance, nonenhancing capsule appearance, corona enhancement, and periobservational arterioportal shunts-as well as peripheral and periobservational enhancement in the setting of posttreatment changes. Many of these are considered major or ancillary features of HCC, ancillary features of malignancy in general, features of non-HCC malignancy, features associated with benign entities, or features related to treatment response. Distinction between these different patterns of enhancement can help with achieving a more specific diagnosis of HCC and better assessment of response to local-regional therapy. ^©RSNA, 2021.

Construction of a Novel Gene-Based Model for Survival Prediction of Hepatitis B Virus Carriers With HCC Development

Despite the effectiveness of hepatitis B virus (HBV) vaccination in reducing the prevalence of chronic HBV infection as well as the incidence of acute hepatitis B, fulminant hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC), there was still a large crowd of chronically infected populations at risk of developing cirrhosis or HCC. In this study, we established a comprehensive prognostic system covering multiple signatures to elevate the predictive accuracy for overall survival (OS) of hepatitis B virus carriers with HCC development. Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and multivariate COX analysis, along with a suite of other online analyses were successfully applied to filtrate a three-gene signature model (TP53, CFL1, and UBA1). Afterward, the gene-based risk score was calculated based on the Cox coefficient of the individual gene, and the prognostic power was assessed by time-dependent receiver operating characteristic (tROC) and Kaplan–Meier (KM) survival analysis. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and TNM stage), was revealed by the calibration plot and tROC curves, which was verified in the validation set. Taken together, our study may be more effective in guiding the clinical decision-making of personalized treatment for HBV carriers.

A novel computer-aided diagnostic system for accurate detection and grading of liver tumors

Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{1}$$\end{document}1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$88\%\pm 5\%$$\end{document}88%±5%, 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI

PURPOSE

With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions for a deep learning (DL) model that predicts contrast-enhanced images of the brain with approximately 10% of the standard dose, across different sites and scanners.

METHODS

The proposed DL model consists of a set of methods that improve the model robustness and generalizability. The steps include multi-planar reconstruction, 2.5D model, enhancement-weighted L1, perceptual, and adversarial losses. The proposed model predicts contrast-enhanced images from corresponding pre-contrast and low-dose images. With IRB approval and informed consent, 640 heterogeneous patient scans (56 train, 13 validation, and 571 test) from 3 institutions consisting of 3D T1-weighted brain images were used. Quantitative metrics were computed and 50 randomly sampled test cases were evaluated by 2 board-certified radiologists. Quantitative tumor segmentation was performed on cases with abnormal enhancements. Ablation study was performed for systematic evaluation of proposed technical solutions.

RESULTS

The average peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) between full-dose and model prediction were 35.07 ± 3.84 dB and 0.92 ± 0.02 , respectively. Radiologists found the same enhancing pattern in 45/50 (90%) cases; discrepancies were minor differences in contrast intensity and artifacts, with no effect on diagnosis. The average segmentation Dice score between full-dose and synthesized images was 0.88 ± 0.06 (median = 0.91).

CONCLUSIONS

We have proposed a DL model with technical solutions for low-dose contrast-enhanced brain MRI with potential generalizability under diverse clinical settings.

Bone mesenchymal stem cells derived extracellular vesicles promote TRAIL-related apoptosis of hepatocellular carcinoma cells via the delivery of microRNA-20a-3p

OBJECTIVE

Bone mesenchymal stem cells (BMSCs) have been widely researched in cancer treatment, including hepatocellular carcinoma (HCC). This study intended to discuss the mechanism of miR-20a-3p in BMSCs-extracellular vesicles (EVs) in HCC apoptosis.

METHODS

BMSCs were isolated and identified. EVs derived from BMSCs were extracted and identified. After overexpressing or inhibiting miR-20a-3p expression in BMSCs, EVs were extracted and acted on HCC cells and transplanted tumors. HCC cell apoptosis in the treatment of BMSCs-conditioned medium, BMSCs-EVs and/or miR-20a-3p mimic/inhibitor was evaluated, with the detection of levels of TRAIL and TRAIL-related proteins. A functional rescue experiment about c-FLIP was carried out in HCC cells. The target binding relationship between miR-20a-3p and c-FLIP was detected. The subcutaneous tumorigenesis model of mice was established and injected with BMSCs-EVs to estimate the effect of BMSCs-EVs-miR-20a-3p on HCC growth.

RESULTS

EVs isolated from BMSCs conditioned medium promoted the apoptosis of HCC cells. After BMSCs-EVs treatment, TRAIL levels, downstream proteins and miR-20a-3p were increased significantly, but the expression of c-FLIP was decreased. miR-20a-3p could target c-FLIP. BMSCs-EVs inhibited the growth of HCC cells, decreased c-FLIP expression, increased TRAIL levels, and promote the of HCC cell apoptosis. BMSCs-EVs with overexpressing miR-20a-3p further enhanced the apoptotic effect of HCC cells in vitro and in vivo.

CONCLUSION

BMSCs-EVs-carried miR-20a-3p targets c-FLIP and increases TRAIL levels in HCC cells, thus promoting TRAIL-related apoptosis.

Circular RNA hsa_circ_0004277 Stimulates Malignant Phenotype of Hepatocellular Carcinoma and Epithelial-Mesenchymal Transition of Peripheral Cells

Accumulating evidence shows that exosomal circRNAs reflect the physiological status of donor cells, and various cell reactions are induced after exosomal circRNAs are captured by recipient cells. In this study, qRT-PCR was performed to detect circ-0004277 expression in hepatocellular carcinoma (HCC) cell lines, tissues, and plasma exosomes. The effects of circ-0004277 on the proliferation and migration of HCC cells were assessed by cell counting, 5-ethynyl-2'-deoxyuridine assays, Transwell migration assays, and tumor formation in nude mice. We found that circ-0004277 was significantly upregulated in HCC cells, tissues, and plasma exosomes compared to that in normal controls. Overexpression of circ-0004277 enhanced the proliferation, migration, and epithelial-mesenchymal transition (EMT) of HCC cells in vivo and in vitro. Furthermore, exosomes from HCC cells enhanced circ-0004277 expression in surrounding normal cells and stimulated EMT progression. ZO-1, a tight junction adapter protein, was downregulated in HCC tissues. In conclusion, our findings suggest that circ-0004277 promotes the malignant phenotype of HCC cells via inhibition of ZO-1 and promotion of EMT progression. In addition, exosomal circ-0004277 from HCC cells stimulates EMT of peripheral cells through cellular communication to further promote the invasion of HCC into normal surrounding tissues.

GAN and dual-input two-compartment model-based training of a neural network for robust quantification of contrast uptake rate in gadoxetic acid-enhanced MRI

PURPOSE

Gadoxetic acid uptake rate (k₁ ) obtained from dynamic, contrast-enhanced (DCE) magnetic resonance imaging (MRI) is a promising measure of regional liver function. Clinical exams are typically poorly temporally characterized, as seen in a low temporal resolution (LTR) compared to high temporal resolution (HTR) experimental acquisitions. Meanwhile, clinical demands incentivize shortening these exams. This study develops a neural network-based approach to quantitation of k₁ , for increased robustness over current models such as the linearized single-input, two-compartment (LSITC) model.

METHODS

Thirty Liver HTR DCE MRI exams were acquired in 22 patients with at least 16 min of postcontrast data sampled at least every 13 s. A simple neural network (NN) with four hidden layers was trained on voxel-wise LTR data to predict k₁ . Low temporal resolution data were created by subsampling HTR data to contain six time points, replicating the characteristics of clinical LTR data. Both the total length and the placement of points in the training data were varied considerably to encourage robustness to variation. A generative adversarial network (GAN) was used to generate arterial and portal venous inputs for use in data augmentation based on the dual-input, two-compartment, pharmacokinetic model of gadoxetic acid in the liver. The performance of the NN was compared to direct application of LSITC on both LTR and HTR data. The error was assessed when subsampling lengths from 16 to 4 min, enabling assessment of robustness to acquisition length.

RESULTS

For acquisition lengths of 16 min NRMSE (Normalized Root-Mean-Squared Error) in k₁ was 0.60, 1.77, and 1.21, for LSITC applied to HTR data, LSITC applied to LTR data, and GAN-augmented NN applied to LTR data, respectively. As the acquisition length was shortened, errors greatly increased for LSITC approaches by several folds. For acquisitions shorter than 12 min the GAN-augmented NN approach outperformed the LSITC approach to a statistically significant extent, even with HTR data.

CONCLUSIONS

The study indicates that data length is significant for LSITC analysis as applied to DCE data for standard temporal sampling, and that machine learning methods, such as the implemented NN, have potential for much greater resilience to shortened acquisition time than directly fitting to the LSITC model.

Integrating radiomics into clinical trial design

In radiomics, quantitative features that describe phenotypic tumor characteristics are derived from radiographic images. Because radiomics generates information from routine medical images, it is a powerful way to non-invasively examine the spatial and temporal heterogeneity of disease, and thus has potential to significantly impact clinical trial design, execution, and ultimately patient care. The aim of this review article is to discuss how radiomics may address some of the current challenges in clinical randomized control trials, and the difficulties of integrating robust and repeatable radiomics analysis into trial design. Each step of the radiomics process, including image acquisition and reconstruction, image segmentation, feature extraction, and computational analysis, requires extensive standardization in order to be successfully incorporated into clinical trials and inform clinical decision making. By addressing these challenges, the potential of radiomics may be realized.

A preliminary study of the local biomechanical environment of liver tumors in vivo

PURPOSE

Biomechanical properties can be used as biomarkers to diagnose tumors, monitor tumor development, and evaluate treatment efficacy. The purpose of this preliminary study is to characterize the biomechanical environment of two typical liver tumors, hemangiomas (HEMs) and hepatocellular carcinomas (HCCs), and to investigate the potential of using strain metrics as biomarkers for tumor diagnosis, based on a limited clinical dataset.

METHODS

Magnetic resonance (MR) tagging was used to quantify the motion and deformation of the two types of liver tumors. Displacements of the tumors arising from a heartbeat were measured over one cardiac cycle. Local biomechanical conditions of the tumors were characterized by estimating two principal strains (ε₁ and ε₂ ) and an octahedral shear strain (ε_soct ) of the tumor and its peripheral region. Biomechanical conditions of the tumors were compared with those of the arbitrarily selected regions from healthy volunteers.

RESULTS

We observed that the HCCs had significantly smaller strain values compared to their peripheral tissues. However, the HEMs did not have significantly different strains from those of the peripheral tissues, and were similar to healthy liver regions. The sensitivity of using ε₁ , ε₂ , and ε_soct to diagnose HCC were all 1, while the sensitivity of using ε₁ , ε₂ , and ε_soct to diagnose HEM were 0.67, 0.17, and 0.67, respectively.

CONCLUSIONS

Lagrangian strain metrics provide insight into the biomechanical conditions of certain liver tumors in the human body and may provide another perspective for tumor characterization and diagnosis.

Pathological variants of hepatocellular carcinoma on MRI: emphasis on histopathologic correlation

Hepatocellular carcinoma (HCC) is a unique tumor because it is one of the few cancers which can be treated based on imaging alone. Magnetic resonance imaging (MRI) carries higher sensitivity and specificity for the diagnosis of HCC than either computed tomography (CT) or ultrasound. MRI is imaging modality of choice for the evaluation of complex liver lesions and HCC because of its inherent ability to depict cellularity, fat, and hepatocyte composition with high soft tissue contrast. The imaging features of progressed HCC are well described. However, many HCC tumors do not demonstrate classical imaging features, posing a diagnostic dilemma to radiologists. Some of these can be attributed to variations in tumor biology and histology, which result in radiological features that differ from the typical progressed HCC. This pictorial review seeks to demonstrate the appearance of different variants of HCC on MRI imaging, in relation to their histopathologic features.

LI-RADS 2017: An update

The computed tomography / magnetic resonance imaging (CT/MRI) Liver Imaging Reporting & Data System (LI-RADS) is a standardized system for diagnostic imaging terminology, technique, interpretation, and reporting in patients with or at risk for developing hepatocellular carcinoma (HCC). Using diagnostic algorithms and tables, the system assigns to liver observations category codes reflecting the relative probability of HCC or other malignancies. This review article provides an overview of the 2017 version of CT/MRI LI-RADS with a focus on MRI. The main LI-RADS categories and their application will be described. Changes and updates introduced in this version of LI-RADS will be highlighted, including modifications to the diagnostic algorithm and to the optional application of ancillary features. Comparisons to other major diagnostic systems for HCC will be made, emphasizing key similarities, differences, strengths, and limitations. In addition, this review presents the new Treatment Response algorithm, while introducing the concepts of MRI nonviability and viability. Finally, planned future directions for LI-RADS will be outlined.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1459-1474.

Magnetic resonance imaging ancillary features used in Liver Imaging Reporting and Data System: An illustrative review

Hepatocellular carcinoma (HCC) usually develops in the setting of chronic liver disease. In the adequate clinical context, both multiphasic contrast-enhanced CT and magnetic resonance imaging are non-invasive modalities that allow accurate diagnosis and staging of HCC, although the latter demonstrates greater sensitivity and specificity. Imaging criteria for HCC diagnosis rely on hemodynamic features such as hyperenhancement in the arterial phase and washout in the portal or equilibrium phase. However, imaging performance drops considerably for small (< 20 mm) nodules because their tendency to exhibit atypical enhancement patterns. In order to improve accuracy in the diagnosis and staging of HCC, particularly in cases of atypical nodules, ancillary features, i.e., imaging characteristics that modify the likelihood of HCC, have been described and incorporated into clinical reports, especially in Liver Imaging Reporting and Data System. In this paper, ancillary imaging features will be reviewed and illustrated.

LI-RADS technical requirements for CT, MRI, and contrast-enhanced ultrasound

Accurate detection and characterization of liver observations to enable HCC diagnosis and staging using LI-RADS requires a technically adequate imaging exam. To help achieve this objective, LI-RADS has proposed technical requirements for CT, MR, and contrast-enhanced ultrasound of liver. This article reviews the technical requirements for liver imaging, including the description of minimum acceptable technical standards, such as the scanner hardware requirements, recommended dynamic imaging phases, and common technical challenges of liver imaging.

Liver Masses: What Physicians Need to Know About Ordering and Interpreting Liver Imaging

PURPOSE OF REVIEW

This paper reviews diagnostic imaging techniques used to characterize liver masses and the imaging characteristics of the most common liver masses.

RECENT FINDINGS

The role of recently adopted ultrasound and magnetic resonance imaging contrast agents will be emphasized. Contrast-enhanced ultrasound is an inexpensive exam which can confirm benignity of certain liver masses without ionizing radiation. Magnetic resonance imaging using hepatocyte-specific gadolinium-based contrast agents can help confirm or narrow the differential diagnosis of liver masses.

CD146-targeted immunoPET and NIRF Imaging of Hepatocellular Carcinoma with a Dual-Labeled Monoclonal Antibody

Overexpression of CD146 has been correlated with aggressiveness, recurrence rate, and poor overall survival in hepatocellular carcinoma (HCC) patients. In this study, we set out to develop a CD146-targeting probe for high-contrast noninvasive in vivo positron emission tomography (PET) and near-infrared fluorescence (NIRF) imaging of HCCs. YY146, an anti-CD146 monoclonal antibody, was employed as a targeting molecule to which we conjugated the zwitterionic near-infrared fluorescence (NIRF) dye ZW800-1 and the chelator deferoxamine (Df). This enabled labeling of Df-YY146-ZW800 with (89)Zr and its subsequent detection using PET and NIRF imaging, all without compromising antibody binding properties. Two HCC cell lines expressing high (HepG2) and low (Huh7) levels of CD146 were employed to generate subcutaneous (s.c.) and orthotopic xenografts in athymic nude mice. Sequential PET and NIRF imaging performed after intravenous injection of (89)Zr-Df-YY146-ZW800 into tumor-bearing mice unveiled prominent and persistent uptake of the tracer in HepG2 tumors that peaked at 31.65 ± 7.15 percentage of injected dose per gram (%ID/g; n=4) 72 h post-injection. Owing to such marked accumulation, tumor delineation was successful by both PET and NIRF, which facilitated the fluorescence image-guided resection of orthotopic HepG2 tumors, despite the relatively high liver background. CD146-negative Huh7 and CD146-blocked HepG2 tumors exhibited significantly lower (89)Zr-Df-YY146-ZW800 accretion (6.1 ± 0.5 and 8.1 ± 1.0 %ID/g at 72 h p.i., respectively; n=4), demonstrating the CD146-specificity of the tracer in vivo. Ex vivo biodistribution and immunofluorescent staining corroborated the accuracy of the imaging data and correlated tracer uptake with in situ CD146 expression. Overall, (89)Zr-Df-YY146-ZW800 showed excellent properties as a PET/NIRF imaging agent, including high in vivo affinity and specificity for CD146-expressing HCC. CD146-targeted molecular imaging using dual-labeled YY146 has great potential for early detection, prognostication, and image-guided surgical resection of liver malignancies.