Characterization Of Liver Tumors Research Articles

Pixel-based small-window parametric ultrasound imaging for liver tumor characterization.

Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor. Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations. In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images. The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

Histopathological Spectrum and Molecular Characterization of Liver Tumors in the Setting of Fontan-Associated Liver Disease.

Univentricular heart is corrected with the Fontan procedure (FP). In the long term, so-called Fontan-associated liver diseases (FALDs) can develop. The aim of this study is to analyze the molecular profile of FALDs. FALDs between January 1990 and December 2022 were reviewed for histology and immunohistochemistry, laboratory data, and images. Targeted next generation sequencing (NGS), performed on the DNA and RNA of both neoplastic and non-lesional liver tissue, was applied. A total of 31/208 nodules > 1 cm in diameter were identified on imaging, but a liver biopsy was available for five patient demonstrating the following: one hepatocellular adenoma (HA), two hepatocellular carcinomas (HCCs), one fibrolamellar carcinoma (FLC), and one intrahepatic cholangiocarcinoma (ICC). Molecular analysis showed a copy number alteration involving FGFR3 in three cases (two HCCs and one ICC) as well as one HCC with a hotspot mutation on the CTNNB1 and NRAS genes. Tumor mutational burden ranged from low to intermediate. A variant of uncertain significance in GNAS was present in two HCCs and in one ICC. The same molecular profile was observed in a non-lesional liver. A DNAJB1-PRKACA fusion was detected only in one FLC. Neoplastic FALDs show some unusual molecular profiles compared with non-Fontan ones. The presence of the same alterations in non-lesional cardiac cirrhosis could contribute to the development of FALD.

Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians.

Contrast-enhanced ultrasound (CEUS) is widely used in the characterization of liver tumors; however, the evaluation of perfusion patterns using CEUS has a subjective character. This study aims to evaluate the accuracy of an automated method based on CEUS for classifying liver lesions and to compare its performance with that of two experienced clinicians. The system used for automatic classification is based on artificial intelligence (AI) algorithms. For an interpretation close to the clinical setting, both clinicians knew which patients were at high risk for hepatocellular carcinoma (HCC), but only one was aware of all the clinical data. In total, 49 patients with 59 liver tumors were included. For the benign and malignant classification, the AI model outperformed both clinicians in terms of specificity (100% vs. 93.33%); still, the sensitivity was lower (74% vs. 93.18% vs. 90.91%). In the second stage of multiclass diagnosis, the automatic model achieved a diagnostic accuracy of 69.93% for HCC and 89.15% for liver metastases. Readers demonstrated greater diagnostic accuracy for HCC (83.05% and 79.66%) and liver metastases (94.92% and 96.61%) compared to the AI system; however, both were experienced sonographers. The AI model could potentially assist and guide less-experienced clinicians to discriminate malignant from benign liver tumors with high accuracy and specificity.

Diffusion-Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?

Background: Strong correlation has been reported between tissue water diffusivity and tissue elasticity in the liver. The purpose of this study is to explore the capability of diffusion–based virtual MR elastography (VMRE) in the characterization of liver tumors by extending beyond liver fibrosis assessments. Methods: Fifty-four patients (56 liver tumors: hepatocellular carcinoma (HCC), 31; metastases, 25) who underwent MRE, diffusion-weighted imaging (DWI) (b: 0, 800 s/mm2), and VMRE (b: 200, 1500 s/mm2) were enrolled. The MRE shear modulus (µMRE), apparent diffusion coefficient (ADC), and shifted ADC (sADC) were obtained. Virtual stiffness (µdiff) was estimated from the relationship between µMRE and sADC. A linear discriminant analysis combining VMRE and MRE to classify HCC and metastases was performed in a training cohort (thirty-two patients) to estimate a classifier (C), and evaluate its accuracy in a testing cohort (twenty-two patients). Pearson’s correlations between µMRE, sADC, and ADC were evaluated. In addition to the discriminant analysis, a receiver operating characteristic (ROC) curve was used to assess the discrimination capability between HCC and metastases. Results: The correlations between µMRE and sADC were significant for liver, HCC, and metastases (r = 0.91, 0.68, 0.71; all p < 0.05). Those between µMRE and ADC were weaker and significant only for metastases (r = 0.17, 0.20, 0.55). µdiff values were not significantly different between HCC and metastases (p = 0.56). Areas under the curves (AUC) to differentiate HCC from metastases were as follows: VMRE, 0.46; MRE alone, 0.89; MRE + VMRE, 0.96. The classifier C also provided better performance than MRE alone, in terms of sensitivity (100 vs. 93.5%, respectively) and specificity (92 vs. 76%, respectively, p = 0.046). Conclusions: The correlation between sADC and µMRE was strong both in the liver and in tumors. However, VMRE alone could not classify HCC and metastases. The combination of MRE and VMRE, however, allowed discriminant performance between HCC and metastases.

Advanced CT techniques for assessing hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Dynamic PET/CT with the Hepatobiliary Tracer [68Ga]Ga-Tmos-DAZA for Characterization of a Hepatic Tumor.

Established imaging modalities for the characterization of liver tumors are computed tomography (CT), magnetical resonance (MR) imaging, sonography, and hepatobiliary scintigraphy. In some cases, their results may be inconclusive or certain examinations not possible due to contraindications. Positron emission tomography (PET)/CT has the capability of dynamic imaging with high temporal resolution. With radiolabeled tri-alkoxysalicyl-1,4-diazepan-6-amine (TAoS-DAZA) tracers, imaging of liver perfusion and hepatobiliary function is possible in a single examination. In the presented case, the PET/CT was performed in a patient with suspected hepatocellular carcinoma and atypical CT findings. PET imaging characteristics were consistent with a hepatocellular carcinoma (HCC). PET with DAZA ligands may be a supplemental method for liver tumor characterization in difficult cases.

Radiomics and deep learning in liver diseases.

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Updates on Imaging of Liver Tumors.

We review advances in imaging of liver tumors, by particularly focusing on the utility of novel imaging in diagnosis and management of these lesions. Contrast-enhanced CT and/or MRI are currently utilized for accurate diagnosis of liver tumors, but several ongoing studies are examining the use of other advanced techniques. Novel CT (i.e., dual-energy CT and perfusion CT), MRI (diffusion-weighted imaging, MR elastography, and T1 mapping), and image processing (texture analysis and artificial intelligence-based methods) techniques have emerged and can be used for precise characterization of liver tumors, quantification of treatment responses, and prediction of overall survival rate of patients. Recent advancements in imaging of liver tumors allowed for a precise assessment of tumor features. These evolving technologies can be utilized for applying individualized treatment based on the presence of specific imaging biomarkers.

Sonographic diagnostics of liver tumors

Liver tumors are often incidentally found during ultrasound examinations. The decision on further diagnostic imaging depends on the clinical context and the appearance in Bmode ultrasound. This review highlights the role of grey scale and contrast-enhanced ultrasound (CEUS) and summarizes the ultrasonographic key features of the most common benign and malignant liver tumors. Conventional grey scale ultrasound is recommended in several guidelines for screening and follow-up of liver tumors in certain risk populations but its ability to characterize liver tumors is limited in most cases. The CEUS has an excellent tolerability and enables liver tumor characterization with ahigh sensitivity and specificity. The diagnostic value of CEUS is comparable to magnetic resonance imaging. In the case of unclear lesions, inconclusive findings by different imaging methods or if molecular targeted treatment is pursued, ultrasound-guided biopsy is often mandatory. Ultrasound is arapidly and ubiquitously available method for the detection of liver tumors and CEUS is the only imaging method that enables real-time examination of all contrast phases in the liver. It should therefore be used as the first line imaging method for liver tumor characterization.

Conspicuity of Malignant Liver Tumors on Diffusion-Weighted Imaging With Short tau Inversion Recovery After Gadolinium Ethoxybenzyl Diethylenetriaminepentaacetic Acid Administration.

Diffusion-weighted imaging (DWI) has been used for the detection and characterization of liver tumors because it has excellent contrast resolution. DWI using short tau inversion recovery (STIR) can improve tumor-to-liver contrast after gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) administration that shortens the T1 relaxation of liver parenchyma. To quantitatively and qualitatively compare the conspicuity of malignant liver tumors on DWI after Gd-EOB-DTPA administration between STIR and chemical shift selective (CHESS) sequences. Single-institution retrospective study. Fifty-seven patients with histologically confirmed malignant liver tumors were evaluated. Low b-value DWIs with STIR and CHESS sequences 18-20 minutes after Gd-EOB-DTPA administration were acquired at 1.5T. Tumor contrast-to-noise ratio (CNR) and visual grade of tumor conspicuity on DWI between STIR and CHESS sequences were compared. Paired Student's t-test and the Wilcoxon signed rank-test were applied. P < 0.05 was considered statistically significant. The mean tumor CNR and visual grade of tumor conspicuity on DWI were significantly higher for STIR than for CHESS (both P < 0.001). Regardless of the presence of chronic liver disease, the mean CNR (normal liver 33.5 ± 19.8 vs. 15.7 ± 12.2, P < 0.001; chronic liver disease 19.6 ± 11.0 vs. 9.2 ± 7.8, P < 0.001) and the visual conspicuity grade (normal liver 3.36 ± 0.64 vs. 2.56 ± 0.77, P < 0.001; chronic liver disease 2.94 ± 0.80 vs. 2.25 ± 0.84, P = 0.001) were significantly higher for STIR than for CHESS. Mean CNR and the visual conspicuity grade were also significantly higher for STIR than for CHESS in patients with hepatocellular carcinomas (CNR 18.1 ± 10.5 vs. 8.8 ± 7.2, P < 0.001; visual grade 2.88 ± 0.83 vs. 2.22 ± 0.87, P = 0.001) or metastases (CNR 35.0 ± 19.3 vs. 16.2 ± 13.1, P < 0.001; visual grade 3.45 ± 0.51 vs. 2.59 ± 0.73, P < 0.001). DWI using STIR may be more helpful for depicting malignant liver tumors after Gd-EOB-DTPA administration compared with DWI using CHESS. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:565-573.

Abstract 4096: Stage-defined, transgenic immunocompetent mouse model (ASV-B) to investigate new drugs for hepatocellular carcinoma

Abstract Background: Hepatocellular carcinoma (HCC) is a complex multistep malignancy in need of new therapeutic options often arising on underlying chronic liver disease. HCC animal models relevant to clinical situations are crucial to investigate new anticancer drugs, alone or in combination. Mice bearing xenografts and transgenic mice are the two main models used for preclinical drug development, but most of them fail to mimic the different step of HCC observed in patients. In this study, we aim to describe a stage-defined, transgenic immunocompetent HCC mouse model. Methods: ASV-B is a transgenic mouse model that spontaneously develops, upon SV40 T-Ag oncogene expression in hepatocytes, a reproducible stage-defined HCC. Hyperplasia at week(W)8 is followed by nodular stage at W12 (multiple well-delimited tumor nodules of about 1 mm, growing progressively up to 1 cm), then diffuse carcinoma stage at W16-20. HCC is restricted to male, backcrossed with C57BL/6J mice, the female littermates being used as controls. Results: Liver volume assessed by ultrasound at each step of carcinogenesis showed a 2.7-, 2.7-, 3.9-, and 5.8-fold increase (p&amp;lt;0.001) in transgenic mice compared to controls (CTRL) at W8, 12, 16, and 20, respectively. ASV-B model displays marked arterialization with intense arterial flow in liver tumor similar to human HCC. Sinusoids in tumor nodules are tortuous and dilated, surrounded by activated hepatic stellate cells (HSCs) expressing smooth muscle actin (SMA) while HSCs surrounding normal sinusoids only express desmin. In addition, ASV-B livers develop along carcinogenesis a mesenchymal phenotype (increased vimentin and decreased e-cadherin staining), mimicking certain clinical situations of acquired resistance to VEGFR inhibitors. Angiogenesis, monitored using Doppler assessing blood flow velocity (BFV) in the hepatic artery, is enhanced by 29%, 51% and 156% at W8, W12 and W16, respectively, in transgenic liver animals as compared to CTRL. Increased angiogenesis was confirmed by CD31 staining in tumor specimen showing increased number of vessels per field. ASV-B model has been used to investigate the toxicity and efficacy of new drugs (galunisertib, MET/AXL inhibitor), including antiangiogenic agents (sorafenib and ramucirumab) alone or in combination, and may be relevant to explore the effects of immunotherapy agents. At the conference, we will display further characterization of liver tumors regarding vascularization, and immune cells localization using IHC. Conclusion: ASV-B transgenic mouse model mimics several characteristics of human HCC developing on healthy liver including multinodular disease, vessel abnormalities, and dedifferentiation toward mesenchymal phenotype. Further steps will consist of characterizing the adjacent nontumor liver, and developing nonalcoolic steatohepatitis (NASH) on ASV-B model using specific diets. Citation Format: Annemilaï Tijeras-Raballand, Patricia Hainaud, Christian Hobeika, Clarisse Eveno, Marc Pocard, Philippe Bonnin, Valérie Paradis, Mohamed Bouattour, Eric Raymond, Armand de Gramont, Evelyne Dupuy, Sandrine Faivre. Stage-defined, transgenic immunocompetent mouse model (ASV-B) to investigate new drugs for hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4096.

Contrast-enhanced ultrasound of the liver: technical and lexicon recommendations from the ACR CEUS LI-RADS working group.

Contrast-enhanced ultrasound (CEUS) is a specific form of ultrasound imaging performed with intravenous administration of microbubble contrast agents. It has been extensively used for liver tumor characterization and was recently added to the American College of Radiology Liver Imaging Reporting and Data System (CEUS LI-RADS). This paper describes technical recommendations for successful liver CEUS lesion characterization, and provides imaging protocol and Lexicon of imaging findings.

Elasticity Characterization of Liver Cancers Using Shear Wave Ultrasound Elastography: Comparison Between Hepatocellular Carcinoma and Liver Metastasis

Purpose:This was a feasibility study of shear wave ultrasound elastography for characterization of liver tumors and to compare the tissue elasticity values of hepatocellular carcinoma (HCC), liver metastases, and normal liver tissues.Methods:Forty-one patients and 30 healthy volunteers were recruited and categorized into HCC, liver metastasis, and control groups based on their computed tomography and sonographic examinations. Elasticity values of different groups were compared statistically.Results:Mean (standard deviation) elasticity values for HCC, liver metastasis, and normal liver tissue were 51.45 (14.96), 49.89 (13.82), and 6.63 (1.65) kilopascal, respectively. Statistically significant differences were found between the elasticity values of HCC and liver metastasis with normal liver tissue. Based on the receiver operating characteristics analysis, 18.25 kilopascal may differentiate the malignant focal liver lesions from the normal liver tissue with both sensitivity and specificity of 100%.Conclusion:Shear wave ultrasound elastography may be able to differentiate HCC and liver metastasis from normal liver tissue based on the tissue elasticity values.

CEUS LI-RADS: algorithm, implementation, and key differences from CT/MRI.

Contrast-enhanced ultrasound (CEUS) is a specialized form of ultrasound (US) performed with an intravenous injection of microbubble contrast agents. It has been successfully used for a variety of applications including characterization of liver tumors. In April 2014, the American College of Radiology (ACR) convened a working group of international experts to develop ACR CEUS Liver Imaging Reporting and Data System (CEUS LI-RADS). An initial version of CEUS LI-RADS was published in August 2016. Although the CEUS LI-RADS concept and principles for liver lesion characterization, using dynamic contrast enhancement features, are similar to those for CT or MRI, there are significant differences between CT/MRI and CEUS LI-RADS. Therefore, CEUS LI-RADS has different diagnostic features and a unique characterization algorithm. The size of a lesion, the type and degree of arterial phase enhancement, the presence of washout, and the timing and degree of washout are the major features used for categorization. This paper describes key differences between CT/MRI and CEUS, and provides a diagnostic algorithm of CEUS LI-RADS with detailed, step-by-step instructions and imaging examples of CEUS LI-RADS categories.

CEUS in the Diagnosis and Treatment for Malignant Liver Tumors

Outline of the Lecture1.CEUS with Sonazoid is useful in the characterization of Liver Tumors.2.In RFA treatment, there are some HCC lesions, that are demonstrated or CT or MRI, can not be visualized by B-mode US alone.3.However, using defect re-perfusion imaging technique (re-injection of sonazoid for the defect at Kupffer phase, HCC nodules can be clearly demonstrated, resulting in making the RFA procedure possible.4.In addition, overlay imaging by use of CEUS, correct judgement of ablative margin becomes possible.

Potential Pitfalls of Contrast Enhanced Ultrasound (CEUS) in Characterization of Liver Tumours: Case Series

Potential Pitfalls of Contrast Enhanced Ultrasound (CEUS) in Characterization of Liver Tumours: Case Series

Smyd3-associated regulatory pathways in cancer.

SMYD3 is a member of the SET and MYND-domain family of methyl-transferases, the increased expression of which correlates with poor prognosis in various types of cancer. In liver and colon tumors, SMYD3 is localized in the nucleus, where it interacts with RNA Pol II and H3K4me3 and functions as a selective transcriptional amplifier of oncogenes and genes that control cell proliferation and metastatic spread. Smyd3 expression has a high discriminative power for the characterization of liver tumors and positively correlates with poor prognosis. In lung and pancreatic cancer, SMYD3 acts in the cytoplasm, potentiating oncogenic Ras/ERK signaling through the methylation of the MAP3K2 kinase and the subsequent release from its inhibitor. A clinico-pathological analysis of lung cancer patients uncovers prognostic significance of SMYD3 only for first progression survival. However, stratification of patients according to their smoking history significantly expands the prognostic value of SMYD3 to overall survival and other features, suggesting that smoking-related effects saturate the clinical analysis and mask the function of SMYD3 as an oncogenic potentiator.

Differential diagnosis of liver tumours using intraoperative real-time tissue elastography

Real-time tissue elastography is an innovative tool that informs the surgeon about tissue elasticity by applying the principle of ultrasonography. The aim of this study was to investigate the accuracy of intraoperative real-time tissue elastography (IORTE) for the detection and characterization of liver tumours. Between October 2010 and November 2011, IORTE was performed for liver lesions after the completion of routine B-mode intraoperative ultrasonography (IOUS). The elasticity images of all tumours, except those of cysts, were classified into six categories according to modified criteria (types 1-6), according to the degree of strain contrast with the surrounding liver. The concordance of IORTE with pathological examination of the tumour, B-mode IOUS and clinical diagnosis after follow-up was assessed. Images were obtained from 92 hepatocellular carcinomas (HCCs), 92 adenocarcinomas, 19 other malignant tumours and 18 benign tumours in 158 patients. Using a minilinear probe, 73 of 88 HCCs were classified as having a 'HCC pattern' (type 3, 4 or 5), resulting in a sensitivity of 83·0 per cent, a specificity of 67·2 per cent and an accuracy of 73·7 per cent. Some 66 of 90 adenocarcinomas were classified as 'adenocarcinoma pattern' (type 6), resulting in a sensitivity of 73·3 per cent, specificity of 95·1 per cent and accuracy of 85·9 per cent. IORTE detected seven new lesions (8 per cent). IORTE is useful for the detection and characterization of liver tumours.

MRI for characterization of primary tumors in the non-cirrhotic liver: Added value of Gd-EOB-DTPA enhanced hepatospecific phase

PurposeTo evaluate the added value of hepatospecific phase in Gd-EOB-DTPA enhanced magnetic resonance imaging (MRI) in patients with primary tumors in non-cirrhotic liver. MethodsTwenty-nine patients (median, 39 years; range, 18–81 years; 11 male) underwent preoperative Gd-EOB-DTPA enhanced MRI including hepatospecific phase after 10 and 20min of contrast injection at four institutions in Europe, North America and New Zealand. Images were evaluated by three different readers (R1–R3) who characterized liver tumors with and without consultation of the hepatospecific phase images. Confidence in diagnosis was scored on a visual analog scale from 1 to 10. Histopathology (adenoma, n=5; focal nodular hyperplasia, n=11 and hepatocellular carcinoma, n=13) in all patients served as the standard of reference. Differences were evaluated using the McNemar and Wilcoxon signed rank test. ResultsWithout hepatospecific phase images available, 22 (76%), 19 (66%) and 19 (66%) of 29 tumors were characterized correctly by the three readers respectively. Mean confidence in diagnosis was 6.1, 5.7 and 5.8. With the hepatospecific phase included, characterization of liver tumors did not change significantly with 21 (72%), 23 (79%) and 19 (66%) of 29 tumors diagnosed correctly (p>0.05). According confidence ratings increased to 6.3, 6.5 and 7.7, respectively. Increase in diagnostic confidence was significant for R2 and R3 (p<0.05) and independent of reader's experience. ConclusionThe additional hepatospecific phase in Gd-EOB-DTPA enhanced MRI did not significantly increase diagnostic accuracy in characterization of primary tumors in the non-cirrhotic liver. However, 2/3 readers showed a significant increase in diagnostic confidence after consultation of the hepatospecific phase.