Detection Of Focal Liver Lesions Research Articles

State of the art of magnetic resonance imaging ( MRI ) in evaluation of focal hepatic lesions

Background: The recent developments in (MRI) sequences for the upper abdomen comprise unenhanced diffusion-weighted imaging (DWI), and (DCE) MRI. DWI allows improved detection of small (< 10 mm) focal liver lesions and useful as a road map sequence. The aim of this work was to study recent and advanced magnetic resonance imaging (MRI) techniques in the evaluation of focal hepatic lesions. Methods: this study included 40 patients, patients underwent assessment with recent MRI sequences (DWI), DCE MRI, T2-weighted imaging, steady-state free precession (SSFP) imaging and T1 in-phase/out-phase imaging. Results: Half of the studied patients were females. Age ranged from 30 to 67 years with mean 48.825 years. Forty percent of patients had right-side lesion while 22.5% had left side lesion. On HPE of the studied patients, 52.5% had malignant growth while the remaining 47.5% had benign lesions. About 23% had HCC. One fifth had hemorrhagic cyst and the same percentage had metastatic lesion. Adenoma was present in 7.5%, There is statistically significant relation between nature of foci and ADC value (significantly higher in benign lesions). ROC curve of ADC in diagnosis of benign nature of focal hepatic masses among the studied patients; area under curve 0.945, (p < 0.05). Conclusion: diffusion-weighted MRI sequence with quantitative ADC measurements should be used as an additional sequence to supplement conventional MRI protocol studies for proper detection (using low b values) and characterization (using high b values) of focal liver lesions

Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels

ObjectivesTo assess task-based image quality for two abdominal protocols on various CT scanners. To establish a relationship between diagnostic reference levels (DRLs) and task-based image quality.MethodsA protocol for the detection of focal liver lesions was used to scan an anthropomorphic abdominal phantom containing 8- and 5-mm low-contrast (20 HU) spheres at five CTDIvol levels (4, 8, 12, 16, and 20 mGy) on 12 CTs. Another phantom with high-contrast calcium targets (200 HU) was scanned at 2, 4, 6, 10, and 15 mGy using a renal stones protocol on the same CTs. To assess the detectability, a channelized Hotelling observer was used for low-contrast targets and a non-prewhitening observer with an eye filter was used for high contrast targets. The area under the ROC curve and signal to noise ratio were used as figures of merit.ResultsFor the detection of 8-mm spheres, the image quality reached a high level (mean AUC over all CTs higher than 0.95) at 11 mGy. For the detection of 5-mm spheres, the AUC never reached a high level of image quality. Variability between CTs was found, especially at low dose levels. For the search of renal stones, the AUC was nearly maximal even for the lowest dose level.ConclusionsComparable task-based image quality cannot be reached at the same dose level on all CT scanners. This variability implies the need for scanner-specific dose optimization.Key Points• There is an image quality variability for subtle low-contrast lesion detection in the clinically used dose range.• Diagnostic reference levels were linked with task-based image quality metrics.• There is a need for specific dose optimization for each CT scanner and clinical protocol.

Mn(II) Complex of Lipophilic Group-Modified Ethylenediaminetetraacetic Acid (EDTA) as a New Hepatobiliary MRI Contrast Agent.

Liver-specific contrast agents (CAs) can improve the Magnetic resonance imaging (MRI) detection of focal and diffuse liver lesions by increasing the lesion-to-liver contrast. A novel Mn(II) complex, Mn-BnO-TyrEDTA, with a lipophilic group-modified ethylenediaminetetraacetic acid (EDTA) structure as a ligand to regulate its behavior in vivo, is superior to Gd-EOB-DTPA in terms of a liver-specific MRI contrast agent. An MRI study on mice demonstrated that Mn-BnO-TyrEDTA can be rapidly taken up by hepatocytes with a combination of hepatobiliary and renal clearance pathways. Bromosulfophthalein (BSP) inhibition imaging, biodistribution, and cellular uptake studies confirmed that the mechanism of hepatic targeting of Mn-BnO-TyrEDTA is the hepatic uptake of the amphiphilic anion contrast agent mediated by organic anion transporting polypeptides (OATPs) expressed by functional hepatocytes.

Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.

Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted.

Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI.

Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) has high diagnostic accuracy in the detection of liver lesions. There is a demand for computer-aided detection/diagnosis software for Gd-EOB-DTPA-enhanced MRI. We propose a deep learning-based method using one three-dimensional fully convolutional residual network (3D FC-ResNet) for liver segmentation and another 3D FC-ResNet for simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI. We prepared a five-phase (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary phases) series as the input image sets and labeled focal liver lesion (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scars) images as the output image sets. We used 100 cases to train our model, 42 cases to determine the hyperparameters of our model, and 42 cases to evaluate our model. We evaluated our model by free-response receiver operating characteristic curve analysis and using a confusion matrix. Our model simultaneously detected and classified focal liver lesions. In the test cases, the detection accuracy for whole focal liver lesions had a true-positive ratio of 0.6 at an average of 25 false positives per case. The classification accuracy was 0.790. We proposed the simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI using multichannel 3D FC-ResNet. Our results indicated simultaneous detection and classification are possible using a single network. It is necessary to further improve detection sensitivity to help radiologists.

Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice.

Magnetic resonance imaging (MRI) is highly important for the detection, characterization, and follow-up of focal liver lesions. Several quantitative MRI-based methods have been proposed in addition to qualitative imaging interpretation to improve the diagnostic work-up and prognostics in patients with focal liver lesions. This includes DWI with apparent diffusion coefficient measurements, intravoxel incoherent motion, perfusion imaging, MR elastography, and radiomics. Multiple research studies have reported promising results with quantitative MRI methods in various clinical settings. Nevertheless, applications in everyday clinical practice are limited. This review describes the basic principles of quantitative MRI-based techniques and discusses the main current applications and limitations for the assessment of focal liver lesions.

Image Quality and Detection of Small Focal Liver Lesions in Diffusion-Weighted Imaging: Comparison of Navigator Tracking and Free-Breathing Acquisition.

The aim of this study was to compare intraindividual diffusion-weighted imaging (DWI) of the liver acquired with free breathing (FB) versus navigator triggering (NT) for assessing small focal liver lesions (FLLs) in noncirrhotic patients. Patients with known or suspected multiple FLLs were prospectively included, and spin-echo echo-planar DWI with NT and FB acquisition was performed (b-values, 50 and 800 s/mm2 [b50 and b800]). NT and FB DWI sequences with similar acquisitions times were used. Liver and lesion signal-to-noise ratios were measured at b800. The DWI scans were analyzed independently by 2 readers. Liver edge delineation, presence of stair-step artifacts, vessel sharpness, severity of cardiac motion artifacts, overall image quality, and lesion conspicuity were rated with 5-point Likert scales. Small and large FLLs (ie, <1 cm or ≥1 cm) were rated separately for lesion conspicuity. The FLL detectability was estimated by comparing the number of lesions visible with FB to those visible with NT. Forty-three patients were included in the study. The FB acquisition performed better in terms of severity of cardiac motion artifacts. The NT performed better in terms of liver edge delineation and vessel sharpness. Little difference was found for stair-step artifact, overall image quality, and conspicuity of large FLL, whereas the conspicuity of small FLL was better for NT. For small FLL, both readers found more lesions with NT in 11 cases at b800. For large FLL, this effect was much less pronounced (1 case at b800 reported by 1 of the readers). The mean liver and lesion signal-to-noise ratios were 16.8/41.5 and 19.8/38.4 for NT/FB, respectively. Small FLL detection is better with NT. Large FLL detection by FB and NT is similarly good. We conclude that NT should be used.

Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study

With the increasing daily workload of physicians, computer-aided diagnosis (CAD) systems based on deep learning play an increasingly important role in pattern recognition of diagnostic medical images. In this paper, we propose a framework based on hierarchical convolutional neural networks (CNNs) for automatic detection and classification of focal liver lesions (FLLs) in multi-phasic computed tomography (CT). A total of 616 nodules, composed of three types of malignant lesions (hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastasis) and benign lesions (hemangioma, focal nodular hyperplasia, and cyst), were randomly divided into training and test sets at an approximate ratio of 3:1. To evaluate the performance of our model, other commonly adopted CNN models and two physicians were included for comparison. Our model achieved the best results to detect FLLs, with an average test precision of 82.8%, recall of 93.4%, and F1-score of 87.8%. Our model initially classified FLLs into malignant and benign and then classified them into more detailed classes. For the binary and six-class classification, our model achieved average accuracy results of 82.5 and73.4%, respectively, which were better than the other three classification neural networks. Interestingly, the classification performance of the model was placed between a junior physician and a senior physician. Overall, this preliminary study demonstrates that our proposed multi-modality and multi-scale CNN structure can locate and classify FLLs accurately in a limited dataset, and would help inexperienced physicians to reach a diagnosis in clinical practice.

Comparison and correlation of CT scan and MRI for focal liver lesion detection and characterization

Background: Liver diseases create diagnostic challenge for attending clinicians. Role of radiological imaging, particularly newer modalities like Ultrasound, Color Doppler, CT scan, MRI, DSA, are highly reliable. Early identification of liver lesions provides the opportunity for the success of therapeutic approach. Contrast enhanced CT scan and contrast enhanced MRI are most advanced and appropriate modalities to detect and characterize liver focal lesions. In present study we tried to compare and correlate findings of both modalities. Methodology: We evaluated 40 cases of liver pathologies which are suspected in sonography examination. All patients were undergone contrast enhanced CT scan as well as MRI evaluation. Various radiological findings are recorded in standard format. Result: In total 40 patients of focal liver pathology all age groups are included. Male: female ratio is 70:30.Patients are presented with various clinical symptoms like right hypochondriac pain, fever, lump, jaundice, weight loss etc. Benign lesions are found in 40% of cases and malignant lesions are found in 60 % of cases. Most common pathology is liver metastatic lesions which are picked up by MRI with 100% accuracy. CT is able to characterize only 10 out of 13 metastatic cases(77%). Few other cases of benign lesions like hemangioma, hamartoam ,adenoma and FNH are also remained indeterminate in CT scan which all are accurately diagnosed and characterized by MRI and confirmed in histopathology. A case of HCC, lymphoma and nodular dysplasia also remained indeterminate in CT scan and further diagnosed and characterized by MRI.

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To compare the diagnostic efficacy of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) vs. multidetector computed tomography (MDCT) for the detection and classification of focal liver lesions, differentiated also for lesion entity and size; a separate analysis of pre- and postcontrast images as well as T2-weighted MRI sequences of focal and exclusively solid lesions was integrated.Twenty-nine patients with 130 focal liver lesions underwent MDCT (64-detector-row; contrast medium iopromide; native, arterial, portalvenous, venous phase) and MRI (1.5-T; dynamic and tissue-specific phase 20 min after application of Gd-EOB-DTPA). Hepatic lesions were verified against a standard of reference (SOR). CT and MR images were independently analysed by four blinded radiologists on an ordinal 6-point-scale, determining lesion classification and diagnostic confidence.Among 130 lesions, 68 were classified as malignant and 62 as benign by SOR. The detection of malignant and benign lesions differed significantly between combined and postcontrast MRI vs. MDCT; overall detection rate was 91.5% for combined MRI and 80.4% for combined MDCT (p < 0.05). Considering all four readers together, combined MDCT achieved sensitivity of 66.2%, specificity of 79.0%, and diagnostic accuracy of 72.3%; combined MRI reached superior diagnostic efficacy: sensitivity 86.8%, specificity 94.4%, accuracy 90.4% (p < 0.05). Differentiated for lesion size, in particular lesions <20 mm revealed diagnostic benefit by MRI. Postcontrast MRI also achieved higher overall sensitivity, specificity, and accuracy compared to postcontrast MDCT for focal and exclusively solid liver lesions (p < 0.05).Combined and postcontrast Gd-EOB-DTPA-enhanced MRI provided significantly higher overall detection rate and diagnostic accuracy, including low inter-observer variability, compared to MDCT in a single centre study.

Irregular Respiratory Motion Compensation for Liver Contrast-Enhanced Ultrasound via Transport-Based Motion Estimation.

Contrast-enhanced ultrasound (CEUS) imaging has been widely applied for the detection and characterization of focal liver lesions (FLLs), for its ability to visualize the blood flow in real time. However, cyclic liver motion poses a great challenge to the recovery of perfusion curves as well as quantitative kinetic parameters estimation. Recently, a few gating methods have been proposed to eliminate unexpected intensity fluctuations by the breathing phase estimation, with the assumption that each breathing phase corresponds to a specific lesion position strictly. While practical liver motion tends to be irregular due to changes in the patient's underlying physiologic status, that is, the same phase might not correspond to the same position. To tackle the challenge of motion irregularity, we present a novel motion estimation-based respiratory compensation method, named RCME, which first estimates salient motion information through the framework of optimal transport (OT) by jointly modeling pixel intensity as well as their locations and then employs sparse subspace clustering (SSC) to identify the subset of frames acquired at the same position. Our proposed method is evaluated on 15 clinical CEUS sequences in both qualitative and quantitative manners. Experimental results demonstrate good performance on irregular liver motion compensation.

DIAGNOSTIC ACCURACY OF CONTRAST ENHANCED ULTRASONOGRAPHY IN FOCAL LIVER LESIONS DETECTED BY CONTRAST ENHANCED COMPUTED TOMOGRAPHY

Context: Contrast enhanced Ultrasonography(CEUS) has improved the detection and characterization of focal liver lesions, offering comparable results to those with contrast CT when ultra-sound exploration is technically satisfactory.The vascular property of the lesion in arterial, portal and delayed phases by enhancement and washout is used in this technique. Aim: The aim of the study is to interpret the use of contrast-enhanced ultra-sound (CEUS) in characterising the focal liver lesions and compare the diagnostic accuracy of the contrast-enhanced ultra-sound with contrast enhanced CT Materials and Methods: This is a cross sectional analytical study in patients with focal liver lesions detected by USG/CECT coming to Department of Radio diagnosis in Government Stanley Hospital.The duration of the study is two and a half years from January 2017 to July 2019.Sample Size was total of 51 patients detected by CECT /USG as focal liver lesions registered in Govt. Stanley Medical College Hospital between January 2017 to July 2019. Results: Diagnostic accuracy for CEUS is 76.46 % and CECT is 83.9%. Sensitivity, specificity , PPV ,NPV and Diagnostic accuracy of the CEUS is almost near equal or comparable with that of CECT. Conclusion: CEUS has ability to detect and characterise of focal liver lesions with no risk of radiation exposure and aids in real time imaging assessment.The study parameters like sensitivity, specificity and diagnostic accuracy of the CEUS are comparable with that of CECT.CEUS can be used to characterise the focal liver lesions in patients where CECT is contraindicated.

Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses

Detection and characterization of focal liver lesions (FLLs) is key for optimizing treatment for patients who may have a primary hepatic cancer or metastatic disease to the liver. This is the first study to develop an EUS-based convolutional neural network (CNN) model for the purpose of identifying and classifying FLLs. A prospective EUS database comprising cases of FLLs visualized and sampled via EUS was reviewed. Relevant still images and videos of liver parenchyma and FLLs were extracted. Patient data were then randomly distributed for the purpose of CNN model training and testing. Once a final model was created, occlusion heatmap analysis was performed to assess the ability of the EUS-CNN model to autonomously identify FLLs. The performance of the EUS-CNN for differentiating benign and malignant FLLs was also analyzed. A total of 210,685 unique EUS images from 256 patients were used to train, validate, and test the CNN model. Occlusion heatmap analyses demonstrated that the EUS-CNN model was successful in autonomously locating FLLs in 92.0% of EUS video assets. When evaluating any random still image extracted from videos or physician-captured images, the CNN model was 90% sensitive and 71% specific (area under the receiver operating characteristic [AUROC], 0.861) for classifying malignant FLLs. When evaluating full-length video assets, the EUS-CNN model was 100% sensitive and 80% specific (AUROC, 0.904) for classifying malignant FLLs. This study demonstrated the capability of an EUS-CNN model to autonomously identify FLLs and to accurately classify them as either malignant or benign lesions.

Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images

To assess the impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images from a Fast kV-Switching Dual Energy CT (KVSCT) platform. Acquisitions on an image-quality phantom were performed using a KVSCT for three dose levels (CTDIvol:12.72/10.76/8.79mGy). Raw data were reconstructed for five energy levels (40/50/60/70/80keV) using Filtered Back Projection (FBP) and four levels of ASIR (ASIR30/ASIR50/ASIR70/ASIR100). Noise power spectrum (NPS) and task-based transfer function (TTF) were measured before computing a Detectability index (d') to model the detection task of liver metastasis (LM) and hepatocellular carcinoma (HCC) as function of keV. From 40 to 70keV, noise-magnitude was reduced on average by -68%±1% with FBP; -61%±3% with ASIR50 and -52%±6% with ASIR100. The mean spatial frequency of the NPS decreased when the energy level decreased and the iterative level increased. TTF values at 50% decreased as the energy level increased and as the percentage of ASIR increased. The detectability of both lesions increased with increasing dose level and percentage of ASIR. For the LM, d' peaked at 70keV for all reconstruction types, except for ASIR70 at 12.72mGy and ASIR100, where d' peaked at 50keV. For HCC, d' peaked at 60keV for FBP and ASIR30 but peaked at 50keV for ASIR50, ASIR70 and ASIR100. Using percentage of ASIR above 50% at low-energy monochromatic images could limit the increase of noise-magnitude, benefit from spatial resolution improvement and hence enhance detectability of subtle low contrast focal liver lesions such as HCC.

Focal Liver Lesion Detection in Ultrasound Image Using Deep Feature Fusions and Super Resolution

This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.

Beyond the vascular profile: conventional DWI, IVIM and kurtosis in the assessment of hepatocellular carcinoma.

To describe the role of the Diffusion Weighted Imaging (DWI) in the assessment of hepatocellular carcinoma (HCC). Several electronic databases were evaluated in the present review. The search included articles published from January 2010 to May 2019. The references of all articles were also evaluated. All titles and abstracts were assessed, and only the studies of DWI in patients with HCC were retained. HCC is the most common primitive hepatic cancer. The non-invasive radiological criteria for HCC diagnosis are based on the presence of the specific vascular profile characterized by contrast uptake during arterial phase, defined as arterial hyperenhancement, followed by washout in the venous/portal phase. However, arterial hyperenhancement and wash out appearance have a sensitivity rate of 50-60% in lesion smaller than 2 cm. Therefore, other functional parameters have been introduced in the detection and characterization of HCC nodules. DWI has been applied to liver imaging as an excellent tool for detection and characterization of focal liver lesions, increasing clinical confidence and decreasing false positives. The assessment of DW images can be done qualitatively and quantitatively, through the apparent diffusion coefficient (ADC) map. Intravoxel incoherent motion (IVIM) is a more sophisticated analysis, a biexponential model, to better defining the relationship between signal attenuation and increasing b value that separately reproduces tissue diffusivity and tissue perfusion. Traditionally DWI approach to analyze data is founded on the hypothesis that water molecules diffuse within a voxel following a single direction with a Gaussian behavior without any restriction. However, according to the presence of microstructures, water molecules within biologic tissues exhibits a non-Gaussian phenomena proposed by Jensen in 2005 called Diffusion Kurtosis Imaging (DKI). This approach assesses the kurtosis coefficient (K) that shows the deviance of diffusion from a Gaussian approach, and the diffusion coefficient (D) with the correction of non-Gaussian bias. DKI is an advanced DWI model that quantifies non-Gaussian behavior of diffusion and provides both a corrected ADC, as well as the excess kurtosis of tissue, a measure of the extent to which tissue diffusion deviates from a Gaussian pattern. It is believed that the DKI model is more sensitive to tissue microstructural complexity than standard DW. DWI should be an integral part of study protocol for HCC patients, considering the great advantages due to DWI and DWI-based approaches in detection and characterization of HCC.

Contrast-enhanced ultrasound imaging of the liver: a review of the clinical evidence for SonoVue and Sonazoid.

Contrast-enhanced ultrasound (CEUS) has become an established modality in various clinical indications for liver diseases. SonoVue®, a pure blood pure agent, and Sonazoid®, which exhibits an additional Kupffer phase, are contrast agents approved for liver imaging. This review discusses and compares the current clinical evidence for these two ultrasound contrast agents in the characterization and detection of focal liver lesions in the non-cirrhotic and cirrhotic liver, as well as for the use in interventional procedures such as liver biopsy guidance, and local ablation treatment monitoring. Reference is made to clinical studies which evaluated the accuracy of CEUS using a standard of reference, its safety, or to comparative studies of these two agents.

Application of new ultrasound techniques for focal liver lesions.

Ultrasonography (US) has the overwhelming advantages of not entailing radiation exposure and being a noninvasive, real-time, convenient, easy-to-perform, and relatively inexpensive imaging modality. It is used as the first-line imaging modality for screening, detection, and diagnosis of focal liver lesions (FLLs) [small hepatocellular carcinomas (HCCs), in particular]. However, with the increasing demand for accurate and early diagnosis of small HCCs, newer radiologic methods need to be explored to overcome certain limitations of US. For example, the imaging is easily negatively affected by the presence of gas, rib cage, and subcutaneous fat, and is insensitive for capturing the subtle but vital information on the blood flow. It was in response to this need that new promising technologies such as contrast-enhanced ultrasound and fusion imaging were introduced for the detection of liver lesions. This paper presents an overview of the epidemiology and mechanisms of the development of HCCs, with an emphasis on the application of US in the diagnosis and treatment of FLLs. The aim of this article is to provide the state-of-the-art developments in the imaging diagnosis of FLLs and evaluation of ablation treatment of early HCCs. By keeping abreast of these recent advances, we hope that doctors and researchers working in the field of diagnosis/treatment of liver diseases will be able to discriminate benign FLLs such as regenerative nodules and focal nodular hyperplasia from HCCs, so as to avoid unnecessary repeated tumor biopsies and overtreatment. In particular, we expect that small HCCs or precancerous nodules (such as dysplastic nodules) can be accurately diagnosed and appropriately treated even at an early stage.

Automatic Detection of Focal Liver Lesions in Multi-phase CT Images Using A Multi-channel & Multi-scale CNN.

There are multiple types of tumors occurring in the liver, each of which have a different visual appearance that changes after injection of the contrast medium. Therefore, detection of liver tumors is considered to be a challenging task.In the present article, the authors propose a method for detection of liver tumor candidates with different size in multiphase CT images using a multi-channel and multi-scale CNN protocol. Experimental results demonstrated that recall scores and dice coefficient can be significantly improved, with a reduction in the number of average false positives using the proposed method compared with existing methods.

Role of Computerized Tomographic Findings in Cases of Focal Hepatic Lesions

Introduction: Focal Hepatic Lesions (FHL) has been a common reason for consultation faced by medical consultants. With the widespread use of imaging studies led to an increase in detection of incidental focal liver lesions. It is important to consider both malignant liver lesions as well as benign solid and cystic liver lesions such as hemangioma, focal nodular hyperplasia, hepatocellular adenoma, and hepatic cysts, in the differential diagnosis. Objectives: Our aim was to study the computerized tomographic finding in the various focal hepatic lesions and also to study the various enhancement pattern of the focal hepatic lesion. Materials and Methods: CT scan study of abdomen and pelvis was done on 100 patients from August 2015 to December 2017 over a period of the 2 years. Patients, irrespective of age and sex referred to radiology department of our institute on IPD or OPD basis were included in the study. All patients will be subjected to computerized tomography of the abdomen with CT (Siemens Somatom Emotion 6) machine and Somatom Essenza. Results: Total 100 patients were studied for various focal hepatic lesions out of which 64(64%) were male patients and 36(36%) were female patients. The most common affected age group encountered in our study was 51-60 years. Various focal hepatic lesions were reported in which 64(64%) patient had malignant nature of lesion with most common pathology detected was hepatic metastasis seen in 37(37%) patient. On other hand benign lesion were evident in the 36(36%) patient with hemangioma as most common pathology in this category. Conclusion: Multi-detector Computed Tomography (MDCT) with a correlation of triple phase study is an excellent tool for diagnosis of the focal liver lesion by learning the degree and pattern of enhancement in all three phase thus helping in better characterization of the lesion. MDCT allows the reconstruction and reformation of images supported in detecting multiple lesions and early diagnosis of a focal lesion in the presence diffuse liver condition.