Diagnosis Of Liver Lesions Research Articles

Hepatic Inflammatory Pseudotumor on 18F-FDG and 18F-FAP-42 PET/CT.

Hepatic inflammatory pseudotumor is a rare benign disease. We describe the 18F-FDG and 18F-FAPI-42 PET/CT findings of hepatic inflammatory pseudotumor in a 57-year-old man. Compared with 18F-FDG, 18F-FAPI-42 PET/CT identified more lesions and showed more intense uptake within the lesions. This case illustrates that hepatic inflammatory pseudotumor should be contemplated in the differential diagnosis of liver lesions with increased 18F-FDG and 18F-FAPI-42 uptake. 18F-FAPI-42 PET/CT might be used as a helpful tool for evaluating hepatic inflammatory pseudotumor.

Focal liver lesion diagnosis with deep learning and multistage CT imaging

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning

BackgroundContrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help improve the constrains, this study aims to develop an end-to-end method based on deep learning to make malignancy diagnosis of liver lesions using CEUS.MethodsA total of 420 focal liver lesions with 136 benign cases and 284 malignant cases were included. A deep learning model based on a two-dimensional convolution neural network, a long short-term memory (LSTM), and a linear classifier (with sigmoid) was developed to analyze the CEUS loops from different contrast imaging phases. For comparison, a 3D-CNN based method and a machine-learning (ML)-based time-intensity curve (TIC) method were also implemented for performance evaluation.ResultsResults of the 4-fold validation demonstrate that the mean AUC is 0.91, 0.88, and 0.78 for the proposed method, the 3D-CNN based method, and the ML-based TIC method, respectively.ConclusionsThe proposed CNN-LSTM method is promising in making malignancy diagnosis of liver lesions in CEUS without any additional manual features selection.

Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.

Improving liver lesions classification on CT/MRI images based on Hounsfield Units attenuation and deep learning

The early sign detection of liver lesions plays an extremely important role in preventing, diagnosing, and treating liver diseases. In fact, radiologists mainly consider Hounsfield Units to locate liver lesions. However, most studies focus on the analysis of unenhanced computed tomography images without considering an attenuation difference between Hounsfield Units before and after contrast injection. Therefore, the purpose of this work is to develop an improved method for the automatic detection and classification of common liver lesions based on deep learning techniques and the variations of the Hounsfield Units density on computed tomography scans. We design and implement a multi-phase classification model developed on the Faster Region-based Convolutional Neural Networks (Faster R–CNN), Region-based Fully Convolutional Networks (R–FCN), and Single Shot Detector Networks (SSD) with the transfer learning approach. The model considers the variations of the Hounsfield Unit density on computed tomography scans in four phases before and after contrast injection (plain, arterial, venous, and delay). The experiments are conducted on three common types of liver lesions including liver cysts, hemangiomas, and hepatocellular carcinoma. Experimental results show that the proposed method accurately locates and classifies common liver lesions. The liver lesions detection with Hounsfield Units gives high accuracy of 100%. Meanwhile, the lesion classification achieves an accuracy of 95.1%. The promising results show the applicability of the proposed method for automatic liver lesions detection and classification. The proposed method improves the accuracy of liver lesions detection and classification compared with some preceding methods. It is useful for practical systems to assist doctors in the diagnosis of liver lesions. In our further research, an improvement can be made with big data analysis to build real-time processing systems and we expand this study to detect lesions from all parts of the human body, not just the liver.

Contrast phase recognition in liver computer tomography using deep learning

Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of São Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively.

S3327 Angiosarcoma Presenting as Hemorrhagic Shock: A Rare Diagnosis of Liver Lesions in a Young Patient

Introduction: Angiosarcoma is an aggressive tumor and rare in young healthy patients. Angiosarcoma is associated with exposure to arsenic, vinyl chloride, and oral contraceptives1. This is an unusual case of angiosarcoma in a young patient presenting with an acute abdomen from hemorrhagic shock. Angiosarcoma has poor prognosis and even removal and transplantation of the Liver is no sufficient 1. It is important to include angiosarcoma in the differential for patients presenting with acute abdomen and explore aggressive medical therapy along with surgical resection. Case Description/Methods: A 19-year-old male with no past medical history presented with sudden severe diffuse abdominal pain, difficulty breathing, nausea, vomiting, and diarrhea. FAST exam showed large volume of free fluid in the abdomen. Initial labs showed elevated liver associated enzymes, Hgb 5.4, lactic acidosis, fibrinogen 171. CT of abdomen and pelvis showed hepatomegaly with multiple small hypervascular lesions in liver [Figure 1]. Massive transfusion protocol was initiated, and he subsequently required emergent exploratory laparotomy with 3L of old-appearing blood drained. Intraoperatively, the liver was found to be multinodular, woody, enlarged, and cirrhotic with fresh heme. Final surgical pathology showed angiosarcoma with atypia over epithelioid hemangioendothelioma. Patient continued losing blood and required a hepatic angiogram with embolization. He continued to worsen, developing acute liver failure. Patient continued to decompensate and eventually required palliative care. (Figure) Discussion: Angiosarcoma often presents in elderly patients with nonspecific symptoms such as abdominal distention, abdominal discomfort, weight loss, and fatigue2,3,4. Differentiating liver tumors such as hepatoma, adenoma, or vascular malformations on imaging is challenging and surgical resection is essential. Average survival of patients with untreated liver angiosarcoma is approximately 6 months and it increases by 2 years with treatment2,3,4. The standard treatment for liver angiosarcoma is surgical resection. Liver transplant is not indicated because of the high recurrence rate and poor prognosis2,3,4. There are no standardized treatments for patients presenting with hemorrhagic shock besides embolization and resection. More treatments need to be explored for aggressive treatment for hepatic angiosarcoma, as this case highlights a healthy 19-year-old patient dying one month after initial diagnosis with limited options for therapeutic intervention.Figure 1.: CT Abdomen and Pelvis with multiple hypervascular lesions.

Benign focal liver lesions: The role of magnetic resonance imaging.

Liver lesions are common findings in radiologists’ daily routine. They are a complex category of pathology that range from solitary benign lesions to primary liver cancer and liver metastases. Benign focal liver lesions can arise from different liver cell types: Epithelial (hepatocytes and biliary cells) and nonepithelial (mesenchymal cells). Liver magnetic resonance imaging (MRI) is a fundamental radiological method in these patients as it allows with its multiparametric approach optimal non-invasive tissue characterization. Furthermore, advanced liver MRI techniques such as diffusion-weighted imaging and hepatobiliary contrast agents have improved the detection of focal liver lesions and can be highly effective in differentiating pseudotumor from tumors, as well as benign from malignant lesions, and can also be used for differential diagnosis. Although histological examination can be useful in making a definitive diagnosis, MRI is an important modality in the diagnosis of liver lesions with a significant impact on patient care. This aim of this review is to provide a comprehensive overview of benign liver lesions on MRI.

Cytomorphological Study of Hepatic Lesions by FNAC

Introduction: Fine Needle Aspiration Cytology (FNAC) is a simple, safe, very low-cost and rapid procedure for diagnostic purpose which makes surgical intervention and exploratory laparotomy unnecessary. The introduction of modern diagnostic imaging techniques, mainly ultrasonography (USG) and Computerized Tomography (CT), has enabled the detection and location of lesions in sites such as liver, which are not easily accessible to surgical biopsies.
 Aim: To assess the utility of FNAC in the diagnosis of liver lesions. To study the cytomorphological features, age and sex distribution of liver lesions.
 Materials and methods: The study included 24 liver lesions which were detected clinically or radiologically. USG guided FNA was done in all cases. The smears were stained with Haematoxylin and eosin (H and E), May Grunwald Giemsa (MGG) and Papanicolaou’s stains. A cyto-histopathological correlation was done wherever possible. From the residual material, additional slides and cell blocks were prepared. Special stains were used whenever required. In doubtful cases immunohistochemistry (IHC) was done on formalin-fixed paraffin cell blocks and immunocytochemistry on conventional smears wherever necessary and possible.
 Results: All the lesions were found to be malignant. Out of 24 liver FNA cases, maximum lesions were seen in females and the affected age group for liver malignancies was 51-60 years. Out of 24 liver FNA cases, 20 cases were metastatic and 4 were primary cases. Among metastatic cases, maximum cases were of metastatic adenocarcinoma and the primary lesions of liver included all cases of hepatocellular carcinoma. Conclusion: Fine needle aspiration cytology of liver is a simple, safe, repeatable and rapid diagnostic procedure. In case of hepatic lesions, it is of outmost advantage in recognizing the nature of lesion i.e., primary, and metastatic as well as in early diagnosis of lesion so that patient can be treated as early as possible.

Reliability of combined fine needle aspiration and core needle biopsies in the diagnosis of liver lesions: An 8-year institutional experience.

Fine needle aspiration (FNA), followed by core needle biopsy (CNB) when needed, was adopted as the standard care for liver lesions in our institution. This study explores the diagnostic efficacy of combined image-guided FNA and CNB in liver lesion diagnosis. We retrospectively reviewed all liver FNA cases performed in our institution between January 2010 and September 2018. A total of 550 cases from 531 patients (173 females) with a median age of 59years (range, 13-90) were identified. All FNA cases were initially assessed with rapid on-site evaluation, and cell blocks were prepared. A total of 459 FNA specimens with concurrent CNBs were included in the study. Both FNAs and CNBs in the paired sampling were read by a cytopathologist, with expert consultation as needed. The concordance rate between FNA and CNB was 85.2%. Combined FNA/CNB showed higher sensitivity in detecting malignant tumours when compared to FNA or CNB alone (98%, vs 87% and 92%, p<0.001), especially for detecting metastatic tumours, hepatocellular carcinoma, and haematopoietic neoplasms (98%, 97%, and 94%, respectively; all p<0.001). Combined FNA/CNB showed a lower false negative rate in malignant tumours than FNA or CNB alone (2%, vs 13% and 8%, p<0.001). There was no significant difference among FNA, CNB, and combined FNA/CNB in diagnosing benign liver lesions. Combined liver FNA/CNB has high diagnostic efficacy for malignancy and a lower false negative rate than either procedure alone, especially in metastatic tumours, hepatocellular carcinoma, and haematopoietic neoplasms.

Incidental liver lesions on baseline breast MRI: Outcomes on subsequent abdominal imaging

PurposeTo examine outcomes of incidental liver lesions on baseline breast magnetic resonance imaging (MRI) that were further evaluated with dedicated abdominal imaging. MethodsConsecutive breast MRI reports from 2011 to 2016 were retrospectively reviewed to identify incidental liver lesions. Only patients without prior breast MRI, without prior abdominal imaging, and with subsequent abdominal imaging were included. Patient demographics, breast MRI indication, and final liver lesion diagnosis were recorded. ResultsOf 131 women (mean age 53.8 years), 94/131 (71.8%) underwent breast MRI for extent of disease evaluation, 25/131 (19.1%) for high-risk screening, 11/131 (8.4%) for implant evaluation, and 1/131 (0.8%) for problem-solving. Of 131 liver lesions (6–80 mm), 117/131 (89.3%) were deemed benign on subsequent abdominal imaging; 10/131 (7.6%) probably benign; and 4/131 lesions (3.1%) were confirmed breast cancer metastases. Metastatic liver lesions identified on breast MRI were more likely for women with a current diagnosis of breast cancer than for women without a current diagnosis of breast cancer: 4.3% vs 0%. Similarly, metastatic liver lesions identified on breast MRI were more likely for those with a higher prognostic stage (2 or 3) vs a lower prognostic stage (0 or 1) or no current breast cancer: 11.1% vs 0%. ConclusionBaseline breast MRIs showing incidental liver lesions showed unsuspected liver metastases only in women with a current diagnosis of clinical stage 2 or 3 breast cancer. This suggests breast MRI indication and clinical staging of current breast cancer, if present, can help plan management and decisions to obtain follow-up of liver lesions.

Malignant Tumors Misdiagnosed as Liver Hemangiomas.

Background and Aim: To derive lessons from the data of patients who were followed for various periods with the misdiagnosis of liver hemangioma and eventually found to have a malignancy.Material and Methods: The records of 23 patients treated between 2003 and 2018 were analyzed retrospectively.Results: Twelve patients were men and 11 were women; median (range) age was 55 (35–80). The principal diagnostic modality for the initial diagnosis was ultrasonography (n:8), magnetic resonance imaging (MRI) (n:13), and computed tomography (CT) (n:2). At our institution, MRI was performed in 16 patients; the diagnosis was made with the available MRI and CT studies in five and two patients, respectively. In other words, the ultrasonography interpretations were not confirmed on MRI; in others, the MRI or CT examinations were of low quality or they had not been interpreted properly. Fifteen patients underwent surgery; the other patients received chemotherapy (n:6) or chemoembolization (n:2). The misdiagnosis caused a median (range) 10 (0–96) months delay in treatment. The final diagnoses were hepatocellular carcinoma in 12 patients, cholangiocarcinoma in four patients, metastatic mesenchymal tumor, metastasis of colon cancer, metastatic neuroendocrine carcinoma, sarcomatoid hepatocellular carcinoma, angiosarcoma, thoracic wall tumor, and metastatic tumor of unknown primary in one patient each.Conclusions: High-quality MRI with proper interpretation and judicious follow up are vital for the accurate differential diagnosis of liver lesions.

A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation.

Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for liver lesion diagnosis on magnetic resonance images, termed as MMF-CNN. We introduce a multi-scale representation strategy to encode both the local and semi-local complementary information of the images. To take advantage of the complementary information of multi-scale representations, we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning. We further explore the explanation of the diagnosis decision of the deep neural network through visualizing the areas of interest of the network. A new scoring method is designed to evaluate whether the attention maps can highlight the relevant radiological features. The explanation and visualization make the decision-making process of the deep neural network transparent for the clinicians. We apply our proposed approach to various state-of-the-art deep learning architectures. The experimental results demonstrate the effectiveness of our approach.

An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks

Liver cancer is one of the major causes of death by cancer. The early detection of lesions in the liver provides a better chance of treatment and cure of the disease. Computed tomography (CT) is one of the most used imaging techniques for the detection and diagnosis of liver lesions. However, the manual segmentation of liver and tumors, aside from being time-consuming, can still cause errors and may vary among specialists. Because of this hard work, computer-aided detection (CAD) and computer-aided diagnosis (CADx) systems have been developed to assist specialists in the detection and characterization of lesions in the liver and reduce the required time for diagnosis. The automatic segmentation of these lesions is a complex task since they present variability in contrast, shape, size, and location. In this work, a method to automatically segment liver lesions in CT images is proposed. The proposed method, which presents two deep convolutional neural networks (CNN) models, consists of five main steps: (1) image acquisition, (2) image pre-processing, (3) initial segmentation using RetinaNet, (4) lesion segmentation using U-Net, and (5) segmentation refinement. The proposed method was evaluated using a set of 131 CT images from the LiTS dataset, and the best result obtained a matthews correlation coefficient (MCC) of 83.62%, a sensitivity of 83.86%, a specificity of 99.96%, a Dice coefficient of 82.99%, a volumetric overlap error (VOE) of 27.89%, and a relative volume difference (RVD) of 1.69%. We show in our method that the problem of segmentation of liver lesions in CT images can be efficiently solved through the use of deep CNNs to define the scope of the problem and to precisely segment lesions.

Role of MRI in Evaluation of Focal Liver Lesions: A Cross-sectional Study

Introduction: The non-invasive diagnosis of liver lesions is usually achieved with contrast material-enhanced computed tomography and Magnetic Resonance (MR) imaging. Dynamic three dimensional gradient-recalled-echo MR imaging provides dynamic contrast-enhanced thin-section images with fat saturation and a high signal-to-noise ratio and is excellent for the evaluation of various focal hepatic lesions. Aim: To explore the effectiveness and hence the clinical utility of plain and contrast MR characterisation of focal hepatic lesions in patients and their correlation with the histopathological findings. Materials and Methods: The present study was a multicenteric study done on both Inpatients and Outpatients of Department of Radiology of Tertiary care hospitals in Bengaluru, from January 2020 to August 2020. A total of 42 patients diagnosed with focal liver lesions by Ultrasonography were included in this study. Plain MRI study was done by using spin echo technique and contrast MRI study was done after rapid bolus injection (0.1 mmol/kg body weight) of gadolinium dimeglumine. Pathological and post-operative histopathological findings were noted. Microsoft excel software was used for data analysis and tables were prepared for comparison of collected data. Specificity and sensitivity were calculated for diagnostic accuracy of MRI. Results: This study included forty-two patients in total, twenty-five male and seventeen female patients, in age group of 15 to 84 years with a mean age of 48 years. Metastasis and Hepatocellular Carcinoma (HCC) were most common malignant lesions in liver while Hemangiomas were most common benign lesions followed by simple and complex cysts and hepatic adenoma. The present study calculated the specificity and sensitivity of MRI of focal hepatic lesions and correlated the findings clinically, pathologically and histopathologically. The specificity of MRI for characterisation of focal liver disease was high (90.4%). Specificity of MRI for HCC was found to be 96.7 % and that for metastasis was 97% while sensitivity for Haemangiomas is found to be 100%.MRI is superior compared to other modalities like ultrasound and CT in having high spatial resolution. Multiple sequences like T1, T2 and fat suppression techniques like IN PHASE, OUT OF PHASE helps in demonstrating lesion characters like architecture, margin, haemorrhage, fat component and vascular invasion of the lesions. Conclusion: MRI is valuable for the characterization of focal liver lesions detected on Ultrasonography. Basic MRI sequences along with chemical shift imaging sequences and dynamic contrast imaging has significant role in characterising the focal liver lesions. Despite that, It should be the modality choice for the lesions which are undetected or doubtful on ultrasound and CT examinations.

Performance analysis of pre-processing filters using computed tomography images for liver lesion diagnosis

Performance analysis of pre-processing filters using computed tomography images for liver lesion diagnosis

Performance analysis of pre-processing filters using computed tomography images for liver lesion diagnosis

Performance analysis of pre-processing filters using computed tomography images for liver lesion diagnosis

Ultrasound/computerized tomography guided fine needle aspiration cytology of liver lesions

Introduction: Malignancy in the liver, primary or metastatic, is usually inoperable at the time of diagnosis and as such, portends an ominous prognosis. A diagnostic modality such as FNAC, which offers accuracy with minimal complications and requires minimal intervention at low cost, warrants consideration early in the investigation sequence. Objectives: The study has been undertaken to evaluate the diagnostic efficacy of ultrasonography (USG)/ computerized tomography (CT) guided FNAC in the diagnosis of liver lesions, to correlate FNAC diagnosis with histopathology wherever possible, to correlate FNAC diagnosis with radiology diagnosis and to study the cytological patterns of liver lesions. Materials and Methods: A prospective study of 60 patients was conducted between November 2008 and October 2010. After obtaining the detailed clinical and radiological data, patients were subjected for FNAC under USG or CT guidance. Results: Cytodiagnosis of 60 cases were categorized into 6 (10%) Non-neoplastic lesions, 53 (88.33%) malignant neoplastic lesions and 1 (1.66%) as suspicious of carcinoma. The different neoplastic lesions were 21 (39.62%) hepatocellular carcinoma, 21 (39.62%) metastatic adenocarcinoma, 6 (11.32%) metastatic poorly differentiated carcinoma and 5cases (9.43%) of unclassified malignancy. Histopathological correlation was available in 11 malignant neoplastic lesions which confirmed the diagnosis. Nineteen cases of multiple lesions described by ultrasonography and suggested differential diagnosis of metastasis and HCC proved to be metastatic in 10cases (52.63%) and HCC in 9 cases (47.36%) by cytological examination. Overall diagnostic accuracy of the FNAC of liver to detect malignant lesions was 93.75%. Conclusion: USG/CT guided FNAC of liver permits the categorization of more frequent non-neoplastic lesions and neoplastic primary and secondary metastatic malignancy in a simple and rational manner which is helpful for the management of hepatic les

The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions.

BackgroundThe present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions.MethodsIn this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0–600 s/mm2), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves.ResultsThere were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29.ConclusionsA multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.

Frequency of Different Types of Focal Hepatic Lesions on Triphasic Computed Tomography Scan in Adult Patients

Out of various pathologies that affect the liver, focal liver lesion form an important group. Triphasic computed tomography is the imaging modality most often used to evaluate focal liver lesions. The purpose of this study was to evaluate the role of computed tomography in the diagnosis of liver lesions and to determine frequency of these lesions. Keywords : Liver, focal lesion, liver parenchyma, enhancement, iso-dense, hypo-dense and hyper-dense DOI : 10.7176/JHMN/77-04 Publication date :July 31st 2020