Prevalence and Risk Factors for Portal Cavernoma in Adult Patients with Portal Vein Thrombosis.

Portal vein thrombosis (PVT) represents a restriction or occlusion of the portal vein by a blood clot, which can appear in liver cirrhosis, inherited or acquired thrombophilia, malignancies, abdominal infection, abdominal inflammation, and injury to the portal vein; it can evolve to local venous extension, recanalization, or portal cavernoma (PC). This research represents an observational study of patients admitted with a diagnosis of PVT between January 2018 and December 2022. We assessed the rate of and risk factors for PC. In total, 189 patients with PVT were included; the rate of PC was 14.8%. In univariate and multivariate analysis, the main risk factors for the presence of PC were etiology (thrombophilia, myeloproliferative disorders, local inflammatory diseases, and idiopathic causes), prior PVT, and complete versus incomplete or single-branch portal obstruction. In patients with superior mesenteric vein (SMV) thrombosis, distal obstruction was more prone to PC than proximal obstruction. The main predictive factors were etiology, prior PVT, complete PVT obstruction, and no prior non-selective beta-blocker (NSBB) use; in patients with SMV thrombosis, the distal extension was more significantly associated with the risk of PC. We propose a composite score for the prediction of PC which includes etiology, prior diagnosis of PVT, prior NSBB use, complete versus incomplete PVT, and distal versus proximal SMV thrombosis, with good accuracy (AUC 0.822) and an estimated sensitivity of 76.92% and specificity of 82.39% at a cut-off value of 4.

The Staging of Newly Diagnosed Hepatocellular Carcinoma in Romania. A National Multicentric Study.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a significant public health issue, with an increasing incidence and prevalence and a high incidence-to-mortality ratio. The prognosis of HCC depends on two competing factors, tumor burden and underlying liver disease severity, encompassed in the Barcelona Clinic Liver Cancer (BCLC) classification. To assess HCC staging and the way staging affects eligibility for treatment at the time of the first diagnosis in Romania in the setting of opportunistic diagnosis, in the absence of a national HCC screening policy. METHODS: Data regarding HCC staging, underlying liver disease, and eligibility for treatment at the time of diagnosis was analyzed using a prospectively maintained multicentric database, which included patients from the five largest tertiary care hepatology units in the country between June 2016 and February 2020. RESULTS: A consecutive series of 477 patients was included. The distribution within BCLC classes was as follows: very early (0) 7.1%, early (A) 34.3%, intermediate (B) 19.4%, advanced (C) 14.2%, terminal (D) 24.7%. At the time of the diagnosis, 198 (41.5%) were eligible for a curative intent treatment, while 359 (75.2%) were eligible for a disease-modifying therapy. 228 patients (47.8%) had decompensated liver disease at the time of diagnosis, the most common decompensating event being ascites (78.1%). CONCLUSIONS: A large proportion of HCC cases are diagnosed at the time of a decompensating event, severely restricting the therapeutic potential. Proactive diagnostic strategies should be implemented to improve the rate of actionable diagnosis.

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.

The Clinical Value of Multimodal Ultrasound for the Differential Diagnosis of Hepatocellular Carcinoma from Other Liver Tumors in Relation to Histopathology.

Recent advances in the field of ultrasonography offer promising tools for the evaluation of liver tumors. We aim to assess the value of multimodal ultrasound in differentiating hepatocellular carcinomas (HCCs) from other liver lesions. We prospectively included 66 patients with 72 liver tumors. The histological analysis was the reference standard for the diagnosis of malignant liver lesions, and partially for benign tumors. All liver lesions were assessed by multiparametric ultrasound: standard ultrasound, contrast-enhanced ultrasound (CEUS), the point shear wave elastography (pSWE) using shear wave measurement (SWM) method and real-time tissue elastography (RTE). To diagnose HCCs, CEUS achieved a sensitivity, specificity, accuracy and positive predictive value (PPV) of 69.05%, 92.86%, 78.57% and 93.55%, respectively. The mean shear-wave velocity (Vs) value in HCCs was 1.59 ± 0.29 m/s, which was lower than non-HCC malignancies (p < 0.05). Using a cut-off value of 1.58 m/s, SWM achieved a sensitivity of 54.76%, and 82.35% specificity, for differentiating HCCs from other malignant lesions. The combination of SWM and CEUS showed higher sensitivity (79.55%) compared with each technique alone, while maintaining a high specificity (89.29%). In RTE, most HCCs (61.53%) had a mosaic pattern with dominant blue areas corresponding to type "c" elasticity. Elasticity type "c" was 70.59% predictive for HCCs. In conclusion, combining B-mode ultrasound, CEUS, pSWE and RTE can provide complementary diagnostic information and potentially decrease the requirements for other imaging modalities.

An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software, and then the signal can be analyzed for further investigations. METHODS: The purpose of the study was to build an automated method for extracting TICs and classifying liver lesions in CEUS liver investigations. The cohort contained 50 anonymized video investigations from 49 patients. Besides the CEUS investigations, clinical data from the patients were provided. A method comprising three modules was proposed. The first module, a lesion segmentation deep learning (DL) model, handled the prediction of masks frame-by-frame (region of interest). The second module performed dilation on the mask, and after applying colormap to the image, it extracted the TIC and the parameters from the TIC (area under the curve, time to peak, mean transit time, and maximum intensity). The third module, a feed-forward neural network, predicted the final diagnosis. It was trained on the TIC parameters extracted by the second model, together with other data: gender, age, hepatitis history, and cirrhosis history. RESULTS: For the feed-forward classifier, five classes were chosen: hepatocarcinoma, metastasis, other malignant lesions, hemangioma, and other benign lesions. Being a multiclass classifier, appropriate performance metrics were observed: categorical accuracy, F1 micro, F1 macro, and Matthews correlation coefficient. The results showed that due to class imbalance, in some cases, the classifier was not able to predict with high accuracy a specific lesion from the minority classes. However, on the majority classes, the classifier can predict the lesion type with high accuracy. CONCLUSIONS: The main goal of the study was to develop an automated method of classifying liver lesions in CEUS video investigations. Being modular, the system can be a useful tool for gastroenterologists or medical students: either as a second opinion system or a tool to automatically extract TICs.

Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations.

BACKGROUND: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. METHODS: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. RESULTS: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. CONCLUSIONS: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations.

Hepatocellular Carcinoma with Gastrointestinal Involvement: A Systematic Review.

In this paper, we aimed to evaluate clinical and imagistic features, and also to provide a diagnostic algorithm for patients presenting with gastrointestinal involvement from hepatocellular carcinoma (HCC). We conducted a systematic search on the PubMed, Scopus and Web of Science databases to identify and collect papers oncases of HCC with gastrointestinal involvement. This search was last updated on 29 April 2022. One hundred and twenty-three articles were included, corresponding to 197 patients. The majority of the patients were male (87.30%), with a mean age of 61.21 years old. The analysis showed large HCCs located mainly in the right hepatic lobe, and highly elevated alfa-fetoprotein (mean = 15,366.18 ng/mL). The most frequent etiological factor was hepatitis B virus (38.57%). Portal vein thrombosis was present in 27.91% of cases. HCC was previously treated in most cases by transarterial chemoembolization (32.99%) and surgical resection (28.93%). Gastrointestinal lesions, developed mainly through direct invasion and hematogenous routes, were predominantly detected in the stomach and duodenum in equal measure-27.91%. Gastrointestinal bleeding was the most common presentation (49.74%). The main diagnostic tools were esophagogastroduodenoscopy (EGD) and computed tomography. The mean survival time was 7.30 months. Gastrointestinal involvement in HCC should be included in the differential diagnosis of patients with underlying HCC and gastrointestinal manifestations or pathological findings in EGD.

Role of Contrast-Enhanced Ultrasonography in Hepatocellular Carcinoma by Using LI-RADS and Ancillary Features: A Single Tertiary Centre Experience.

Clinical utility of ancillary features (AFs) in contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS®) is yet to be established. In this study, we assessed the diagnostic yield of CEUS LI-RADS and AFs in hepatocellular carcinoma (HCC). We retrospectively included patients with risk factors for HCC and newly diagnosed focal liver lesions (FLL). All lesions have been categorized according to the CEUS LI-RADS v2017 by an experienced sonographer blinded to clinical data and to the final diagnosis. From a total of 143 patients with 191 FLL, AFs favoring HCC were observed in 19.8% cases as hypoechoic rim and in 16.7% cases as nodule-in nodule architecture. From the total of 141 HCC cases, 83.6% were correctly classified: 57.4%- LR-5 and 26.2%- LR-4. In 9.21% cases, CEUS indicated LR-M; 2.12% cases- LR-3. The LR-5 category was 96.2% predictive (PPV) of HCC. LR-5 had 60.4% sensitivity and 93.6% specificity. PPV for primitive malignancy (LR-4 + LR-5) was 95.7%, with 88% sensitivity, 89.3% specificity and 88.4% accuracy for HCC. LR-4 category had 94.8% PPV and 26.2% sensitivity. CEUS LR4 + LR5 had 81,8% sensitivity for HCCs over 2 cm and 78.57% sensitivity for smaller HCCs. CEUS LR-5 remains an excellent diagnostic tool for HCC, despite the size of the lesion. The use of AFs might improve the overarching goal of LR-5 + LR-4 diagnosis of high specificity for HCC and exclusion of non-HCC malignancy.

Evaluation of Hepatocellular Carcinoma Patients: Interim Analysis of Cases from a Tertiary Referral Center in Craiova, Romania.

Although medicine is constantly evolving, hepatocellular carcinoma remains a pathology with a poor prognosis due to the frequent delayed diagnosis and the aggressiveness of the disease. AIM: Our objective was to evaluate liver function and stage of disease of newly diagnosed HCC patients. METHODS: We conducted a retrospective study between July 2016 and January 2021 and we included hospitalized patients within the Department of Gastroenterology of the Emergency County Hospital of Craiova. We identified 119 newly diagnosed patients and we collected data from patient history, contrast-enhanced imaging and laboratory analysis. RESULTS: 81 patients were diagnosed in BCLC Stage A and B. Liver function was not significantly modified, despite 91.5% of the patients presented with elevated AST levels. Because of the cirrhotic liver already affected, 73 patients had thrombocytopenia. Contrast-enhanced ultrasound was performed in 79 patients, as a complementary imaging exploration. Alfa-fetoprotein values could not be correlated with the severity of disease. CONCLUSIONS: Early diagnosis was mostly established. It is mandatory for treatment management and overall survival to follow a rigorous surveillance of patients at risk for HCC.

One stop shop approach for the diagnosis of liver hemangioma.

Hepatic hemangioma is usually detected on a routine ultrasound examination because of silent clinical behaviour. The typical ultrasound appearance of hemangioma is easily recognizable and quickly guides the diagnosis without the need for further investigation. But there is also an entire spectrum of atypical and uncommon ultrasound features and our review comes to detail these particular aspects. An atypical aspect in standard ultrasound leads to the continuation of explorations with an imaging investigation with contrast substance [ultrasound/ computed tomography/or magnetic resonance imaging (MRI)]. For a clinician who practices ultrasound and has an ultrasound system in the room, the easiest, fastest, non-invasive and cost-effective method is contrast enhanced ultrasound (CEUS). Approximately 85% of patients are correctly diagnosed with this method and the patient has the correct diagnosis in about 30 min without fear of malignancy and without waiting for a computer tomography (CT)/MRI appointment. In less than 15% of patients CEUS does not provide a conclusive appearance; thus, CT scan or MRI becomes mandatory and liver biopsy is rarely required. The aim of this updated review is to synthesize the typical and atypical ultrasound aspects of hepatic hemangioma in the adult patient and to propose a fast, non-invasive and cost-effective clinical-ultrasound algorithm for the diagnosis of hepatic hemangioma.

Metastases to the Kidney: A Case Report and Review of the Literature.

Renal metastases are uncommon in clinical practice, even as autopsy reports much frequent cases în disseminated tumors. Usually multiple and bilateral, they can determine many problems of differential diagnosis in case of solitary renal mass, when a primary kidney neoplasm must be excluded. Main sources are represented by the tumors of the lung, breast, digestive tract, melanomas and lymphomas, but rare cases with other etiology have been reported. Imaging can help to the diagnosis; CT scan, MRI, transabdominal ultrasound and sometimes contrast enhanced ultrasound can be useful. The treatment is individualized by the general status, by other organs involved and by the control of primary tumors; nephrectomy can be made in cases with unsure diagnosis and if primary tumor is controlled.