Pál Novák Kaposi

Purpose: Ultrasound fat fraction estimation (USFF) using quantitative ultrasonography (QUS) is a promising method for assessing liver fat in metabolic dysfunction‐associated steatotic liver disease (MASLD). However, its performance across different ethnicities remains unclear. This study aimed to externally validate and compare USFF accuracy in Asian and Caucasian cohorts. Methods: This retrospective analysis used QUS data from three institutions. Participants underwent QUS and magnetic resonance imaging of the proton density fat fraction (MRI‐PDFF), the reference standard. A multivariable regression model was developed using two QUS parameters. Linear regression was used to estimate fat fraction, with adjusted R ² values assessing model fit. The diagnostic performance of USFF for hepatic fat content thresholds (≥ 5%, ≥ 10%, ≥ 20%) was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The development set included 427 South Korean participants, while the validation set comprised 100 from Hungary and 79 from Italy. R ² values were 0.689, 0.702, and 0.485 for the development, internal validation, and external validation sets, respectively. AUC for detecting hepatic steatosis (MRI‐PDFF ≥ 5%) was 0.939 (development) and 0.863 (external validation). Using a cutoff > 0.513, USFF achieved 85.1% sensitivity and 87.4% specificity. Conclusion: USFF is effective but shows variability across populations, highlighting the need for ethnicity‐specific models.

Background: Spectral imaging via photon-counting detector CT (PCD-CT) scanners allows the generation of spectral attenuation curves. Our study aimed to test the feasibility of spectral attenuation curve analysis for differentiating mucinous from non-mucinous cyst fluids of pancreatic cystic lesions (PCL). Methods: Our study included 74 patients with PCLs. The classic morphological features were evaluated by an expert radiologist. The spectral attenuation curves were generated from the pancreatic-phase scans. The average CT values were measured on the 70 keV (HU_70keV) and 40 keV (HU_40keV) virtual monoenergetic images (VMIs), and the differences were calculated (HU_Δ40−70keV). The cases were divided into training (53 mucinous vs. 19 non-mucinous PCLs) and test (10 mucinous vs. 10 non-mucinous PCLs) datasets. The receiver operating characteristic (ROC) curve analysis assessed the discrimination performance. The intra- and interobserver reproducibilities were evaluated by the intraclass correlation coefficient (ICC). Results: On 70 keV VMIs, no significant differences were found between the average CT values of mucinous and non-mucinous PCLs, however, the difference was significant in HU_Δ40−70keV values (p < 0.0001). The diagnostic performance of HU_Δ40−70keV in differentiating between mucinous vs. non-mucinous PCL had AUCs of 0.92 and 0.92 on the training and test datasets, respectively, with a good interobserver (ICC = 0.82) and excellent intraobserver reproducibility (ICC = 0.94). The conventional clinical-radiological model achieved AUCs of 0.85 and 0.86, and with the addition of HU_Δ40−70keV values its performance increased significantly to 0.98 and 0.94 on the training and test datasets, respectively. Conclusions: Spectral attenuation curve assessment of cyst fluids could be a useful additional measurement to facilitate the noninvasive differential diagnosis of PCLs.

Background: Uterine artery embolisation is a recommended method of adenomyosis treatment with good clinical results. Changes in uterine volume and maximal junctional zone thickness (JZmax) after embolisation are thoroughly analyzed in the literature. In contrast changes in other suggested morphological diagnostic markers of adenomyosis (junctional zone differential / JZdiff—and junctional zone ratio / JZratio) are rarely evaluated. This single-centre retrospective study aimed to analyse the changes in morphological parameters used for the MR imaging diagnosis of adenomyosis (including JZdiff and JZratio) after UAE. Clinical effectiveness and safety were also analysed. Materials and methods: Patients who underwent UAE for pure adenomyosis from Jan 2008 to Dec 2021 were evaluated. Adenomyosis was diagnosed based on JZmax, JZdiff, and JZratio measured on MR imaging. To assess clinical efficacy, the numerical-analog-quality-of-life (QoL) score was routinely obtained from patients at our centre. MRI morphological data were analysed. Statistical analysis was conducted using Wilcoxon signed-rank test, uni- and multivariate regression models, Pearson product-moment correlation, and Kruskal–Wallis tests. Results: From our database of 801 patients who underwent UAE between Jan 2008 to Dec 2021, preprocedural MR images were available in 577 cases and, 15 patients had pure adenomyosis (15/577, 2.6%). Uterine volume, JZmax, and JZdiff decreased significantly after UAE; QoL score increased significantly. A significant correlation was found between QoL change vs. JZmax and JZdiff change. Permanent amenorrhoea and elective hysterectomy 5 years after UAE were both 7.1%. Conclusion: Change of JZdiff after UAE in adenomyosis is a potential marker of clinical success. UAE is a clinically safe and effective treatment for adenomyosis. Graphical

Background and Objectives: This study aims to evaluate artificial intelligence-calculated hepatorenal index (AI-HRI) as a diagnostic method for hepatic steatosis. Materials and Methods: We prospectively enrolled 102 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD). All patients had a quantitative ultrasound (QUS), including AI-HRI, ultrasound attenuation coefficient (AC,) and ultrasound backscatter-distribution coefficient (SC) measurements. The ultrasonographic fatty liver indicator (US-FLI) score was also calculated. The magnetic resonance imaging fat fraction (MRI-PDFF) was the reference to classify patients into four grades of steatosis: none < 5%, mild 5–10%, moderate 10–20%, and severe ≥ 20%. We compared AI-HRI between steatosis grades and calculated Spearman’s correlation (r_s) between the methods. We determined the agreement between AI-HRI by two examiners using the intraclass correlation coefficient (ICC) of 68 cases. We performed a receiver operating characteristics (ROC) analysis to estimate the area under the curve (AUC) for AI-HRI. Results: The mean AI-HRI was 2.27 (standard deviation, ±0.96) in the patient cohort. The AI-HRI was significantly different between groups without (1.480 ± 0.607, p < 0.003) and with mild steatosis (2.155 ± 0.776), as well as between mild and moderate steatosis (2.777 ± 0.923, p < 0.018). AI-HRI showed moderate correlation with AC (r_s = 0.597), SC (r_s = 0.473), US-FLI (r_s = 0.5), and MRI-PDFF (r_s = 0.528). The agreement in AI-HRI was good between the two examiners (ICC = 0.635, 95% confidence interval (CI) = 0.411–0.774, p < 0.001). The AI-HRI could detect mild steatosis (AUC = 0.758, 95% CI = 0.621–0.894) with fair and moderate/severe steatosis (AUC = 0.803, 95% CI = 0.721–0.885) with good accuracy. However, the performance of AI-HRI was not significantly different (p < 0.578) between the two diagnostic tasks. Conclusions: AI-HRI is an easy-to-use, reproducible, and accurate QUS method for diagnosing mild and moderate hepatic steatosis.

Microvascular flow imaging (MVFI) is an advanced Doppler ultrasound technique designed to detect slow-velocity blood flow in small-caliber microvessels. This technique is capable of realtime, highly detailed visualization of tumor vessels without using a contrast agent. MVFI has been recently applied for the characterization of focal liver lesions and has revealed typical vascularity distributions in multiple types thereof. Focal nodular hyperplasia (FNH) constitutes an important differential diagnosis of malignant liver tumors. In this essay, we provide iconographic documentation of the MVFI appearance of FNH and other common solid liver lesions. Identifying the typical patterns of vascularity, including the spoke-wheel pattern with MVFI, can expedite the diagnosis, spare patients from unnecessary procedures, and save costs.

The area of Artificial Intelligence is developing at a high rate. In the medical field, an extreme amount of data is created every day. As the images and the reports are quantifiable, the field of radiology aspires to deliver better, more efficient clinical care. Artificial intelligence (AI) means the simulation of human intelligence by a system or machine. It has been developed to enable machines to “think”, which means to be able to learn, reason, predict, categorize, and solve problems concerning high amounts of data and make decisions in a more effective manner than before. Different AI methods can help radiologists with pre-screening images and identifying features. In this review, we summarize the basic concepts which are needed to understand AI. As the AI methods are expected to exceed the threshold for clinical usefulness soon, in the near future it will be inevitable to use AI in medicine.

Splenic hamartoma is a rare benign vascular lesion of the spleen. A splenic mass was incidentally detected in an asymptomatic 65-year-old male during an abdominal ultrasound scan. The workup of the splenic lesion included an abdominal CT angiography (CTA), contrast-enhanced ultrasound (CEUS), and microvascular flow imaging Doppler sonography. The CT scan confirmed that the splenic mass had benign characteristics, and its contrast enhancement was similar to the spleen. The CEUS was able to rule out the possibility of hemangioma. Meanwhile, chronic compression of the celiac trunk and the filling of the splenic artery from collaterals were also detected on CTA. The imaging studies suggested a splenic hamartoma, and the diagnosis was confirmed by the result of a fine needle aspiration biopsy. Splenic hamartoma is a rare benign vascular lesion of the spleen. A splenic mass was incidentally detected in an asymptomatic 65-year-old male during an abdominal ultrasound scan. The workup of the splenic lesion included an abdominal CT angiography (CTA), contrast-enhanced ultrasound (CEUS), and microvascular flow imaging Doppler sonography. The CT scan confirmed that the splenic mass had benign characteristics, and its contrast enhancement was similar to the spleen. The CEUS was able to rule out the possibility of hemangioma. Meanwhile, chronic compression of the celiac trunk and the filling of the splenic artery from collaterals were also detected on CTA. The imaging studies suggested a splenic hamartoma, and the diagnosis was confirmed by the result of a fine needle aspiration biopsy.

We aimed to assess the feasibility of ultrasound-based tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) for quantification of liver steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We prospectively enrolled 101 participants with suspected NAFLD. The TAI and TSI measurements of the liver were performed with a Samsung RS85 Prestige ultrasound system. Based on the magnetic resonance imaging proton density fat fraction (MRI-PDFF), patients were divided into ≤5%, 5–10%, and ≥10% of MRI-PDFF groups. We determined the correlation between TAI, TSI, and MRI-PDFF and used multiple linear regression analysis to identify any association with clinical variables. The diagnostic performance of TAI, TSI was determined based on the area under the receiver operating characteristic curve (AUC). The intraclass correlation coefficient (ICC) was calculated to assess interobserver reliability. Both TAI (r_s = 0.78, P < .001) and TSI (r_s = 0.68, P < .001) showed significant correlation with MRI-PDFF. TAI overperformed TSI in the detection of both ≥5% MRI-PDFF (AUC = 0.89 vs 0.87) and ≥10% (AUC = 0.93 vs 0.86). MRI-PDFF proved to be an independent predictor of TAI (β = 1.03; P < .001), while both MRI-PDFF (β = 50.9; P < .001) and liver stiffness (β = −0.86; P < .001) were independent predictors of TSI. Interobserver analysis showed excellent reproducibility of TAI (ICC = 0.95) and moderate reproducibility of TSI (ICC = 0.73). TAI and TSI could be used successfully to diagnose and estimate the severity of hepatic steatosis in routine clinical practice.

Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to address by assessing multiple DL methods using the Medical Open Network for Artificial Intelligence (MONAI) framework, which may provide clinicians with preliminary information about a given liver lesion. For this purpose, we collected 2274 three-dimensional images of lesions, which we cropped from gadoxetate disodium enhanced T1w, native T1w, and T2w magnetic resonance imaging (MRI) scans. After we performed training and validation using 202 and 65 lesions, we selected the best performing model to predict features of lesions from our in-house test dataset containing 112 lesions. The model (EfficientNetB0) predicted 10 features in the test set with an average area under the receiver operating characteristic curve (standard deviation), sensitivity, specificity, negative predictive value, positive predictive value of 0.84 (0.1), 0.78 (0.14), 0.86 (0.08), 0.89 (0.08) and 0.71 (0.17), respectively. These results suggest that AI methods may assist less experienced residents or radiologists in liver MRI reporting of focal liver lesions.

Artificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.