Abdominal Radiology最新文献

Purpose: To determine the diagnostic accuracy of combining CEUS and CT/MRI LI-RADS major imaging features for the improved categorization of liver observations indeterminate on both CT/MRI and CEUS.

Materials and methods: A retrospective analysis using a database from a prospective study conducted at 11 centers in North America and Europe from 2018 to 2022 included a total of 109 participants at risk for HCC who had liver observations with indeterminate characterization (LR3, LR-4, and LR-M) on both CEUS and CT/MRI. The individual CEUS and CT/MRI LI-RADS major features were extracted from the original study and analyzed in various combinations. Reference standards included biopsy, explant histology, and follow-up CT/MRI. The diagnostic performance of the combinations of LI-RADS major features for definitive diagnosis of HCC was calculated. A reverse, stepwise logistical regression sub-analysis was also performed.

Results: This study included 114 observations indeterminate on both CT/MRI and CEUS. These observations were categorized as LR-3 (n = 37), LR-4 (n = 41), and LR-M (n = 36) on CT/MRI and LR-3 (n = 48), LR-4 (n = 36), LR-M (n = 29), and LR-TIV (n = 1) on CEUS. Of them, 43.0% (49/114) were confirmed as HCC, 37.3% (43/114) non-malignant, and 19.3% (22/114) non-hepatocellular malignancies. The highest diagnostic accuracy among the combinations of imaging features was achieved in CT/MRI LR-3 observations, where the combination of CEUS arterial phase hyper-enhancement (APHE) + CT/MRI APHE had 96.7% specificity, 75.0% positive predictive value (PPV), and 86.5% accuracy for HCC.

Conclusion: The combination of LI-RADS major features on CT/MRI and CEUS showed higher specificity, PPV, and accuracy compared to individual modalities' assessments, particularly for CT/MRI LR-3 observations.

The establishment of the Liver Imaging Reporting and Data System (LI-RADS) in 2011 provided a comprehensive approach to standardized imaging, interpretation, and reporting of liver observations in patients diagnosed with or at risk for hepatocellular carcinoma (HCC). Each set of algorithms provides criteria pertinent to the various components of HCC management including surveillance, diagnosis, staging, and treatment response supported by a detailed lexicon of terms applicable to a wide range of liver imaging scenarios. Before its widespread adoption, the variability in the terminology of diagnostic criteria and definitions of imaging features led to significant challenges in patient management and made it difficult to replicate findings or apply them consistently. The integration of LI-RADS into the clinical setting has enhanced the efficiency and clarity of communication between radiologists, referring providers, and patients by employing a uniform language that averts miscommunications. LI-RADS has been strengthened with its integration into the American Association for Study of Liver Diseases practice guidelines. We will provide the background on the initial development of LI-RADS and reasons for development to serve as a starting point for conveying the system's benefits and evolution over the years. We will also suggest strategies for the implementation and maintenance of a LI-RADS program will be discussed.

Hepatocellular carcinoma (HCC), the most common primary liver cancer, is a significant global health burden. Accurate imaging is crucial for diagnosis and treatment response assessment, often eliminating the need for biopsy. The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation and reporting of liver imaging for diagnosis and treatment response assessment, categorizing observations using defined categories that are based on the probability of malignancy or post-treatment tumor viability. Optimized imaging protocols are essential for accurate visualization and characterization of liver findings by LI-RADS. Common technical pitfalls, such as suboptimal postcontrast phase timing, and MRI-specific challenges like subtraction misregistration artifacts, can significantly reduce image quality and diagnostic accuracy. The use of hepatobiliary contrast agents introduces additional challenges including arterial phase degradation and suboptimal uptake in advanced cirrhosis. This review provides radiologists with comprehensive insights into the technical aspects of liver imaging for LI-RADS. We discuss common pitfalls encountered in routine clinical practice and offer practical solutions to optimize imaging techniques. We also highlight technical advances in liver imaging, including multi-arterial MR acquisition and compressed sensing. By understanding and addressing these technical aspects, radiologists can improve accuracy and confidence in the diagnosis and treatment response assessment for hepatocellular carcinoma.

Numerous conditions and pathologies affect the biliary system, many of which have underlying benign courses. However, these overall benign conditions can predispose the patient to malignant pathologies, often due to malignancy arising from abnormal biliary ducts (such as with cholangiocarcinoma) or due to malignancy arising from end-stage liver disease caused by the biliary condition (such as with hepatocellular carcinoma). While these malignancies can at times be obvious, some pathologies can be very difficult to detect and distinguish from the underlying benign biliary etiology. This paper discusses various benign biliary pathologies, with discussion of epidemiology, imaging features, malignant potential, and treatment considerations, with the goal of educating radiologists and referring clinicians to the risk and appearance of hepatobiliary malignancies associated with benign biliary conditions.

Purpose: To evaluate the impact of optimized trigger threshold on 40-keV pancreatic phase images acquired with a dual-energy CT (DECT) protocol.

Methods: A cohort of 69 consecutive participants (median age, 72 years) undergoing a pancreatic protocol DECT examination between September to December 2021 were prospectively randomized into two protocols: conventional trigger threshold of 100 HU (Group A, n = 34) and optimized trigger threshold of 30 HU (Group B, n = 35). Pancreatic phase image acquisition was performed with fixed delay of 20 s from the trigger threshold. Two radiologists assessed the 40-keV pancreatic phase images for scan timing adequacy using a binary scale (adequate or inadequate). The proportions of these classifications were compared in the two groups using the Fisher's test.

Results: The median times to achieve the aortic attenuation of 30 HU and 100 HU were 16.3 s and 22.3 s in Group A, respectively, and was 17.8 s for 30 HU in Group B. The median time difference from 30 HU to 100 HU was 4.5 s in Group A. The scan timing adequacies of pancreatic phase images were classified as adequate (50.0% and 74.3%) or inadequate (50.0% and 25.7%) in Group A and Group B (P = 0.049).

Conclusion: An optimized trigger threshold of 30 HU allows consistent acquisition of adequate pancreatic phase images compared to the conventional trigger threshold of 100 HU for pancreatic protocol DECT at 40-keV which might lead to improved pancreatic lesion conspicuity.

Locoregional treatments (LRT) continue to advance for hepatocellular carcinoma (HCC). Selective internal radiation therapy (SIRT) or transarterial radioembolization (TARE) with radioactive ⁹⁰ Yttrium (Y90) microspheres is currently widely accepted, and external beam and stereotactic body radiation (EBRT/SBRT) are increasingly used as LRT^1-5. Assessment of treatment response after these radiation-based therapies can be challenging, given that the adjacent liver also undergoes treatment related changes, inflammatory changes occur, and there is a variable time for response to develop. In 2017, the liver imaging reporting and data system (LI-RADS) workgroup initially developed a single algorithm for the imaging assessment of treatment response encompassing all types of locoregional therapies, the LI-RADS treatment response (LR-TR) algorithm. Recognizing that response and imaging patterns differ between radiation and non-radiation based therapies, the LR-TR working group recently updated the algorithm to reflect the unique characteristics of tumor response for therapies involving radiation. This article aims to elucidate the changes in the new version of the LI-RADS TR, with a guide for algorithm utilization and illustration of expected and unexpected findings post liver directed therapies for HCC.

Objectives: To explore the potential of CT quantitative parameters in differentiating adrenal lipid-poor adenoma (LPA) from nodular hyperplasia and evaluate diagnostic performance.

Materials and methods: Patients with LPA or nodular hyperplasia who underwent contrast-enhanced CT before adrenalectomy were analyzed retrospectively. The study included 128 patients (83 with LPA and 45 with nodular hyperplasia). Each lesion's unenhanced attenuation, portal-venous phase attenuation (CTp), and the portal-venous phase attenuation of the abdominal aorta were evaluated. We subsequently calculated absolute enhancement [a lesion's portal-venous phase attenuation minus unenhanced attenuation (in HUs)], relative enhancement (absolute enhancement divided by unenhanced attenuation), and the relative enhancement ratio [(absolute enhancement divided by abdominal aorta's portal-venous phase attenuation) ×100%]. Lesion number and size were recorded. Volume was assessed by ITK-snap software and the ratio of lesion volume to ipsilateral adrenal volume (volume ratio) was determined. Intergroup differences were analyzed using Student's t-test and chi-squared test. Logistic regression models were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC), sensitivity, and specificity. The model's performance was then compared against radiologists' subjective assessments, and the inter- and intra-reader agreement values among radiologists were calculated.

Results: Portal-venous phase attenuation, volume ratio, and lesion number were independent predictors of LPA. The logistic regression model incorporating CTp, volume ratio, and lesion number achieved an AUC of 0.835, with a sensitivity of 73.5% and a specificity of 80.0%. The radiologists' diagnostic specificity and accuracy appeared to be inferior to the model. The inter-reader agreement among radiologists ranged from 0.082 to 0.535, and the intra-reader agreement of two radiologists were 0.734 and 0.583.

Conclusion: The portal-venous phase CT demonstrated potential in distinguishing LPA from nodular hyperplasia. The diagnostic performance of the model integrating CTp, volume ratio, and lesion number outperformed radiologists in terms of variability and reproducibility.

Purpose: To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H).

Methods: A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value.

Results: Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model.

Conclusions: The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.

Ultrasound (US) evaluation of the spleen is mandatory in the assessment of patients with chronic liver disease, and splenomegaly can be a sign of systemic diseases. However, due to the lack of distinctive ultrasound findings in specific splenic pathologies, clinical diagnosis can be very challenging. Splenomegaly, defined by increased splenic dimensions, can indicate underlying systemic conditions and is a common manifestation of portal hypertension (PH). Ultrasound and Doppler techniques help assessing splenic involvement in PH. Splenic stiffness measurement, using elastography, offers additional diagnostic accuracy, especially when liver stiffness measurements are inconclusive. CEUS enhances the diagnostic capability for focal splenic lesions, differentiating between benign and malignant lesions by their distinct enhancement patterns, and plays also a critical role in the context of splenic traumatic pathology. Overall, CEUS significantly improves the characterization of splenic pathology, reducing the need for invasive procedures and ensuring appropriate patient management. This review article describes the normal US findings of the spleen and examines the role of multiparametric US in the evaluation of the most common splenic pathologies encountered in the daily clinical practice.