Abdominal Radiology最新文献

This comprehensive review examines recent advancements in the integration of multiparametric ultrasound for diagnostic imaging of the urinary bladder. It not only highlights the current state of ultrasound imaging but also projects its potential to further elevate standards of care in managing urinary bladder pathologies. Specifically, contrast-enhanced ultrasound (CEUS) and elastography show significant improvements in detecting bladder tumors and assessing bladder wall mechanics compared to traditional methods. The review also explores the future potential of ultrasound-mediated nanobubble destruction (UMND) as an investigational targeted cancer therapy, showcasing a novel approach that utilizes nanobubbles to deliver therapeutic genes into tumor cells with high precision. Emerging AI-driven innovations and novel techniques, such as microvascular ultrasonography (MVUS), are proving to be powerful tools for the non-invasive and precise management of bladder conditions, offering detailed insights into bladder structure and function. These advancements collectively underscore their transformative impact on the field of urology.

Objective: This study aimed to compare transarterial chemoembolization (TACE) and transarterial radioembolization (TARE) as first-line treatments for unresectable HCC > 8 cm.

Methods: This retrospective study analyzed 129 HCC patients with tumor diameters greater than 8 cm from January 2010 to December 2021, including 40 patients who received TARE, and 89 patients treated with TACE as primary treatment. Following Propensity Score Matching (PSM), 40 patients from each group were harmonized for baseline characteristics. Tumor responses were evaluated using mRECIST criteria, and survival outcomes were compared between treatment groups using Kaplan-Meier curves and the Log-rank test.

Results: There was no significant difference in the objective response rate (ORR) and disease control rate (DCR) at 3, 6, and 12 months between the two groups; ORR and DCR were 72.6%, 83.1% in TACE group vs. 72.5%. 87.5% in TARE group for best tumor response (p-values: 0.625 and 0.981, respectively). Overall survival (OS) and progression-free survival (PFS) between the two groups were comparable pre- and post-PSM. After PSM, the OS was 33.2 months (20.0-58.6) in TACE group and 38.1 months (13.8-98.1) in TARE group (p = 0.53), while PFS was 11.5 months (7.7-18.4) and 9.1 months (5.2-23.8) respectively. After PSM, post-embolization syndrome developed more in TACE group (100% vs. 75%, p = 0.002). Major adverse events were 72% in TACE group vs. 5% in TARE group (p < 0.001).

Conclusions: TARE and TACE offer comparable efficacy in managing large HCC, with TARE providing a safer profile, suggesting its consideration as a preferable initial therapeutic approach for unresectable HCC patients with tumors larger than 8 cm.

Achalasia is a rare esophageal motility disorder characterized by lack of primary peristalsis and a poorly relaxing lower esophageal sphincter. This disease process can be examined several ways and these evaluations can offer complementary information. There are three manometric subtypes of achalasia, with differing appearances on esophagram. Differentiating them is clinically important, because treatment for the subtypes varies. Timed barium esophagram (TBE) is a simple test to quantitatively evaluate esophageal emptying. TBE can be used to diagnose achalasia and assess treatment response. Considerable variation in the TBE protocol exist in the literature. We propose a standardized approach for TBE to allow for comparison across institutions.

Background: Nuclear grading of clear cell renal cell carcinoma (ccRCC) is crucial for its diagnosis and treatment.

Objective: To develop and validate a machine learning model for preoperative assessment of ccRCC nuclear grading using CT radiomics.

Materials and methods: This retrospective study analyzed 146 ccRCC patients who underwent surgery between June 2016 and January 2022 at two hospitals (the Quzhou Affiliated Hospital of Wenzhou Medical University with 117 cases and the Affiliated Cancer Hospital of University of Chinese Academy of Sciences with 29 cases). Radiomic features were extracted from preoperative abdominal CT images. Features reduction and selection were carried out using intraclass correlation efficient (ICCs), Spearman rank correlation coefficientsand and the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Radiomics and clinical models were developed utilizing Support Vector Machine (SVM), Extremely Randomized Trees (Extra Trees), Light Gradient Boosting Machine (LightGBM), Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. Subsequently, the radiomics nomogramwas developed incorporating independent clinical predictors and Rad_signature. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity, with decision curve analysis (DCA) assessing its clinical utility.

Results: We extracted 1834 radiomic features from each CT sequence, with 1320 features passing through the ICCs screening process. 480 radiomics features were screened by Spearson correlation coefficient. Then, 15 radiomic features with non-zero coefficient values were determined by Lasso dimensionality reduction technique. The five machine learning methods effectively distinguished nuclear grades. The radiomics nomogram outperformed clinical radiological models and radiomics feature models in predictive performance, with an AUC of 0.936 (95% CI 0.885-0.986) for the training set and 0.896 (95% CI 0.716-1.000) for the external verification set. DCA indicated potential clinical applicability of the nomogram.

Conclusion: The radiomics nomogram, developed by integrating clinically independent risk factors and and Rad_signature, demonstrated robust performance in preoperative ccRCC grading. It offers a non-invasive tool that aids in ccRCC grading and clinical decision-making, with potential to enhance treatment strategies.

Background: Some clinicopathological risk stratification systems (CRSSs) such as the leibovich score have been used to predict the postoperative prognosis of patients with clear cell renal cell carcinoma (ccRCC), but there are no reliable noninvasive preoperative indicators for predicting postoperative prognosis in clinical practice.

Purpose: To assess the value of a deep learning (DL) model based on CT images in predicting the postoperative prognosis of patients with ccRCC.

Materials and methods: A total of 382 patients with ccRCC were retrospectively enrolled andallocated to training (n = 229) or testing (n = 153) cohorts at a 6:4 ratio. The features were extracted from precontrast-phase (PCP), corticomedullary-phase (CMP) and nephrographic-phase (NP) CT images with ResNet50, and then extreme learning machines (ELMs) were used to construct classification models. The DL model and Leibovich score were compared and combined. A receiver operating characteristic (ROC) curve and integrated discrimination improvement (IDI) were used to evaluate model performance.

Results: Compared with other single-phase DL models, the three-phase CT-based DL model achieved the best performance, with an area under the curve (AUC) of 0.839. Combining the three-phase DL model and the Leibovich score (AUC = 0.823) into a nomogram (AUC = 0.888) statistically improved performance (IDI_{Nomogram vs. Three-phase} = 0.1358, IDI_{Nomogram vs. Leibovich} = 0.1393, [Formula: see text]< 0.001).

Conclusion: The CT-based DL model could be valuable for preoperatively predicting the prognosis of patients with ccRCC, and combining it with the Leibovich score can further improve its predictive performance.

Objective: This study aimed to investigate whether contrast-enhanced computed tomography (CECT) based radiomics analysis could noninvasively predict the perineural invasion (PNI) in esophageal squamous cell carcinoma (ESCC).

Methods: 398 patients with ESCC who underwent resection between February 2016 and March 2020 were retrospectively enrolled in this study. Patients were randomly divided into training and testing cohorts in a 7:3 ratio. Radiomics analysis was performed on the arterial phase images of CECT scans. From these images, 1595 radiomics features were initially extracted. The intraclass correlation coefficient (ICC), wilcoxon rank-sum test, spearman correlation analysis, and boruta algorithm were used for feature selection. Logistic regression (LR), random forest (RF), and support vector machine (SVM) models were established to preidict the PNI status. The performance of these radiomics models was assessed by the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was conducted to evaluate their clinical utility.

Results: Six radiomics features were retained to build the radiomics models. Among these models, the random forest (RF) model demonstrated superior performance. In the training cohort, the AUC value of the RF model was 0.773, compared to 0.627 for the logistic regression (LR) model and 0.712 for the support vector machine (SVM) model. Similarly, in the testing cohort, the RF model achieved an AUC value of 0.767, outperforming the LR model at 0.638 and the SVM model at 0.683. Decision curve analysis (DCA) suggested that the RF radiomics model exhibited the highest clinical utility.

Conclusions: CECT-based radiomics analysis, particularly utilizing the RF, can noninvasively predict the PNI in ESCC preoperatively. This novel approach could enhance patient management by providing personalized information, thereby facilitating the development of individualized treatment strategies for ESCC patients.

Purpose: Adrenal computed tomography (CT) has limitation due to imaging overlaps inthe washout characteristics of pheochromocytomas and adenomas (especially lipid-poor). The aim of this study was to investigate the distinguishability of lipid-poor adrenal adenomas and pheochromocytomas using whole-lesion CT histogram analysis.

Materials and methods: Histopathologically proven 24 lipid-poor adenomas and 29 pheochromocytomas (total 53 lesions in 53 patients) were included in this retrospective study. Data obtained from standard and volumetric examinations of the lesions by dedicated adrenal CT were compared between the two groups using univariate analysis. Parameters that showed differences were further evaluated using multivariate logistic regression analysis.

Results: Univariate analysis revealed significant differences between the two groups in terms of lesion size, lesion volume, percentage of relative wash out, peak HU values and the percentage of voxels with attenuation ≥ 100 HU, ≥ 110 HU and ≥ 120 HU (p = 0.0001, P = 0.0001, P = 0.01, P = 0.008, p = 0.04, p = 0.02, p = 0.02, respectively). Multivariate analysis revealed lesion size ≥ 22.05 mm (OR: 22; p < 0.0001), the percentage of voxels with attenuation ≥ 120 HU being ≥ 9% (OR: 3.27; p = 0.04), peak HU value ≥ 161.5 HU (OR: 4.40; p = 0.01) as risk factors for pheochromocytomas.

Conclusions: Whole lesion CT histogram analysis can be used to differentiate pheochromocytomas from lipid-poor adenomas. Lesion volume, the percentage of voxels with attenuation ≥ 120 HU and peak HU values are independent parameters that can assist in this differentiation. These findings may help avoid unnecessary biopsies and surgeries for lipid-poor adenomas, while identifying pheochromocytoma risk may improve perioperative patient management. Our results should be validated by future prospective studies.

Background: Ectopic fat deposition, involving lipid infiltration within organs and fat accumulating surrounding organs, plays a crucial role in the development of metabolic abnormalities in obesity. Current imaging measurements of obesity primarily focus on lipid infiltration within liver, neglecting fat deposition in other areas. This study aims to explore the methods of measuring and correlating different types of abdominal ectopic fat deposition in obese patients using magnetic resonance imaging (MRI) and ultrasound techniques, and to investigate the relationship between these fat parameters and obesity-related metabolic markers.

Methods: Abdominal ectopic fat deposition including liver fat content, mesenteric fat thickness (MFT), perirenal fat thickness (PrFT) and preperitoneal fat thickness (PFT) were measured in 220 overweight/obese patients using both MRI and ultrasound techniques. Correlation analysis validated the concordance of fat parameters at specific sites between the two imaging methods and identified the cutoff values of hepatic attenuation coefficient (AC) for diagnosis of liver steatosis. Additionally, we investigated the correlation between fat parameters by both methods and obesity-related metabolic markers.

Results: Ultrasonic measurement of PrFT and hepatic AC both had high correlation with PrFT (r = 0.829, p < 0.001) and hepatic Proton-density fat fraction (PDFF, r = 0.822, p < 0.001) measured via MR. Hepatic AC cutoff values for diagnosing mild, moderate, and severe fatty liver were 0.705 dB/cm/MHz (AUC = 0.922), 0.755 dB/cm/MHz (AUC = 0.923), and 0.875 dB/cm/MHz (AUC = 0.890) respectively. Hepatic AC correlated significantly with AST and ALT (r = 0.477 ~ 0.533, p < 0.001). MFT measured by ultrasound were positively associated with glycated hemoglobin (r = 0.324 ~ 0.371, p < 0.001) and serum triglyceride levels (r = 0.303 ~ 0.353, p < 0.001). PrFT measured by both methods showed significant positive correlations with serum creatinine levels (r = 0.305 ~ 0.308, p < 0.001).

Conclusions: Both MRI and ultrasound demonstrate metabolic correlations in quantifying mesenteric, hepatic, and perirenal fat. In addition to assessment of liver fat content, the measurements of ectopic fat deposition by MRI or ultrasound are a simple and crucial way for comprehensive fat evaluation in individuals with overweight/obesity.

Purpose: Lifelong re-examination of CT enterography (CTE) in patients with inflammatory bowel disease (IBD) may be necessary, and reducing radiation exposure during CT examinations is crucial. We investigated the potential application of deep learning reconstruction (DLR) in CTE to reduce radiation dose and improve image quality in IBD.

Methods: Thirty-six patients with known or suspected IBD were prospectively recruited to the low-dose CTE (LDCTE) group, while forty patients were retrospectively selected from previous clinical standard-dose CTE (STDCTE) scans as controls. STDCTE images were reconstructed with hybrid-IR (adaptive iterative dose reduction 3-dimensional [AIDR3D], standard setting); LDCTE images were reconstructed with AIDR3D and DLR (Advanced Intelligence ClearIQ Engine [AiCE], Body mild/standard/strong, Sharp Body mild/standard/strong setting). The effective radiation dose (ED), image noise, signal-to-noise ratio (SNR), overall image quality, subjective image noise, and diagnostic effectiveness were compared between the LDCTE and STDCTE groups.

Results: Compared with STDCTE, the ED of LDCTE was lower by 54.1% (p<0.001). Compared with STDCTE-AIDR3D, LDCTE-AIDR3D reconstruction objective image noise and SNR were greater (p<0.05), the subjective overall image quality was lower (p<0.05), and the diagnostic efficiency was lower (AUC=0.52, p<0.05). The SNRs of reconstructedimages of LDCTE-AiCE Body Strong and LDCTE-AiCE Body Sharp standard/strong groups were greater than that of STDCTE-AIDR3D group (all p<0.05), and the diagnostic performance was better than or comparable to that of STDCTE; the AUCs were 0.83, 0.76 and 0.76, respectively CONCLUSION: Compared with STDCTE with AIDR3D, LDCTE with DLR effectively reduced the radiation dose and improve image quality in IBD patients.