Abdominal Radiology最新文献

Objective: The aim of this study was to assess the prevalence of peritumoral enhancement (PTE) in patients with muscle-invasive bladder cancer (MIBC) and non-MIBC (NMIBC) and to propose a modified diagnostic criterion for Vesical Imaging Reporting and Data System (VI-RADS) that incorporates PTE.

Materials and methods: This retrospective study included 95 patients with bladder cancer (age, 72 ± 11; 77 men; 36 MIBCs and 59 Non-MIBCs) who underwent multiparametric MRI in our referral center between 2011 and 2023. The images were interpreted by four radiologists. The readers classified the possibility of muscle layer invasion into categories 1-5, based on the VI-RADS categorical diagnostic criterion. PTE was defined as a linear contrast enhancement observed at the edge of tumor invasion which is convex outward from the normal bladder wall and contrasts more than the normal muscle layer and tumor. A modified VI-RADS that upgrades the final VI-RADS category to 4 if PTE is present when the original VI-RADS category is 3 or less was proposed. The frequency of PTE in the MIBC and NMIBC groups was compared using the Fisher's exact test. Sensitivity and specificity for the diagnosis of MIBC were compared with the original VI-RADS using McNemar test. Pathologic diagnosis was used as the reference standard.

Results: PTE was present in 70-81% (25/36-29/36) of MIBC and absent in 92-98% (54/59-58/59) of non-MIBC. For all readers, the PTE was significantly more frequent (p < 0.001) in the MIBC group than the NMIBC group. The sensitivities of modified VI-RADS (75.0-86.1%) were significantly higher than those of original VI-RADS (41.7-55.6%) (p = 0.002-0.008). The specificity of modified VI-RADS (91.5-98.3%) were not statistically different from original VI-RADS (98.3-100%).

Conclusions: In conclusion, PTE is a highly specific finding for MIBC. modified VI-RADS incorporating PTE increases sensitivity for MIBC.

Purpose: Risk stratification for locoregional neuroblastoma partially relies on image-defined risk factors (IDRFs). This study aimed to evaluate how inter-observer variability in assessing IDRFs impacts risk stratification in locoregional abdominopelvic neuroblastoma.

Methods: A retrospective analysis was conducted on 123 patients who underwent upfront contrast-enhanced CT scans. Two radiologists independently assessed the presence of IDRFs. Patients were staged as either L1 (IDRF-negative) or L2 (IDRF-positive) according to the International Neuroblastoma Risk Group Staging System. Based on the radiologists' evaluations, 97 cases with sufficient clinical data were classified into risk groups using the revised Children's Oncology Group neuroblastoma risk classifier. The kappa values and 95% confidence intervals (CIs) were calculated to assess inter-radiologist agreement on IDRF evaluation and risk stratification.

Results: There was low agreement between radiologists in assessing L1/L2 status with a kappa value of 0.28 (95% CI: 0.14-0.42). However, agreement for evaluating the number of IDRFs was good, with an intraclass correlation coefficient of 0.73 (95% CI: 0.64-0.80). Based on the first radiologist's evaluation, 13 patients were classified as low-risk, 52 as intermediate-risk, and 32 as high-risk. Based on the second radiologist's evaluation, 37 patients were classified as low-risk, 37 as intermediate-risk, and 23 as high-risk. The kappa value for risk stratification between the two radiologists was 0.47 (95% CI: 0.33-0.62).

Conclusion: Inter-observer variability in assessing IDRF presence may affect risk stratification in locoregional abdominopelvic neuroblastoma.

Purpose: To evaluate Relaxation-Enhanced Angiography without Contrast and Triggering (REACT), a novel 3D isotropic flow-independent non-contrast-enhanced magnetic resonance angiography (non-CE-MRA) for imaging of the abdominal arteries, by comparing image quality and assessment of vessel stenosis intraindidually with 4D CE-MRA.

Methods: Thirty patients (mean age 35.7 ± 16.8 years; 20 females) referred for the assessment of the arterial abdominal vasculature at 3 T were included in this retrospective, single-centre study. The protocol comprised both 4D CE-MRA and REACT (navigator-triggering, Compressed SENSE factor 10, nominal scan time 02:54 min, and reconstructed voxel size 0.78 × 0.78 × 0.85  mm³). Two radiologists independently evaluated 14 abdominal artery segments for stenoses, anatomical variants, and vascular findings (aortic dissection, abdominal aorta aneurysms and its branches). Subjective image quality was assessed using a 4-point Likert scale (1 = non-diagnostic, 4 = excellent).

Results: REACT had a total acquisition time of 5:36 ± 00:40 min, while 4D CE-MRA showed a total acquisition time (including the native scan and bolus tracking sequence) of 3:45 ± 00:59 min (p = 0.001). Considering 4D CE-MRA as the reference standard, REACT achieved a sensitivity of 87.5% and specificity of 100.0% for relevant (≥ 50%) stenosis while detecting 89.5% of all vascular findings other than stenosis. For all vessels combined, subjective vessel quality was slightly higher in 4D CE-MRA (3.0 [IQR: 2.0; 4.0.]; P = 0.040), although comparable to REACT (3.0 [IQR: 2.0; 3.5]).

Conclusion: In a short scan time of about 5 min, REACT provides good diagnostic performance for detection of relevant stenoses, variants, and vascular findings of the abdominal arteries, while yielding to 4D CE-MRA comparable image quality.

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and developing imaging biomarkers, through novel imaging techniques, and most notably, artificial intelligence (AI) technology. This review provides an overview of these advancements, with a focus on deep learning methods for early detection of PDA.

Purpose: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

Methods: This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6 months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6 months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6 months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests.

Results: A total 168 HCCs (mean size 2.9 ± 1.7 cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001).

Conclusion: The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.

Objectives: To investigate the safety and efficacy of prostatic artery embolization (PAE) in the treatment of lower urinary tract symptoms (LUTS) caused by benign prostatic hyperplasia (BPH), and to investigate predictors of clinical success of PAE.

Methods: A retrospective analysis was used to collect 107 patients [median age 81.0 (73.0,85.0)] with BPH-related LUTS treated with PAE from September 2014 to February 2022 in a hospital. Repeated measurement ANOVA was used to compare the efficacy evaluation indicators at different times before and after PAE. Univariate and multivariate analyses were used to identify potential predictors of PAE clinical success and establish the optimal joint prediction model. The Receiver Operating Characteristic curves of the quantitative predictors and multivariate model prediction probability values significantly correlated with clinical success were plotted.

Results: Of the 107 cases, 103 (96.3%) successfully underwent PAE. The International Prostate Symptom Score (IPSS) decreased from a baseline mean of 24.94 to 10.19 (P < 0.05) 3 months after PAE, and the mean IPSS at 6 months, 12 months and 24 months was 10.12, 11.30 and 11.86, respectively, which were statistically significant compared with baseline (P<0.05). Predictors of clinical success were greater prostate volume (> 65 ml, P = 0.018), adenomatous-dominant benign prostatic hyperplasia (AdBPH)(P = 0.030), indwelling catheterization due to urine retention (P = 0.028), and bilateral embolization (P = 0.018).

Conclusion: PAE was able to significantly improve BPH-related LUTS and the outcome indicators remained stable at long-term follow-up. Preoperative urinary retention catheters, AdBPH, larger prostate volume (> 65 ml) and bilateral embolization suggest better clinical efficacy.

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is crucial for effective triage, referral, and follow-up therapy. While various deep learning techniques have been proposed for identifying pelvic masses, current methods are often not accurate enough and can be computationally intensive. To address these issues, this manuscript introduces an optimized Siamese circle-inspired neural network with deep linear graph attention (SCINN-DLGN) model designed for pelvic mass classification. The SCINN-DLGN model is intended to classify pelvic masses into three categories: benign, malignant, and healthy. Initially, real-time MRI pelvic mass images undergo pre-processing using semantic-aware structure-preserving median morpho-filtering to enhance image quality. Following this, the region of interest (ROI) within the pelvic mass images is segmented using an EfficientNet-based U-Net framework, which reduces noise and improves the accuracy of segmentation. The segmented images are then analysed using the SCINN-DLGN model, which extracts geometric features from the ROI. These features are classified into benign, malignant, or healthy categories using a deep clustering algorithm integrated into the linear graph attention model. The proposed system is implemented on a Python platform, and its performance is evaluated using real-time MRI pelvic mass datasets. The SCINN-DLGN model achieves an impressive 99.9% accuracy and 99.8% recall, demonstrating superior efficiency compared to existing methods and highlighting its potential for further advancement in the field.