Abdominal Radiology最新文献

Endometriosis is the presence of hormonally sensitive endometrium-like tissue outside the uterus. It is a common condition, affecting 10% of reproductive-age people assigned as female at birth. Although usually occurring in the pelvis, it can rarely involve the thoracic cavity and diaphragm which is termed thoracic endometriosis. Thoracic endometriosis syndrome (TES) refers to four well-recognized clinical entities: catamenial pneumothorax, catamenial hemothorax, catamenial hemoptysis, and lung nodules. However, TES presentation can also be nonspecific, even absent. Optimal management is multidisciplinary, as it depends on patient presentation and lesion characteristics. While initial imaging of thoracic symptoms is with chest radiography and computerized tomography, these modalities have inferior soft tissue contrast resolution and tissue characterization compared to magnetic resonance imaging (MRI), especially for the detection of both hematic and cystic endometriotic implants. Therefore, a dedicated MRI protocol is essential for diagnosing thoracic endometriosis and aiding surgical planning. Considering the dome-shaped morphology of the diaphragm, sagittal and coronal projections can improve visualization of tiny endometriotic plaques or deposits that are inconspicuous on the axial plane. Breath-hold and respiratory-triggered or navigated techniques are critical for mitigating motion artifacts. T1-weighted fat-suppressed sequences are important for identifying intrinsic T1 hyperintensity and blood products associated with endometriotic lesions. T2-weighted fat-suppressed sequences increase sensitivity for cystic or vesicular tissue. Diffusion-weighted and postcontrast imaging can help diagnose alternative causes of symptomology, including malignancy.

Purpose: This study aims to compare the performance of mono-exponential (Mono), fractional-order-calculus (FROC), and multi-compartment (MC) diffusion models in differentiating prostate lesions, including benign prostatic hyperplasia (BPH) and prostate cancer (PCa), as well as classifying PCa by clinical significance and risk levels.

Methods: A prospective study was conducted with 224 men (aged 50-80) undergoing 3 T MR imaging. Regions of interest (ROIs) analyses were performed on quantitative parameters from Mono, FROC, and MC models. These parameters were evaluated for their ability to distinguish BPH from PCa, clinically significant (CS) from clinically insignificant (CInS) PCa, and among PCa risk levels. Group differences were assessed using the Mann-Whitney U test and Kruskal-Wallis test, followed by post-hoc Dunn's test. ROC curves were plotted, and AUC was calculated. Logistic regression was used for parameter combinations, and performance was evaluated via 1000 bootstrap samples. The correlation between parameter pairs was analyzed. The image quality and PCa detection capability were also evaluated visually.

Results: In distinguishing PCa from BPH, the F1, ADC, and D parameters from the three models achieved high AUCs of 0.92, 0.91, and 0.91, respectively. For differentiating CS-PCa from CInS-PCa, the F2 parameter and the combination of C1 + F2 from the MC model showed the highest AUCs (0.75 and 0.76). In assessing PCa risk levels, F2 and C1 + F2 from the MC model showed the highest AUCs (0.73 and 0.74) for low vs. intermediate-risk PCa. For intermediate vs. high-risk PCa, F1, F1F2, and β + F1F2 from MC and FROC models had the highest AUCs (0.66, 0.66, and 0.71). In addition, ADC was strongly or moderately correlated to D, μ, F1, F1F2, F3, C1 and C3, and not correlated to β and F2. ADC and C1 demonstrated high image quality and strong PCa detection capability.

Conclusion: Advanced diffusion models, particularly the MC model, demonstrated a significant improvement over ADC in differentiating prostate lesions, especially between low and intermediate-risk PCa, between intermediate and high-risk PCa, and between clinically significant and insignificant PCa. Comparable performance was observed in distinguishing BPH from PCa among three models. Moreover, the combination of MC and FROC models further enhanced differentiation accuracy, particularly in the more challenging classifications between intermediate and high-risk PCa, where ADC alone proved inadequate. These results highlight the potential clinical value of MC model and combining MC and FROC models for more precise PCa risk stratification.

Purpose: Tumor deposits (TDs) and extramural venous invasion (EMVI) in locally advanced rectal cancer (LARC) are MRI-detectable markers that reflect the invasive and metastatic potential of tumors. However, both mrTDs and mrEMVI are closely associated with peritumoral vascular signals, and they show a high degree of statistical correlation. We developed a novel scoring system that integrates mrTDs and mrEMVI into a single parameter, simplifying the assessment process and capturing the prognostic value of both factors simultaneously.

Methods: We retrospectively included LARC patients who received neoadjuvant chemoradiotherapy at five centers and proposed a novel MRI-based scoring system, mrTE (derived from mrTDs and mrEMVI), to integrate the prognostic significance of mrEMVI and mrTDs in rectal cancer. The prognostic value of different mrTE scores was evaluated using Kaplan-Meier curves and the Cox model. The predictive accuracy of the new scoring system was evaluated using the integrated area under the ROC curve (iAUC).

Results: A total of 1188 patients with LARC were included in the evaluation cohort to assess the reliability of the novel imaging scoring system. Based on the mrTE scores ranging from 0 to 2, the patients were categorized into three groups. The 3-year disease-free survival rates for the groups were 88.1%, 78.1%, and 51.9% (score 1 vs 0: HR 2.00, 95% CI 1.36-2.93, p < 0.001; score 2 vs 0: HR 4.75, 95% CI 3.61-6.26, p < 0.001). The mrTE scoring system demonstrated superior performance in predicting DFS compared to other clinical and imaging markers, with a higher predictive accuracy (iAUC = 0.707).

Conclusions: The mrTE scoring system simplifies the clinical assessment of relevant MR markers and has proven to be an effective tool for predicting the prognosis of LARC patients.

Objectives: The Node Reporting and Data System (Node-RADS) provides structured and effective evaluation for lymph nodes in malignancies. This study aims to investigate its value in predicting residual lymph node metastasis (LNM) and survival outcome of locally advanced gastric cancer (LAGC).

Materials and methods: This retrospective study included 118 patients with LAGC underwent neoadjuvant chemotherapy (NAC) and gastrectomy from April 2015 to June 2020. The diagnostic performance of the post-NAC CT-based Node-RADS score for regional LNM, both at the patient level and at the perigastric/extragastric subgroup level, was estimated using area under receiver operating characteristic curve (AUC) and Youden's index. Kaplan-Meier curve was employed for prognostic analyses between high/low Node-RADS score group. A predictive Node-RADS (NR) model for LNM was developed using logistic regression analyses and a prognostic NR model for overall survival (OS) was developed using Cox regression analyses.

Results: In the prediction of LNM, the Node-RADS score exhibited an AUC of 0.843 (95%CI: 0.765-0.921) at patient level, 0.838 (95%CI: 0.757-0.918) in perigastric subgroup and 0.813 (95%CI: 0.724-0.901) in extragastric subgroup, surpassing LN short-axis criteria (AUC:0.664 [95%CI: 0.584-0.743], p < 0.001). The AUC of the NR predictive model for LNM increased to 0.870 (95%CI: 0.795-0.945), with 88.7% sensitivity and 78.9% specificity. The Node-RADS score was significantly correlated with post-NAC pathological status, and served as an independent indicator for OS (all p < 0.05).The NR prognostic model exhibited a Harrell's consistency index (C-index) of 0.724 (95%CI: 0.663-0.785), with no significant difference from the pathological prognostic model (0.739 [95%CI: 0.677-0.801], p = 0.695).

Conclusion: The post-NAC Node-RADS score provides accurate prediction of regional LNM and shows promising prognostic value for LAGC patients. Post-NAC Node-RADS related predictive models show potential in early identification of high-risk LAGC patients with residual lymph nodes or poor prognosis after NAC.

Purpose: To assess features of small pancreatic ductal adenocarcinoma (s-PDA, ≤ 2 cm) according to extrapancreatic extension (EPE) and predictors for recurrence.

Methods: This retrospective study included patients diagnosed with s-PDA who underwent surgery between January 2004 and October 2021. Preoperative CT or MRI images were reviewed by two reviewers. Imaging and clinicopathologic features of s-PDA were compared according to the presence of EPE. Cox regression analyses were performed to identify predictors of recurrence.

Results: 142 patients (77 men; 64.7 ± 9.3 years) who underwent preoperative CT (n = 134) or MRI (n = 115) were included. Duct dilatation was a common imaging finding of s-PDA (CT: 75.4%, MRI: 82.6%). Of the 142 patients, 21.8% (31/142) had no EPE, while 78.2% (111/142) had EPE. Tumor size on CT (14.3 ± 8.7 mm vs. 18.2 ± 6.5 mm, p =.01) and abutment or encasement of superior mesenteric vein (13.8% vs. 40.9%, p =.02) on CT were different according to absence or presence of EPE. Recurrence was more common in s-PDA with EPE (32.3% [10/31] vs. 53.2% [59/111], p =.04). Pathologic tumor size (HR 1.103, 95% CI 1.020-1.193, p =.01), tumor size on MRI (HR 1.044, 95% CI 1.001-1.090, p =.048), and extrapancreatic neural invasion on MRI (HR 3.341, 95% CI 1.564-7.140, p =.002) were significant predictors of recurrence.

Conclusion: Even in s-PDA, tumors with EPE are larger and show higher recurrence rates. Imaging features are important for predicting presence of EPE.

Purpose: Obesity has been linked to an increased incidence of various cancers, including bladder cancer. Among the different types of adipose tissue, visceral fat is recognized as the most metabolically active. However, there is a notable scarcity of studies investigating the impact of fat distribution, as measured by computed tomography (CT), on the prognosis of bladder cancer patients undergoing radical cystectomy (RC).

Materials and methods: Between January 2013 and January 2024, preoperative CT images of 34 patients who underwent RC were analyzed to measure total perivesical fat area (TPFA, mm²), fat density (FD, %), and subcutaneous fat thickness (SFT, mm). Multivariate Cox regression analysis was used to assess the effects of these variables on prognosis.

Results: The median age (IQR) of the patients was 65.5 years (12.5), and the median BMI (IQR) was 26.05 (5.98) kg/m². The median follow-up period (IQR) was 11 (31.2) months. A positive correlation was observed between BMI, TPFA, and SFT (r = 0.39, p = 0.02; r = 0.69, p < 0.001, respectively). According to Cox regression analysis, SFT, T stage, and N stage were found to be predictive factors for progression (HR = 1.11, p = 0.007; HR = 4.01, p = 0.04; HR = 6.47, p = 0.02, respectively), and T stage was also identified as an independent predictor for overall survival (HR = 5.32, p = 0.04).

Conclusion: SFT measurement alongside clinical staging would be beneficial in determining progression following RC. Future randomized controlled trials supporting our findings will highlight the significance of these measurements.

Objective: This study aims to evaluate the accuracy of a three-phase T1-derived model in staging liver fibrosis in patients with chronic hepatitis, in comparison with magnetic resonance elastography (MRE).

Methods: A total of 174 patients with chronic hepatitis were enrolled. The acquisition of three-phase longitudinal relaxation times (T1-Unenh = A, T1-DLP = B, T1HBP = C) from magnetic resonance enhancement examinations, as well as magnetic resonance elastography (MRE) and liver biopsy information for patients.Binary logistic regression was employed to construct models ABC. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and DeLong's test. Furthermore, the MRE and ABC models were subjected to a comprehensive analysis through 10-fold cross-validation.

Results: In all liver fibrosis stages (≥ F1-≥F4), both MRE and the ABC model showed moderate correlation with METAVIR fibrosis staging, with significant differences between groups (all P < 0.05). The area under the curve (AUC) for each group of the ABC model was above 0.84. DeLong's test indicated that in the ≥ F2, ≥F3, and ≥ F4 groups, the ABC model was comparable to MRE (all P > 0.05). Ten-fold cross-validation further confirmed that only in the ≥ F3 group did the ABC model outperform MRE in terms of comprehensive performance.

Conclusion: This study successfully validated the effectiveness of the three-phase T1-derived model in staging liver fibrosis in patients with chronic hepatitis. In stages ≥ F2, ≥F3, and ≥ F4, it is comparable to MRE, especially showing superior application value in the ≥ F3 stage.

Purpose: To explore the value of laboratory serum markers and magnetic resonance imaging(MRI) features in the preoperative differential diagnosis of pancreatic ductal adenocarcinoma (PDAC) in periampullary carcinoma (PAC).

Methods: A retrospective analysis of clinical data from 105 PAC patients who underwent pancreaticoduodenectomy was conducted, including 33 cases of PDAC (observation group) and 72 cases of non-PDAC (control group), with 25 cases of ampullary carcinoma, 38 cases of distal bile duct carcinoma, and 9 cases of periampullary duodenal carcinoma. Laboratory serum markers, MRI features, and pathological diagnosis data were compared between the two groups to analyze the value of laboratory serum markers and MRI features for differential diagnosis of PDAC within PAC.

Results: Compared to the control group, the observation group had higher proportions in total bilirubin, direct bilirubin, carcinoembryonic antigen(CEA), carbohydrate antigen 19 - 9 (CA19-9), quadruple duct sign, pancreatobiliary junction angle, main pancreatic duct diameter, pancreatic head side branch duct dilation, and hypovascular mass in pancreatic head, consistency of imaging and pathology diagnosis, perineural invasion (p < 0.05).The common bile duct diameter was smaller in the observation group (p < 0.05). The sensitivities of CA19-9, main pancreatic duct diameter, pancreatic head side branch duct dilation, and hypovascular mass in pancreatic head in PDAC within PAC are 53.8%, 51%, 84.6%, and 81.1%, respectively; the specificities are 81.8%, 87%, 86.1%, and 95.6%, respectively; and the areas under the ROC curve are 0.74, 0.749, 0.806, and 0.906, respectively.

Conclusion: In the preoperative diagnosis of PAC, CA19-9, main pancreatic duct diameter, hypovascular mass in pancreatic head, and pancreatic head side branch duct dilation are effective indicators for distinguishing PDAC.

The purpose of this study was to explore the association between structural changes in abdominal organs and visceral fat obesity (VFO) using a fully automated three-dimensional (3D) volumetric computed tomography (CT) measurement method based on deep learning algorithm. A total of 610 patients (295 men and 315 women; mean age, 68.4 years old) were included. Fully automated 3D volumetric CT measurements of the abdominal organs were performed to determine the volume and average CT attenuation values of each organ. All patients were divided into 2 groups based on the measured visceral fat area: the VFO group (≥ 100 cm²) and non-VFO group (< 100 cm²), and the structural changes in abdominal organs were compared between these groups. The volumes of all organs were significantly higher in the VFO group than in the non-VFO group (all of p < 0.001). Conversely, the CT attenuation values of all organs in the VFO group were significantly lower than those in the non-VFO group (all of p < 0.001). Pancreatic CT values (r = - 0.701, p < 0.001) were most strongly associated with the visceral fat, followed by renal CT values (r = - 0.525, p < 0.001) and hepatic CT values (r = - 0.510, p < 0.001). Fully automated 3D volumetric CT measurement using a deep learning algorithm has the potential to detect the structural changes in the abdominal organs, especially the pancreas, such as an increase in the volumes and a decrease in CT attenuation values, probably due to increased ectopic fat accumulation in patients with VFO. This technique may provide valuable imaging support for the early detection and intervention of metabolic-related diseases.

Objective: To determine the optimal virtual monoenergy for viewing the bowel at photon-counting CT enterography using quantitative assessment of mural attenuation, contrast-to-noise ratio, signal-to-noise ratio and noise.

Methods: This study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Consecutive adults (≥ 18 years) who underwent photon-counting CT enterography from 5/1/2022-5/31/2022 with available Spectral Postprocessing (SPP) images for retrospective virtual monoenergy creation were identified. Nine virtual monoenergetic series (40-120 keV, 10 keV increments) were created. Two region-of-interest measurements were placed in the stomach wall, jejunum wall, ileum wall, and each psoas muscle by two radiologists on 0.6 mm images in PACS. Region-of-interests were copied to other virtual monoenergies to ensure identical placement and size. Attenuation (HU) and noise (HU standard deviation) were recorded from each region-of-interest. Signal-to-noise ratio and contrast-to-noise ratio were computed for stomach, jejunum, ileum, and all bowel combined. Pairwise comparisons for attenuation, noise, signal-to-noise ratio and contrast-to-noise ratio for each virtual monoenergy were performed with ANOVA. A p <.05 indicated statistical significance.

Results: 50 patients (32 female; mean[SD] age: 57 years) were included. Attenuation and noise for all bowel regions were highest at 40 keV with statistically significant pairwise comparisons from 40 to 70 keV (all p <.05), but similar for 70-120 keV (all p >.05). Signal-to-noise ratio was similar from 40 to 70 keV (all p >.05) for all bowel regions. Contrast-to-noise ratio decreased with increasing keV. Contrast-to-noise ratio was similar for all bowel at 40 keV and 50 keV (p =.06), for stomach from 40 to 70 keV (all p >.05), for jejunum from 40 to 50 keV (p =.21), and for ileum from 40 to 60 keV (all p >.05).

Conclusion: 50 keV virtual monoenergetic images from photon-counting CT enterography optimizes contrast-to-noise ratio while mitigating noise and should routinely be utilized for bowel assessment at photon-counting CT enterography. As most photon-counting CT users primarily interpret virtual monoenergetic images in clinical practice, knowledge of the optimal virtual monoenergy can inform protocol development.