Abdominal Radiology最新文献

Purpose: To establish a preoperative prediction model for pathological grade of PanNETs based on computed tomography (CT), magnetic resonance imaging (MRI) and endoscopic ultrasonography (EUS).

Methods: Clinical data of 58 patients with pathologically confirmed PanNETs were included in this retrospectively study and they were divided into grade 1 and grade 2/3. CT, MRI and EUS images were collected within one week before surgery. A clinical predictive model based on the independent clinical risk factors and significant radiological features was established. The area under receiver operating characteristic curve (AUC) was performed to assess the model.

Results: Gender, pancreatic duct dilatation (PDD) and portal enhancement ratio (PER) were the independent predictors for PanNETs grading (P < 0.05). PanNETs grade 1 and grade 2/3 had statistical difference in elastography score (P = 0.001). The combination of gender, PDD and PER had better predictive efficiency than each of these three predictors alone, with a high AUC of 0.925. The elastography score also achieved an AUC of 0.838.

Conclusion: We proposed a comprehensive model based on preoperative CT, MRI and EUS to predict grade 1 and grade 2/3 of PanNETs and better informs clinicians on individualized diagnosis and treatment of patients with PanNETs.

Objectives: (1) To evaluate the diagnostic performance of the VI-RADS score in detecting muscle invasion in variant urothelial carcinomas (VUC). (2) To identify spesific MRI features that may serve as predicting for VUC.

Methods: Two hundred four patients who underwent TUR-B and/or radical cystectomy and a bladder mpMRI scan within three months prior to the procedure were retrospectively enrolled. The tumors were divided into two histological cohorts: pure urothelial carcinoma (PUC) and VUC. Various MRI features, including largest tumor diameter, long-to-short axis ratio, morphology, heterogeneous signal intensity (SI), presence of necrosis, and normalized ADC (ADC_n) value, were analyzed. The diagnostic performance of the VI-RADS score in predicting muscle invasion was calculated using a cut-off point of ≥ 4 in both cohorts. Univariate logistic regression were also performed to identify MRI features that predict VUC. Inter-reader agreement was assessed with the weighted kappa coefficient.

Results: Our study identified several MRI features significantly associated with VUC, including heterogeneous SI on T2-weighted images (OR: 3.055; 95% CI: 1.312-7.112; p < 0.001), dynamic contrast enhancement images (OR: 2.935; 95% CI: 1.263-6.821; p < 0.001), and the presence of necrosis (OR: 3.575; 95% CI: 1.798-7.107; p < 0.001). Additionally, ADC_n values were significantly lower in the VUC cohort (p = 0.003). The VI-RADS score demonstrated high diagnostic performance across both VUC and PUC cohorts, with sensitivity ranging from 94.4 to 86.8% (reader 1) and 94.2-82.2% (reader 2), specificity ranging from 100 to 94.6% (reader 1) and 100-94% (reader 2), and accuracy ranging from 96 to 90.6% (reader 1) and 96-88.2% (reader 2). The area under the curve (AUC) ranged between 0.972 and 0.972 (reader 1) and 0.838-0.781 (reader 2). No significant differences in diagnostic performance were observed between readers or cohorts (p > 0.05), and inter-reader agreement for VI-RADS scores was excellent for both cohorts.

Conclusion: VI-RADS score can be used with high performance to detect muscle invasion in VUC, regardless of reader experience. Additionally, specific MRI features such as heterogeneous SI, the presence of necrosis, and ADC_n values can serve as potential predictors of VUC.

Objective: To correlate fully-automated PMCT-based body composition measures with causes of death and comorbidities.

Materials and methods: Retrospective study of New Mexico Decedent Image Database (NMDID) with non-contrast PMCT scans between 2010 and 2017. Automated pipeline of AI-driven algorithms for quantifying skeletal muscle, subcutaneous/visceral fat, and aortic calcification from the abdominal component of PMCT scans was used. Scans with more than minimal decomposition were excluded. Cause of death was categorized as "acute" or "chronic." A predetermined model derived CT-based "biological age."

Results: 6638 decedents (mean age, 50±18 [SD]; 74% male) comprised the final cohort. 80% of deaths were classified as "acute," 10% as "chronic," and 10% "uncertain." Muscle density (HU) and area at the L3 lumbar level were higher in the "acute" versus "chronic" group (26 HU vs. 18 HU, p < 0.001; 192 cm² vs. 183 cm², p < 0.001). Muscle density and area at the L3 level were higher among those without cancer (25 HU vs. 16 HU, p < 0.001; 190 cm² vs. 169 cm², p < 0.01). Aortic Agatston scores were higher in those who died of heart disease (5120 vs. 2098, p < 0.001). Diabetic patients had higher L3 visceral fat area (227 cm² vs. 175 cm², p < 0.001) and lower muscle density (17 HU vs. 25 HU, p < 0.001). The deviation between chronological and biological age was significantly higher in the chronic versus acute group (median age deviation, 19 years vs. 10 years; p < 0.001).

Conclusion: Fully-automated quantitative PMCT-based tissue biomarkers correlate with the temporal nature of death and chronic co-morbidities, supporting their use for enhancing autopsies.

Purpose: To determine the biochemical recurrence (BCR)-related pelvic anatomic characteristics before radical prostatectomy (RP) and to establish a new predictive model for BCR-free survival (BCRFS).

Methods: The study involved 170 patients who underwent RP between January 2015 and December 2022. Kaplan-Meier plots were applied to estimate survival probabilities. Multivariate Cox regression models were employed to identify predictors for BCRFS, which were subsequently incorporated into an MRI-based nomogram to visualize the model. The Harrell's concordance index (C-index) was employed to evaluate the discrimination, and compared with a basic model without incorporating pelvic anatomy. Time-dependent receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were applied to identify the advantage of the new predictive model. Three risk categories were created.

Results: Multifactorial analysis revealed that age, capsule contact length (CCL), tumor's distance to the proximal membranous urethra (UD), urethral width, and annual surgery volume were independent risk factors for BCR (all p < 0.05). The established predictive model yielded a C-index of 0.850 that was superior to aforementioned basic model with C-index of 0.771 (p < 0.001). Our new model with an area under the ROC curve (AUC) of 0.893 revealed better predictive ability in BCRFS than basic model with the AUC of 0.823 (p = 0.01), and DCA demonstrated that our model generated more net benefits.

Conclusion: UD and urethral width are independent predictors of BCRFS. Our new model exhibits superior predictive accuracy with respect to BCRFS relative to the basic model. Apart from tumor features, pelvic anatomical features should also be considered before the treatment decision making of PCa patients.

Purpose: To find the optimal position of region of interest (ROI) for evaluating hepatic steatosis using attenuation imaging (ATI) in patients with metabolic dysfunction-associated fatty liver disease (MAFLD).

Methods: We retrospectively enrolled 143 consecutive patients who underwent percutaneous liver biopsy for the evaluation of MAFLD between October 2020 and October 2022. All ATI measurements were performed by the same radiologist. The ATI-ROI was placed at four different positions using a specialized workstation: the top edge of the sampling box (P1), the lower edge of the dark orange region (P2), 0.5 cm and 1 cm below the lower edge of the dark orange region (P3 and P4). Multivariate linear regression analysis and the area under the curve (AUC) analysis were performed.

Results: The AUCs of ATI at the four different ATI-ROI positions were 0.472 (95% confidence interval [CI]: 0.362-0.581), 0.693(0.611-0.768), 0.757(0.611-0.768), and 0.809 (0.735-0.870) for ≥ S1; 0.544 (0.459-0.628), 0.779 (0.702-0.844), 0.842 (0.772-0.898), and 0.865 (0.798-0.916) for ≥ S2; and 0.655 (0.571-0.733), 0.904 (0.843-0.947), 0.95 (0.9-0.979), and 0.949 (0.9-0.979) for S3, respectively. The factor that most significantly affected ATI was steatosis grade(P<0.001), when ATI-ROI was placed at the position of P2, P3, and P4.

Conclusion: Hepatic steatosis grade was the most significant determinant factor for ATI value at multivariate analysis. When clinicians conduct ATI measurement, the dark orange region indicating the area of reverberation artifact should be avoided, and placing the ATI-ROI 1 cm below the lower edge of the dark orange region may be a better choice.

Acute appendicitis represents a prevalent condition within the spectrum of acute abdominal pathologies, exhibiting a diverse clinical presentation. Computed tomography (CT) imaging has emerged as a prospective diagnostic modality for the identification and differentiation of appendicitis. This review aims to synthesize current applications, progress, and challenges in integrating machine learning (ML) with CT for diagnosing acute appendicitis while exploring prospects. ML-driven advancements include automated detection, differential diagnosis, and severity stratification. For instance, deep learning models such as AppendiXNet achieved an AUC of 0.81 for appendicitis detection, while 3D convolutional neural networks (CNNs) demonstrated superior performance, with AUCs up to 0.95 and an accuracy of 91.5%. ML algorithms effectively differentiate appendicitis from similar conditions like diverticulitis, achieving AUCs between 0.951 and 0.972. They demonstrate remarkable proficiency in distinguishing between complex and straightforward cases through the innovative use of radiomics and hybrid models, achieving AUCs ranging from 0.80 to 0.96. Even with these advancements, challenges remain, such as the "black-box" nature of artificial intelligence, its integration into clinical workflows, and the significant resources required. Future directions emphasize interpretable models, multimodal data fusion, and cost-effective decision-support systems. By addressing these barriers, ML holds promise for refining diagnostic precision, optimizing treatment pathways, and reducing healthcare costs.

Aim: To evaluate the predictive value of multiphasic magnetic resonance imaging (MRI) in identifying liver tumor perfusion characteristics and to compare it with hepatic artery perfusion scintigraphy findings in patients considered for selective internal radiation therapy (SIRT) with yttrium-90 (Y-90).

Methods: This study included 93 patients diagnosed with primary or secondary liver cancer between May 2021 and February 2024, comprising 47 patients (27 M/20F) deemed unsuitable for SIRT and 46 patients (26 M/20F) eligible for SIRT. The relationship between multiphasic MRI and scintigraphy findings in determining perfusion of tumors was analyzed. Predictive performance was evaluated with receiver operating characteristic (ROC) analysis, and the optimal cut-off values were determined using the Youden index.

Results: The SIRT unsuitable group had a lower frequency of intratumoral arterial phase hyperenhancement(APHE) (40.43% vs. 69.57%, p = 0.042), presence of hyperintensity on T2 sequence (72.34% vs. 95.65%, p = 0.026), lower lesion intensity in the portal phase (p = 0.033), and a lower lesion-to-liver intensity ratio in the portal phase (≤ 0.97, p = 0.011). The absence of intratumoral APHE [p = 0.049, AUC (95% CI) = 0.646 (0.508-0.783)] and a lesion-to-liver intensity ratio in the portal phase with a cut-off value of ≤ 0.97 [p = 0.011, AUC (95% CI) = 0.689 (0.564-0.815)] were significant predictors of SIRT unsuitability.

Conclusion: Both the absence of intratumoral APHE and a lower lesion-to-liver intensity ratio in the portal phase were significant predictors of SIRT unsuitability.

Purpose: Deep Learning Spectral Reconstruction (DLSR) potentially improves dual-energy CT (DECT) image quality, but there is a paucity of research involving human abdominal DECT scans. The purpose of this study was to comprehensively evaluate image quality by quantitatively and qualitatively comparing strong and standard levels of a DLSR algorithm. Optimal virtual monochromatic image (VMI) energy levels were also evaluated.

Methods: DECT scans of the abdomen/pelvis from 51 patients were retrospectively evaluated. VMIs were reconstructed at energy levels ranging from 35 to 200 keV using both standard and strong DLSR levels. For quantitative analysis, various abdominal structures were assessed using regions of interest, and mean signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values were calculated. This was supplemented with a qualitative evaluation of VMIs reconstructed at 35, 45, 55, and 65 keV.

Results: The strong-level DLSR demonstrated significantly better SNR and CNR values (p < 0.0001) compared to standard-level DLSR across all structures. The optimal SNR was observed at 70 keV (p < 0.0001), while the optimal CNR was found at 65 keV (p < 0.0001). The average qualitative scores between standard and strong DLSR were significantly different at 45, 55, and 65 keV (p < 0.0001). There was a moderate level of agreement between observers (ICC = 0.427, p < 0.0001).

Conclusion: A DLSR set to a strong level significantly improves image quality compared to standard-level DLSR, potentially enhancing the diagnostic evaluation of abdominal DECT scans. In addition to achieving a very high SNR, 65 keV VMIs had the highest CNR, which differs from what is typically observed with traditional DECT using non-deep learning reconstruction approaches.

Ovarian endometriotic cysts are associated with an increased risk of clear cell and endometrioid carcinomas, as well as borderline neoplasms. Although contrast-enhancing nodules on magnetic resonance imaging (MRI) suggest malignancy, benign endometriotic cysts can also present with such features, complicating differentiation from malignancy. When malignancy is suspected, minimally invasive procedures, such as laparoscopic cystectomy, are typically avoided. However, preserving fertility and ovarian function warrants careful consideration when selecting invasive surgical procedures. From the perspective of selecting appropriate surgical approaches, accurate preoperative differentiation between benign and malignant ovarian tumors is essential. We present the first case of MRI showing fallopian fimbriae entrapped in an endometriotic cyst mimicking malignancy. A 49-year-old female presented with atypical genital bleeding. MRI revealed a right ovarian endometriotic cyst with a contrast-enhancing mural nodule (10 mm), suggestive of malignancy. The nodule demonstrated T2-weighted hypointensity equivalent to the cyst fluid without diffusion restriction. Laparotomy revealed the nodule as entrapped fallopian fimbriae within the endometriotic cyst, with no malignancy detected. In this case, the fallopian fimbriae entrapped in the endometriotic cyst appeared as an enhancing nodule because of their vascularity, mimicking malignancy. Fallopian fimbriae are inconspicuous structures that can produce false findings suggestive of malignancy, similar to other benign enhancing nodules, such as polypoid endometriosis and decidualization. However, their lack of diffusion restriction and low T2-weighted signal intensity may help distinguish them from malignancy. This knowledge is crucial for accurate diagnosis and avoiding unnecessary interventions.