Journal of Computer Assisted Tomography最新文献

Objective: This study investigated novel radiomic features derived from apparent diffusion coefficient (ADC) maps for diagnosing Sjögren syndrome (SS) in patients without visible magnetic resonance morphologic changes.

Materials and methods: This study prospectively analyzed 119 consecutive patients with SS and 95 healthy volunteers using 3.0 T magnetic resonance imaging, including diffusion-weighted imaging with b values of 0 and 1000 s/mm 2 . Regions of interest (ROIs) were manually delineated along the margins of the largest parotid gland slice on ADC maps, from which 838 quantitative features were automatically extracted. Based on the intraclass correlation coefficient and absolute correlation coefficient, 45 radiomic parameters were selected for analysis. The differentiation between patients with SS and healthy controls was evaluated using univariate analysis and receiver operating characteristic analysis. Multiple radiomic features were integrated using binary logistic regression analysis. Through machine learning algorithms, 4 predictive models were developed, and each was thoroughly evaluated for predictive performance. The Shapley Additive exPlanations (SHAP) approach was employed to elucidate the predictive factors influencing the model.

Results: Twenty-two radiomic parameters demonstrated significant differences between SS and control groups. The AUCs were 0.681 ± 0.100 (0.559~0.878). The optimal diagnostic combination for SS consisted of 6 parameters: 0.975Quantile, 180dr_D(4)_Cluster Prominence, 225dr_D(7)_Entropy, 315dr_D(7)_Entropy, Compactness2, and Max3D Diameter, achieving an AUC of 0.956. The SVM, GBM, and XGBoost models were effectively distinguished SS from healthy controls. Among all the parameters, Max3DDiameter demonstrated the strongest predictive power in the model.

Conclusions: Radiomic features derived from ADC maps demonstrate significant potential in facilitating the early diagnosis of SS.

Purpose: To evaluate the accuracy of statistical parametric mapping (SPM) and Scenium in the differential diagnosis of Parkinson disease (PD) and atypical Parkinsonian syndromes based on 18 F-fluoro-deoxy-glucose ( 18 F-FDG) imaging, and to explore the application of these 2 software programs in analyzing patients with Parkinson disease of varying severity, as well as to construct and evaluate the metabolic profiles of PD patients using Scenium.

Methods: A total of 64 patients with Parkinsonian syndrome who met the diagnostic criteria were included in this study. PET images were used for disease diagnosis with SPM and Scenium based on diagnostic charts, and the diagnostic accuracy of both software programs was assessed through consistency analysis. Meanwhile, an in-depth analysis was performed to compare the sensitivity, specificity, positive predictive value, and negative predictive value of the 2 software programs. In addition, Scenium was used to construct a diagnostic model for PD.

Results: SPM demonstrated greater accuracy in distinguishing between PD and APS, with a significantly higher Kappa value (K_spm=0.704) compared with Scenium (K_scenium=0.440). The sensitivity and specificity of SPM were 82.5% and 91.7%, respectively. Further, a PD diagnostic model was constructed by incorporating PET parameters from the contralateral central region and basal ganglia, achieving a diagnostic accuracy of 82.9%.

Conclusions: SPM can more accurately differentiate the diagnosis of Parkinson disease from atypical Parkinson syndrome compared with Scenium.

Objective: To evaluate the effectiveness of the new-generation snapshot freeze (SSF2) algorithm combined with Deep Learning Image Reconstruction (DLIR) in improving the image quality of coronary artery calcifications (CAC) and their quantification.

Methods: Coronary artery calcification score (CACS) scans were performed on 69 patients using ECG-triggered noncontrast CT. Four groups of images were reconstructed with SSF2 or without (STD), combined with ASIR-V (Adaptive Statistical Iterative Reconstruction-V) and DLIR: STD ASIR-V , STD DLIR , SSF2 ASIR-V , and SSF2 DLIR . CAC image quality was compared, and inter-observer consistency was evaluated among reconstruction groups. CACS, including the Agatston score (AS), volume score (VS), mass score (MS), and the risk stratification based on AS among groups, were compared.

Results: The consistencies of the inter-observer image quality scores were excellent or good (kappa=0.705 to 0.837). SSF2 ASIR-V and SSF2 DLIR had significantly higher scores than STD ASIR-V and STD DLIR in reducing motion artifacts of calcified plaques ( P <0.05), while no significant differences between SSF2 ASIR-V and SSF2 DLIR , or between STD ASIR-V and STD DLIR ( P >0.05). There was no significant difference in CT values of vessels, subcutaneous fat, and muscle in CAC images, but the noises of SSF2 ASIR-V and STD ASIR-V images were significantly higher than those of SSF2 DLIR and STD DLIR images ( P >0.05). STD ASIR-V had the highest CACS values, while SSF2 DLIR had the lowest. Using AS in STD ASIR-V as the reference, 9 patients (13.04%) in SSF2 DLIR and 7 patients (10.14%) in SSF2 ASIR-V had a risk stratification reduced, while no change in STD DLIR .

Conclusions: SSF2 and DLIR significantly reduce motion artifacts and image noise in non-contrast CACS CT, respectively. SSF2 reduces CACS values and risk stratification.

Obstructive sleep apnea (OSA) can result from various causes of partial or complete obstruction of the upper airway. CT is amenable to quantitative analysis of the upper airway and surrounding structures. CT is also useful for identifying abnormalities that could be attributed to the patient's symptoms and is relevant for surgical planning. There are various surgical procedures that can be performed for OSA that can also be encountered on CT. The relevant anatomic measurements, imaging features of various pathologies that can affect the upper airway, and postoperative imaging for OSA are reviewed in this article.

Objective: This study aimed to determine the characteristic clinical and magnetic resonance imaging (MRI) findings that can distinguish nodular fasciitis (NF) from myxofibrosarcoma (MFS) because they are sometimes difficult to differentiate due to the overlapping imaging findings, such as the "fascial tail" sign.

Methods: Thirty patients with NF and 44 with MFS were included in this study. The following MRI features were evaluated: mass size, anatomical and compartmental location, presence and type of pseudo-capsule, degree of heterogeneity, presence, and length of the "fascial tail" sign, and presence of peritumoral edema. Using diffusion-weighted images (DWI), we determined the presence of diffusion restriction and measured the apparent diffusion coefficient (ADC) values. On dynamic contrast-enhanced (DCE) images, we obtained the values of K trans , K ep , V e , iAUC, and time-concentration curves using Tissue 4D.

Results: The patients with NF were significantly younger than those with MFS. The average sizes of MFS and NF were 6.27±3.74 and 3.03±1.81 cm, respectively. Linear logistic regression analysis revealed that age, recurrence, "fascial tail" length, peritumoral edema, enhancement heterogeneity, and V e differed significantly between the NF and MFS groups. The length of "fascial tail," contrast heterogeneity, and compartmental location were statistically significant factors influencing the recurrence.

Conclusions: Older age (above 46 y), larger tumor size (>4 cm), peritumoral edema, enhancement heterogeneity, and longer "fascial tail" (>25 mm) are more frequently associated with MFS, while the functional MR imaging findings, except the V e value (>0.417), showed no significant differences.

Objective: This study aims to evaluate the predictive value of multiparameter characteristics of coronary computed tomography angiography (CCTA) plaque and the ratio of coronary artery volume to myocardial mass (V/M) in guiding percutaneous coronary stent implantation (PCI) in patients diagnosed with unstable angina.

Methods: Patients who underwent CCTA and coronary angiography (CAG) within 2 months were retrospectively analyzed. According to CAG results, patients were divided into a medical therapy group (n=41) and a PCI revascularization group (n=37). The plaque characteristics and V/M were quantitatively evaluated. The parameters included minimum lumen area at stenosis (MLA), maximum area stenosis (MAS), maximum diameter stenosis (MDS), total plaque burden (TPB), plaque length, plaque volume, and each component volume within the plaque. Fractional flow reserve (FFR) and pericoronary fat attenuation index (FAI) were calculated based on CCTA. Artificial intelligence software was employed to compare the differences in each parameter between the 2 groups at both the vessel and plaque levels.

Results: The PCI group had higher MAS, MDS, TPB, FAI, noncalcified plaque volume and lipid plaque volume, and significantly lower V/M, MLA, and CT-derived fractional flow reserve (FFRCT). V/M, TPB, MLA, FFRCT, and FAI are important influencing factors of PCI. The combined model of MLA, FFRCT, and FAI had the largest area under the ROC curve (AUC=0.920), and had the best performance in predicting PCI.

Conclusions: The integration of AI-derived multiparameter features from one-stop CCTA significantly enhances the accuracy of predicting PCI in angina pectoris patients, evaluating at the plaque, vessel, and patient levels.

In the rapidly evolving landscape of medical education, artificial intelligence (AI) holds transformative potential. This manuscript explores the integration of large language models (LLMs) in Radiology education and training. These advanced AI tools, trained on vast data sets, excel in processing and generating human-like text, and have even demonstrated the ability to pass medical board exams. In radiology, LLMs enhance clinical education by providing interactive training environments that improve diagnostic skills and structured reporting. They also support research by streamlining literature reviews and automating data analysis, thus boosting productivity. However, their integration raises significant challenges, including the risk of over-reliance on AI, ethical concerns related to patient privacy, and potential biases in AI-generated content. This commentary from the Early Career Committee of the Society for Advanced Body Imaging (SABI) offers insights into the current applications and future possibilities of LLMs in Radiology education while being mindful of their limitations and ethical implications to optimize their use in the health care system.

Purpose: To compare the scan timing adequacy for the pancreatic phase between fixed and tailored scan delay in the pancreatic protocol CT with a bolus-tracking technique.

Materials and methods: This retrospective study included patients who underwent pancreatic protocol CT using a fixed scan delay of 20 s from January 2020 to November 2022 (conventional group) and those using a tailored scan delay from January 2023 to July 2024 (tailored group). Tailored scan delay was identified to be the same as the time from contrast injection to reaching to trigger threshold of 100 HU (Time TRIG ). The scan delay ratio (SDR) was calculated by dividing the scan delay by Time TRIG . Two radiologists assessed the scan timing adequacy for the pancreatic phase and classified it into 3 categories: early, appropriate, and late. The SDR and scan timing adequacy were compared between the conventional and tailored groups.

Results: This study involved 128 patients (75 men; median age, 71 y), including 63 and 65 in the conventional and tailored groups, respectively. The median SDR was significantly different between the two groups (1.2 and 1.0 in the conventional and tailored groups; P <0.001). The proportion of appropriate scan timing for the pancreatic phase was higher in the tailored group (55/65; 84%) than in the conventional group (47/63; 75%); however, no statistical significance was observed ( P = 0.36).

Conclusions: The tailored scan delay tended to provide a higher rate of appropriate scan timing for the pancreatic phase compared with the conventional protocol using a fixed scan delay of 20 s.

Purpose: To describe imaging features of metanephric adenomas, assess the reliability of a diagnosis with image-guided percutaneous renal mass biopsy, and evaluate patient survival outcomes.

Materials and methods: In this IRB-approved, HIPAA-compliant retrospective study, our institution's radiology report database was searched for the term "metanephric adenoma" from 2010 to 2020. Patient information, imaging mass characteristics, and percutaneous biopsy technique and complications were recorded. Analyses of per-tumor growth rate, per-procedure diagnostic rates, and per-patient disease-specific and metastasis-free survival were evaluated.

Results: The database search yielded 8 tumors (mean diameter 2.0 cm, range 1.0 to 3.1 cm) in 8 patients (median age 60.5 y, range 40 to 66 y; 6 women) who underwent percutaneous biopsies and had imaging available for review. All tumors (8/8) were solitary, well-defined, and hypoenhancing on post-contrast images. For those with available MR, 100% (5/5) demonstrated restricted diffusion. On unenhanced CT, 62.5% (5/8) were hyperdense. The mean tumor growth rate was 0.7 mm/y (range: -0.1 to 3 mm/y) with a median imaging follow-up of 83.4 months (range: 1.6 to 198.0 mo). Specific diagnosis of metanephric adenoma on the first percutaneous biopsy was found in 75% (6/8) of patients; with repeat biopsy in 2 patients confirming metanephric adenoma. Per-patient survival outcome after a median clinical follow-up of 151.8 months (range: 1.6 to 250.6 mo) showed 100% disease-specific and metastasis-free survival.

Conclusions: Metanephric adenomas are usually solitary, well-defined, and hypoenhancing masses on imaging, hyperattenuating compared with the renal parenchyma on noncontrast CT, and with restricted diffusion on MR. Image-guided percutaneous biopsy results of this tumor are reliable and safe.

Objective: This study aims to explore a grading diagnostic method for the binary classification of meniscal tears based on magnetic resonance imaging radiomics. We hypothesize that a radiomics model can accurately grade meniscal injuries in the knee joint. By extracting T2-weighted imaging features, a radiomics model was developed to distinguish meniscal tears from nontear abnormalities.

Materials and methods: This retrospective study included imaging data from 100 patients at our institution between May 2022 and May 2024. The study subjects were patients with knee pain or functional impairment, excluding those with severe osteoarthritis, infections, meniscal cysts, or other relevant conditions. The patients were randomly allocated to the training group and test group in a 4:1 ratio. Sagittal fat-suppressed T2-weighted imaging sequences were utilized to extract radiomic features. Feature selection was performed using the minimum Redundancy Maximum Relevance (mRMR) method, and the final model was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Model performance was evaluated on both the training and test sets using receiver operating characteristic curves, sensitivity, specificity, and accuracy.

Results: The results showed that the model achieved area under the curve values of 0.95 and 0.94 on the training and test sets, respectively, indicating high accuracy in distinguishing meniscal injury from noninjury. In confusion matrix analysis, the sensitivity, specificity, and accuracy of the training set were 88%, 92%, and 87%, respectively, while the test set showed sensitivity, specificity, and accuracy of 89%, 82%, and 85%, respectively.

Conclusions: Our radiomics model demonstrates high accuracy in distinguishing meniscal tears from nontear abnormalities, providing a reliable tool for clinical decision-making. Although the model demonstrated slightly lower specificity in the test set, its overall performance was good with high diagnostic capabilities. Future research could incorporate more clinical data to optimize the model and further improve diagnostic accuracy.