Journal of Computer Assisted Tomography最新文献

Objective: This study aims to clarify the frequency of renal parenchymal defects and deformations in each subtype of perirenal liposarcomas and to compare the differences between well-differentiated and non-well-differentiated types.

Methods: Patients with perirenal liposarcomas seen between July 2004 and June 2024 were included. Two radiologists blinded to the subtypes retrospectively evaluated CT or MR images for renal parenchymal defects and deformations. Frequencies of these findings were compared between well-differentiated versus non-well-differentiated types using the Fisher test.

Results: Forty-two patients (mean age: 66.3±11.5 y; 15 men) with perirenal liposarcomas were included. Renal parenchymal defects and deformations were observed in 0 (0%) and 1 (7.7%) of 13 well-differentiated, 5 (29.4%) and 6 (35.3%) of 17 dedifferentiated, 3 (37.5%) and 0 (0%) of 8 myxoid, and 1 (25.0%) and 1 (25.0%) of 4 pleomorphic types, respectively. Non-well-differentiated liposarcomas had higher frequencies of renal parenchymal defects and deformations compared with well-differentiated liposarcomas [9 of 29 (31.0%) vs. 0 of 13 (0%), P =0.038 and 7 of 29 (24.1%) vs. 1 of 13 (7.7%), P =0.398].

Conclusion: Renal parenchymal defects can be occasionally observed (31.0%) in non-well-differentiated perirenal liposarcomas unlike well-differentiated liposarcomas.

Objectives: (a) To investigate the relationship between tunnel volume (TV) and morphologic parameters of interatrial septum (IAS) in cases with type 3 and type 4 IAS; (b) To investigate the relationship between TV of the IAS and ischemic gliotic foci in brain MRI.

Materials and methods: We retrospectively reviewed the images of 301 cases who underwent CCTA in our center between 2020 and 2022. TV, tunnel length (TL), opening diameter of the right (ODRAE) and left atrium entrance (ODLAE), interatrial groove (IAG) diameter, and free flap length (FFL) were measured. The presence, number, and distribution of ischemic gliotic foci were examined in patients who had undergone brain MRI in the last 5 years before the CCTA. Pearson χ 2 , the Fisher Exact, Mann-Whitney U , linear regression analysis, Kruskal-Wallis test, and the Spearman correlation tests were used for statistical analysis of the data.

Results: A shorter FFL was related to the higher IAS type and increased likelihood of jet flow ( P =0.013). The correlation between wide IAG diameter and FFL was statistically significant ( P =0.003, r =0.22). The correlation between TV and ODRAE and ODLAE was also statistically significant (P <0.001, r =0.364, P <0.001, r =0.332, respectively). In type 3 and type 4 IAS, TV was associated with an increased number of ischemic gliotic foci ( P =0.008) and bilateral distribution ( P =0.006) on brain MRI.

Conclusion: Measurement of TL, ODRAE, ODLAE, and tunnel diameter in symptomatic cases with type 3 and type 4 IAS is crucial in determining the appropriate treatment approach. By adding the TV to the defined parameters, we thought that this innovation would contribute to invasive and noninvasive treatment management.

Objective: To construct and validate the optimal model for preoperative prediction of proliferative HCC based on habitat-derived radiomics features of Gd-EOB-DTPA-Enhanced MRI.

Methods: A total of 187 patients who underwent Gd-EOB-DTPA-enhanced MRI before curative partial hepatectomy were divided into training (n=130, 50 proliferative and 80 nonproliferative HCC) and validation cohort (n=57, 25 proliferative and 32 nonproliferative HCC). Habitat subregion generation was performed using the Gaussian Mixture Model (GMM) clustering method to cluster all pixels to identify similar subregions within the tumor. Radiomic features were extracted from each tumor subregion in the arterial phase (AP) and hepatobiliary phase (HBP). Independent sample t tests, Pearson correlation coefficient, and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm were performed to select the optimal features of subregions. After feature integration and selection, machine-learning classification models using the sci-kit-learn library were constructed. Receiver Operating Characteristic (ROC) curves and the DeLong test were performed to compare the identified performance for predicting proliferative HCC among these models.

Results: The optimal number of clusters was determined to be 3 based on the Silhouette coefficient. 20, 12, and 23 features were retained from the AP, HBP, and the combined AP and HBP habitat (subregions 1, 2, 3) radiomics features. Three models were constructed with these selected features in AP, HBP, and the combined AP and HBP habitat radiomics features. The ROC analysis and DeLong test show that the Naive Bayes model of AP and HBP habitat radiomics (AP-HBP-Hab-Rad) archived the best performance. Finally, the combined model using the Light Gradient Boosting Machine (LightGBM) algorithm, incorporating the AP-HBP-Hab-Rad, age, and AFP (Alpha-Fetoprotein), was identified as the optimal model for predicting proliferative HCC. For the training and validation cohort, the accuracy, sensitivity, specificity, and AUC were 0.923, 0.880, 0.950, 0.966 (95% CI: 0.937-0.994) and 0.825, 0.680, 0.937, 0.877 (95% CI: 0.786-0.969), respectively. In its validation cohort of the combined model, the AUC value was statistically higher than the other models ( P <0.01).

Conclusions: A combined model, including AP-HBP-Hab-Rad, serum AFP, and age using the LightGBM algorithm, can satisfactorily predict proliferative HCC preoperatively.

Objective: In CT imaging of severely obese patients, demanding clinical tasks like liver imaging may be constrained by scanner radiation output limits. This may impose an unavoidable increase in image noise and loss of image quality. In such patients, scan parameters may be restricted, leading to excessive x-ray tube heating and increased scan times that degrade exam and image consistency relative to other patients. In this study, the performance of dual-source (DS) CT with enhanced radiation output capacity was quantified relative to conventional single-source (SS) CT. The focus was on abdominopelvic imaging in severely obese patients (BMI >45 kg/m 2 ).

Methods: Abdominopelvic portal venous phase CT exams performed using DSCT were compared with exams using SSCT. General usage characteristics of the DSCT protocol were analyzed for >3000 exams over a 42-month period. More specifically, a total of 95 matched SS and DS scan pairs for the same patients were assessed in detail. The tube voltage, reconstruction method, and scanner platform were consistent in matched SS and DS scans, and changes in patient weight, diameter, and water equivalent diameter were <5%. Image global noise (GN), radiation dose (CTDI vol ), and key scan parameters were compared between matched SS and DS exams.

Results: The median (IQR) patient BMI was 48.4 kg/m 2 (45.9-52.1 kg/m 2 ). In the matched scan pairs, SS scans had a median (IQR) CTDI vol of 36.5 mGy (35.2-42.9 mGy) and median (IQR) GN of 14.1 HU (12.6-15.9 HU). DS scans had a significantly increased median (IQR) CTDI vol of 62.5 mGy (55.8-69.8 mGy) and reduced median (IQR) GN of 11.4 HU (10.6-12.4 HU; both P <0.001). Relative to SSCT, the DSCT protocol also enabled faster scan times at equal CTDI vol , lower tube current per x-ray tube, and improved GN consistency throughout axial slices.

Conclusion: It is feasible to utilize a DSCT protocol to significantly increase radiation output, bringing image noise characteristics in line with the general patient population in abdominopelvic imaging of severely obese patients. The DSCT protocol offers a more straightforward option to attain consistency in a group of patients where achieving diagnostic CT quality has proved challenging.

Objective: Precise delineation of brain tissues, including lesions, in MR images is crucial for data analysis and objectively assessing conditions like neurological disorders and brain tumors. Existing methods for tissue segmentation often fall short in addressing patients with lesions, particularly those with brain tumors. This study aimed to develop and evaluate a robust pipeline utilizing convolutional neural networks for rapid and automatic segmentation of whole brain tissues, including tumor lesions.

Materials and methods: The proposed pipeline was developed using BraTS'21 data (1251 patients) and tested on local hospital data (100 patients). Ground truth masks for lesions as well as brain tissues were generated. Two convolutional neural networks based on deep residual U-Net framework were trained for segmenting brain tissues and tumor lesions. The performance of the pipeline was evaluated on independent test data using dice similarity coefficient (DSC) and volume similarity (VS).

Results: The proposed pipeline achieved a mean DSC of 0.84 and a mean VS of 0.93 on the BraTS'21 test data set. On the local hospital test data set, it attained a mean DSC of 0.78 and a mean VS of 0.91. The proposed pipeline also generated satisfactory masks in cases where the SPM12 software performed inadequately.

Conclusions: The proposed pipeline offers a reliable and automatic solution for segmenting brain tissues and tumor lesions in MR images. Its adaptability makes it a valuable tool for both research and clinical applications, potentially streamlining workflows and enhancing the precision of analyses in neurological and oncological studies.

Objective: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC.

Methods: We retrospectively analysed the data of 288 eligible patients from 3 hospitals-1 primary cohort (center 1, n=222) and 2 external test cohorts (center 2, n=32 and center 3, n=34)-from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The 3-step (ICC-LASSO-RFE) method was used for feature selection, and 6 machine learning methods were used to construct models. Performance was compared through the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed through calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS).

Results: The DLRR model had the best performance, with AUCs of 0.981, 0.910, and 0.851 in the training, internal validation, and external validation sets, respectively. In addition, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients.

Conclusions: The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.

Objective: 18F-fluorodeoxyglucose positron-emission tomography (FDG PET) and whole-body (WB) MRI with diffusion weighted imaging (DWI) are complementary in assessment of multiple myeloma. However, WB DWI suffers from prolonged acquisition times and artifacts. Alternatively, rapid T2-weighted MRI with 2-point Dixon fat-suppression (T2-FS) has demonstrated promise in detection of bone lesions in exam times shorter than DWI. This study evaluated (1) the accuracy of rapid WB T2-FS for multiple myeloma lesion detection and (2) the incremental impact of adding DWI and FDG PET to T2-FS on diagnostic accuracy and patient care management.

Methods: This retrospective single-center study included patients with clinical WB PET/MRI exams obtained for multiple myeloma. T2-FS, DWI, and PET were reviewed in consensus by 2 readers, each technique reviewed blinded to the other 2 and to other imaging/clinical information. Focal lesions and nonfocal bone marrow disease were recorded. Per-lesion sensitivity and per-patient sensitivity and specificity for each technique were compared with a composite reference standard using McNemar exact test; 95% confidence intervals were calculated, and P<0.05 was considered significant. The incremental impact of adding DWI and PET to T2-FS on diagnostic accuracy and patient care management was recorded.

Results: From 34 PET/MRI exams from 34 patients, 3 incomplete exams were excluded. Among the 31 included exams, T2-FS demonstrated a significantly higher per-lesion sensitivity than DWI and PET, at 91.9%, 66.7%, and 44.4%, respectively (P<0.001). T2-FS identified all 21 patients with disease, compared with 85.7% for both DWI and PET; this difference did not reach statistical significance (P>0.050). Adding DWI to T2-FS did not change management in any patient; adding PET to T2-FS changed management in 3 patients.

Conclusion: T2-FS was more rapid and more sensitive than DWI for assessment of multiple myeloma. Unlike FDG PET, addition of DWI did not impact clinical management. Larger prospective studies for further validation are needed.

Infective endocarditis is a serious infection of the heart's inner lining and valves, with a high risk of systemic complications due to septic emboli. These complications can affect various organs, including the brain, lungs, abdomen, vasculature, and musculoskeletal system. Diagnosing infective endocarditis can be challenging, often with underappreciated complications that significantly impact treatment decisions, including the potential need for surgery. While echocardiography remains the primary diagnostic imaging modality, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) can be crucial for identifying and characterizing complications. This comprehensive review emphasizes the key role of radiologists in identifying secondary features of infective endocarditis, which can manifest in various organs. It explores the diverse presentations of infective endocarditis through patient cases, highlighting the strengths of different imaging modalities-echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-in diagnosing cardiovascular, pulmonary, and systemic complications. Understanding the imaging spectrum of infective endocarditis, is essential to enhancing diagnostic accuracy, guiding treatment decisions, and improving patient outcomes.

Objectives: To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT).

Methods: Fifteen cwHHT underwent a chest CTA (ie, a controlled "study" dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions.

Results: Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 (P=0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR (P=0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs.

Conclusions: Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Purpose: To characterize the predictive value of CT findings of fluid overload for predicting survival in patients undergoing transcatheter aortic valve replacement (TAVR).

Materials and methods: A retrospective review was performed on 265 patients undergoing CTA for TAVR planning purposes. Images for each patient were analyzed for evidence of fluid overload. Additional clinical data were gathered for each patient including serum NT-proBNP, eGFR, and albumin along with echocardiographic evaluation of left ventricular systolic function. Survival between groups with and without CT evidence of fluid overload (CTFO) was compared using Kaplan-Meier survival analysis and Cox proportional hazards model.

Results: Kaplan-Meier analysis demonstrates survival differences between the subjects with and without evidence of fluid overload. The Cox model demonstrates that CTFO is an independent predictor of survival outcomes. The hazard ratio in a model accounting for multiple variables was 2.93 with a P-value of 0.01. Notably, the Kaplan-Meier analysis demonstrates 100% survival for the first 50 days in patients with euvolemia on CT.

Conclusions: CT evidence of fluid overload before TAVR is associated with increased mortality.