Journal of Computer Assisted Tomography最新文献

Objective: Magnetic resonance (MR) relaxometry is an absolute and reproducible quantitative method, compared with signal intensity for the evaluation of liver biliary function. This is obtainable by the T1 reduction rate (T1RR), as it carries a smaller systematic error than the pre/post contrast agent T1 measurement. We aimed to develop and test an MR T1 relaxometry tool tailored for the evaluation of liver T1RR after gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid administration on 1.5T MR.

Methods: In vitro/vivo (liver) T1RR values with two 3D FLASH variable-flip-angle sequences were calculated by a MATLAB algorithm. In vitro measurements were done by 2 physicists, in consensus. The prospective in vivo study was approved by the local ethical committee and performed on 13 normal/26 cirrhotic livers. A supplemental test in 5 normal/5 cirrhotic livers, out of the studied series, was done to compare the results of our method (without B1 inhomogeneity correction) and those of a standardized commercial tool (with B1 inhomogeneity correction). All in vivo evaluations were performed by 2 radiologists with 7 years of experience in abdominal imaging. Open-source Java-based software ImageJ was used to draw the free-hand regions of interest on liver section and for the measurement of hepatic T1RR values. The T1RR values of each group of patients were compared to assess statistically significant differences. All statistical analyses were performed with IBM-SPSS Statistics. In vivo evaluations, the intrareader and interreader reliability was assessed by intraclass correlation coefficient.

Results: Our method showed good accuracy in evaluating in vitro T1RR with a maximum percentage error of 9% (constant at various time points) with T1 values in the 200- to 1400-millisecond range. In vivo, a high concordance between the T1RR evaluated with the proposed method and that calculated from the standardized commercial software was verified ( P < 0.05). The median T1RRs were 74.8, 67.9, and 52.1 for the normal liver, Child-Pugh A, and Child-Pugh B cirrhotic groups, respectively. A very good agreement was found, both within intrareader and interreader reliability, with intraclass correlation coefficient values ranging from 0.88 to 0.95 and from 0.85 to 0.90, respectively.

Conclusions: The proposed method allowed accurate reliable in vitro/vivo T1RR assessment evaluation of the liver biliary function after gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid administration.

Purpose: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images.

Methods: The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists.

Results: The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists.

Conclusions: The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

Objective: The aims of this study were to assess the chest computed tomography (CT) findings in post-COVID-19 respiratory condition (rPCC) patients and compare the findings with asymptomatic participants (APs). It also aimed to evaluate the relationship between CT findings and pulmonary function tests (PFTs) in rPCC patients. Finally, it aimed to compare the quantitative chest CT findings and PFT results of patients with rPCC and APs.

Methods: We retrospectively enrolled consecutive patients with rPCC who underwent unenhanced chest CT and PFTs between June 2020 and September 2022. In addition, a control group (APs) was prospectively formed and underwent nonenhanced chest CT and PFTs. The presence and extent of abnormalities in unenhanced chest CT images were evaluated qualitatively and semiquantitatively in a blinded manner. We used fully automatic software for automatic lung and airway segmentation and quantitative analyses.

Results: Sixty-three patients with rPCC and 23 APs were investigated. Reticulation/interstitial thickening and extent of parenchymal abnormalities on CT were significantly greater in the rPCC group than in the control group ( P = 0.001 and P = 0.004, respectively). Computed tomography extent score was significantly related to length of hospital stay, age, and intensive care unit stay (all P s ≤ 0.006). The rPCC group also had a lower 85th percentile attenuation lung volume ( P = 0.037). The extent of parenchymal abnormalities was significantly correlated with carbon monoxide diffusing capacity ( r = -0.406, P = 0.001), forced vital capacity (FVC) ( r = -0.342, P = 0.002), and forced expiratory volume in 1 second/FVC ( r = 0.427, P < 0.001) values. Pulmonary function tests revealed significantly lower carbon monoxide diffusing capacity ( P < 0.001), FVC ( P = 0.036), and total lung capacity ( P < 0.001) values in the rPCC group.

Conclusions: The rPCC is characterized by impaired PFTs, a greater extent of lung abnormalities on CT, and decreased 85th percentile attenuation lung volume. Advanced age, intensive care unit admission history, and extended hospital stay are risk factors for chest CT abnormalities.

Objective: The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status.

Methods: Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation.

Results: The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results.

Conclusions: In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.

Objective: The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas.

Methods: Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs.

Results: IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images.

Conclusions: TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.

Objective: This study aimed to develop a diagnostic model to estimate the distribution of small renal mass (SRM; ≤4 cm) histologic subtypes for patients with different demographic backgrounds and clear cell likelihood score (ccLS) designations.

Materials and methods: A bi-institution retrospective cohort study was conducted where 347 patients (366 SRMs) underwent magnetic resonance imaging and received a ccLS before pathologic confirmation between June 2016 and November 2021. Age, sex, race, ethnicity, socioeconomic status, body mass index (BMI), and the ccLS were tabulated. The socioeconomic status for each patient was determined using the Area Deprivation Index associated with their residential address. The magnetic resonance imaging-derived ccLS assists in the characterization of SRMs by providing a likelihood of clear cell renal cell carcinoma (ccRCC). Pathological subtypes were grouped into four categories (ccRCC, papillary renal cell carcinoma, other renal cell carcinomas, or benign). Generalized estimating equations were used to estimate probabilities of the pathological subtypes across different patient subgroups.

Results: Race and ethnicity, BMI, and ccLS were significant predictors of histology (all P < 0.001). Obese (BMI, ≥30 kg/m 2 ) Hispanic patients with ccLS of ≥4 had the highest estimated rate of ccRCC (97.1%), and normal-weight (BMI, <25 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 had the lowest (0.2%). The highest estimated rates of papillary renal cell carcinoma were found in overweight (BMI, 25-30 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 (92.3%), and the lowest, in obese Hispanic patients with ccLS ≥4 (<0.1%).

Conclusions: Patient race, ethnicity, BMI, and ccLS offer synergistic information to estimate the probabilities of SRM histologic subtypes.