Intracranial aneurysm (IAN) is a class of cerebrovascular diseases with a serious threat to patients, and an accurate diagnosis of IAN is very important for both selection of the appropriate therapy and prediction of the prognosis. This study aimed to evaluate the diagnostic values of zero-echo-time magnetic resonance angiography (ZTE-MRA) and time-of-flight magnetic resonance angiography (TOF-MRA) in patients with IAN. Digital subtraction angiography, ZTE-MRA, and TOF-MRA were performed in 18 patients diagnosed with IAN. The images of ZTE-MRA and TOF-MRA were compared for image quality, qualitative diagnosis, detailed diagnosis, number of thrombi, and residual aneurysm lumen, with digital subtraction angiography as the reference. Zero-echo-time MRA and TOF-MRA did not show a significant difference in image quality or detailed information (including aneurysm size, growth direction, and angle with the aneurysm-carrying vessel) ( P > 0.05). However, ZTE-MRA showed advantages over TOF-MRA in terms of qualitative diagnosis (sensitivity and specificity), intra-aneurismal thrombus detection, and residual aneurysm lumen detection after embolization ( P < 0.05). Compared with TOF-MRA, ZTE-MRA showed greater diagnostic value for IAN patients in terms of qualitative diagnosis, as well as the detection of intra-aneurysm thrombi and residual aneurysm lumen after embolization.

To determine whether integration of data on body composition and radiomic features obtained using baseline 18 F-FDG positron emission tomography/computed tomography (PET/CT) images can be used to predict the prognosis of patients with stage IV non-small cell lung cancer (NSCLC). A total of 107 patients with stage IV NSCLC were retrospectively enrolled in this study. We used the 3D Slicer (The National Institutes of Health, Bethesda, Maryland) software to extract the features of PET and CT images. Body composition measurements were taken at the L3 level using the Fiji (Curtis Rueden, Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison) software. Independent prognostic factors were defined by performing univariate and multivariate analyses for clinical factors, body composition features, and metabolic parameters. Data on body composition and radiomic features were used to build body composition, radiomics, and integrated (combination of body composition and radiomic features) nomograms. The models were evaluated to determine their prognostic prediction capabilities, calibration, discriminatory abilities, and clinical applicability. Eight radiomic features relevant to progression-free survival (PFS) were selected. Multivariate analysis showed that the visceral fat area/subcutaneous fat area ratio independently predicted PFS ( P = 0.040). Using the data for body composition, radiomic features, and integrated features, nomograms were established for the training (areas under the curve = 0.647, 0.736, and 0.803, respectively) and the validation sets (areas under the receiver operating characteristic = 0.625, 0.723, and 0.866, respectively); the integrated model showed better prediction ability than that of the other 2 models. The calibration curves revealed that the integrated nomogram exhibited a better agreement between the estimation and the actual observation in terms of prediction of the probability of PFS than that of the other 2 models. Decision curve analysis revealed that the integrated nomogram was superior to the body composition and radiomics nomograms for predicting clinical benefit. Integration of data on body composition and PET/CT radiomic features can help in prediction of outcomes in patients with stage IV NSCLC.

A large quantity of ischemic stroke events occur in patients hospitalized for non-stroke-related reason. No scale has been developed to identify the large vessel occlusion (LVO) among inpatient stroke alerts. We aimed to develop a novel evaluation scale to predict LVO from in-hospital stroke alerts. Data from consecutive in-hospital stroke alerts were analyzed at a single high volume stroke center between January 2016 and October 2020. We developed a predictive scale based on the first half of patients (training group) using multivariate logistic regression and evaluated it in the remaining half of patients (validation group) adopting receiver operating curve. Receiver operating characteristics of the scale were analyzed to evaluate its value for the detection of LVO. A total of 243 patients were enrolled for further study, among them, 94 (38.7%) had confirmed LVO. Three risk factors independently predicted the presence of LVO: recent cardiac or pulmonary procedure (1 point), neurological deficit scale (≥1: 2 points), and history of atrial fibrillation (1 point). The CAPS scale was generated based on predictive factors and demonstrated highly effective discrimination in identifying the presence of LVO in the training group (area under curve = 0.956) and the validation group (area under curve = 0.940). When the score ≥2, CAPS scale showed 97.9% sensitivity, 79.2% specificity, 74.8% positive predictive value, and 98.3% negative predictive value for discriminating LVO. CAPS scale was developed for identifying LVO among inpatient stroke alerts with high sensitivity and specificity, which may help to quickly prompt responses by appropriate stroke teams.

We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners. We enrolled 402 patients who underwent noncontrast CT examinations, including L1-L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMDDL and TBSDL were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMDDL and BMD, and TBSDL and TBS. The diagnostic performance of BMDDL for osteopenia/osteoporosis and that of TBSDL for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis. BMDDL and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBSDL and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMDDL for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBSDL for identifying patients with bone microarchitecture impairment were 73% for all values. The BMDDL and TBSDL derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.

To quantitatively measure femoral bone marrow involvement in patients with Gaucher disease (GD) by using fat fraction (FF) derived from the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) technique. Bilateral femora of 23 patients with type 1 GD receiving low-dose imiglucerase treatment were prospectively scanned using structural magnetic resonance imaging sequences and an IDEAL-IQ sequence. Femoral bone marrow involvement was evaluated by both semiquantification (bone marrow burden [BMB] score based on magnetic resonance imaging structural images) and quantification (FF derived from IDEAL-IQ) methods. These patients were further divided into subgroups according to whether they underwent splenectomy or had bone complications. The interreader agreement of measurements and the correlation between FF and clinical status were statistically analyzed. In patients with GD, both BMB and FF evaluation of femora showed good interreader concordance (intraclass correlation coefficient = 0.98 and 0.99, respectively), and FF highly correlated with BMB score ( P < 0.001). The longer the duration of disease, the lower the FF ( P = 0.026). Femoral FF was lower in subgroups with splenectomy or bone complications than those without splenectomy or bone complications (0.47 ± 0.08 vs 0.60 ± 0.15, 0.51 ± 0.10 vs 0.61 ± 0.17, respectively, both P < 0.05). Femoral FF derived from IDEAL-IQ could be used to quantify femoral bone marrow involvement in patients with GD, and low bone marrow FF may predict worse outcomes of GD patients in this small-scale study.

To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer. Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADC max , ADC 90 , ADC median , ADC mean , ADC 10 , ADC min , ADC skewness , ADC kurtosis , and ADC entropy ) was compared between SS-EPI and RS-EPI and between the 2 groups. There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADC max and ADC entropy but lower ADC 10 , ADC min and ADC skewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC 90 and higher ADC kurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC 90 and higher ADC kurtosis and ADC entropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADC kurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%). Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADC kurtosis held great potential in differentiating normal-sized LNM in cervical cancer.

To investigate the incidence of severe iodinated contrast media (ICM)-related hypersensitivity reaction (HSR) and to find the optimal alternative ICM to reduce the risk of severe HSR recurrence. We retrospectively reviewed 23,383,183 cases of ICM administration between January 2015 and December 2019 from the nationwide health insurance database. We classified ICMs based on generic profiles and the presence of N-(2,3-dihydroxypropyl) carbamoyl side chains. The incidence of severe and recurrent severe HSRs was calculated, and χ2 tests were performed to compare the prevalence of severe HSR according to ICM groups. In addition, logistic regression analyses were used to assess differences between ICM groups. The incidence of severe HSRs was 1.92% (450,067 of 23,282,183). Among 1,875,245 individuals who received ICM twice on different days, severe HSR occurred in 40,850 individuals, and severe HSR recurred in 3319 individuals (8.12%). The risk of recurrence significantly decreased when the ICM changed (9.24% vs 7.08%, P < 0.001), especially when the ICM changed to one with a different side chain (6.74%, P < 0.001). In addition, compared with the reuse of the culprit agent, using combinations of iobitridol/iohexol (odds ratio [OR], 0.696; P = 0.04), iohexol/iopamidol (OR, 0.757; P = 0.007), iopamidol/iohexol (OR, 0.447; P < 0.001), and ioversol/iohexol (OR, 0.683; P = 0.04) reduced the risk of recurrence of severe HSR. Changing the culprit ICM to that with a different side chain can reduce severe HSR recurrence. The optimal choice of an alternative ICM depends on the causative agent.

This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram. This was a single-center retrospective study. A total of 110 patients were enrolled. The sample included 38 patients with low Ki67 expression (Ki67 ≤10%) and 72 with high Ki67 expression (Ki67 >10%) as demonstrated by surgical pathology. Patients were randomly divided into either a training (n = 77) or validation (n = 33) cohort. Diffusion-weighted imaging with apparent diffusion coefficient maps was used to extract radiomic features and the signal intensity values of tumor (SI tumor ), normal liver (SI liver ), and background noise (SI background ) from all samples. Subsequently, the clinical model, radiomic model, and fusion model (with clinical data and radiomic signature) were developed and validated. The area under the curve (AUC) of the clinical model for predicting the Ki67 expression including serum α-fetoprotein level ( P = 0.010), age ( P = 0.015), and signal noise ratio ( P = 0.026) was 0.799 and 0.715 in training and validation cohorts, respectively. The AUC of the radiomic model constructed by 9 selected radiomic features was 0.833 and 0.772 in training and validation cohorts, respectively. The AUC of the fusion model containing serum α-fetoprotein level ( P = 0.011), age ( P = 0.019), and rad score ( P < 0.001) was 0.901 and 0.781 in training and validation cohorts, respectively. Diffusion-weighted imaging as a quantitative imaging biomarker can predict Ki67 expression level in hepatocellular carcinoma across various models.

The aim of the study is to investigate the role of whole-body magnetic resonance imaging (MRI) in assessing extrapulmonary metastases in primary osteosarcoma staging. We retrospectively reviewed medical data to identify primary osteosarcoma patients with available preoperative whole-body MRI obtained in the staging or restaging. Histopathology was the reference test for assessing the diagnostic performance, if available. Otherwise, oncology board decisions were used as the reference. In addition, the benefits of whole-body MRI to F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) and bone scintigraphy were investigated. In all, 36 patients with osteosarcoma (24 staging, 12 restaging) with a mean age of 16.36 ± 5.63 years (range, 9-29 years) were included in the study. The median follow-up duration was 26.61 months (interquartile range, 33.3 months). Of 36 patients, 8 had skeletal, 1 had a lymph node, and 1 had a subcutaneous metastasis. Whole-body MRI correctly identified all patients with metastatic disease but incorrectly classified a bone infarct in one patient as a skeletal metastasis, equating a scan-level sensitivity, specificity, accuracy, negative predictive value, and positive predictive value of 100%, 96.3%, 97.3%, 100%, and 90.91%. Whole-body MRI contributed to bone scintigraphy by identifying a skeletal metastasis in one patient and positron emission tomography-computed tomography by ruling out a skeletal metastasis in another. Whole-body MRI could accurately identify extrapulmonary metastases in primary osteosarcoma patients for staging or restaging. In addition, it might contribute to the standard whole-body imaging methods.

The aim of the study is to investigate the performance of coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) in the same patient evaluated by different systolic and diastolic scans, aiming to explore whether 320-slice CT scanning acquisition protocol has an impact on CT-FFR value. One hundred forty-six patients with suspected coronary artery stenosis who underwent CCTA examination were included into the study. The prospective electrocardiogram gated trigger sequence scan was performed and electrocardiogram editors selected 2 optimal phases of systolic phase (preset collection trigger at 25% of R-R interval) and diastolic phase (preset collection trigger at 75% of R-R interval) for reconstruction. The lowest CT-FFR value (the CT-FFR value at the distal end of each vessel) and the lesion CT-FFR value (at 2 cm distal to the stenosis) after coronary artery stenosis were calculated for each vessel. The difference of CT-FFR values between the 2 scanning techniques was compared using paired Wilcoxon signed-rank test. Pearson correlation value and Bland-Altman were performed to evaluate the consistency of CT-FFR values. A total of 366 coronary arteries from the remaining 122 patients were analyzed. There was no significant difference regarding the lowest CT-FFR values between systole phase and diastole phase across all vessels. In addition, there was no significant difference in the lesion CT-FFR value after coronary artery stenosis between systole phase and diastole phase across all vessels. The CT-FFR value between the 2 reconstruction techniques had excellent correlation and minimal bias in all groups. The correlation coefficient of the lesion CT-FFR values for left anterior descending branch, left circumflex branch, and right coronary artery were 0.86, 0.84, and 0.76, respectively. Coronary computed tomography angiography-derived fractional flow reserve based on artificial intelligence deep learning neural network has stable performance, is not affected by the acquisition phase technology of 320-slice CT scan, and has high consistency with the evaluation of hemodynamics after coronary artery stenosis.