A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.

PURPOSE: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP). METHODS: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated. RESULTS: A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001). CONCLUSION: The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.

Smart responsive Fe/Mn nanovaccine triggers liver cancer immunotherapy via pyroptosis and pyroptosis-boosted cGAS-STING activation.

BACKGROUND: The prognosis for hepatocellular carcinoma (HCC) remains suboptimal, characterized by high recurrence and metastasis rates. Although metalloimmunotherapy has shown potential in combating tumor proliferation, recurrence and metastasis, current apoptosis-based metalloimmunotherapy fails to elicit sufficient immune response for HCC. RESULTS: A smart responsive bimetallic nanovaccine was constructed to induce immunogenic cell death (ICD) through pyroptosis and enhance the efficacy of the cGAS-STING pathway. The nanovaccine was composed of manganese-doped mesoporous silica as a carrier, loaded with sorafenib (SOR) and modified with MIL-100 (Fe), where Fe3+, SOR, and Mn2+ were synchronized and released into the tumor with the help of the tumor microenvironment (TME). Afterward, Fe3+ worked synergistically with SOR-induced immunogenic pyroptosis (via both the classical and nonclassical signaling pathways), causing the outflow of abundant immunogenic factors, which contributes to dendritic cell (DC) maturation, and the exposure of double-stranded DNA (dsDNA). Subsequently, the exposed dsDNA and Mn2+ jointly activated the cGAS-STING pathway and induced the release of type I interferons, which further led to DC maturation. Moreover, Mn2+-related T1 magnetic resonance imaging (MRI) was used to visually evaluate the smart response functionality of the nanovaccine. CONCLUSION: The utilization of metallic nanovaccines to induce pyroptosis-mediated immune activation provides a promising paradigm for HCC treatment.

Potentiating dual-directional immunometabolic regulation with nanomedicine to enhance anti-tumor immunotherapy following incomplete photothermal ablation.

Photothermal therapy (PTT) is a promising cancer treatment method due to its ability to induce tumor-specific T cell responses and enhance therapeutic outcomes. However, incomplete PTT can leave residual tumors that often lead to new metastases and decreased patient survival in clinical scenarios. This is primarily due to the release of ATP, a damage-associated molecular pattern that quickly transforms into the immunosuppressive metabolite adenosine by CD39, prevalent in the tumor microenvironment, thus promoting tumor immune evasion. This study presents a photothermal nanomedicine fabricated by electrostatic adsorption among the Fe-doped polydiaminopyridine (Fe-PDAP), indocyanine green (ICG), and CD39 inhibitor sodium polyoxotungstate (POM-1). The constructed Fe-PDAP@ICG@POM-1 (FIP) can induce tumor PTT and immunogenic cell death when exposed to a near-infrared laser. Significantly, it can inhibit the ATP-adenosine pathway by dual-directional immunometabolic regulation, resulting in increased ATP levels and decreased adenosine synthesis, which ultimately reverses the immunosuppressive microenvironment and increases the susceptibility of immune checkpoint blockade (aPD-1) therapy. With the aid of aPD-1, the dual-directional immunometabolic regulation strategy mediated by FIP can effectively suppress/eradicate primary and distant tumors and evoke long-term solid immunological memory. This study presents an immunometabolic control strategy to offer a salvage option for treating residual tumors following incomplete PTT.

Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.

OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT. METHODS: This retrospective study included 1049 patients with 1082 lesions from two independent hospitals that were pathologically confirmed as HCC or non-HCC. All patients underwent a four-phase CT imaging protocol. All lesions were graded (LR 4/5/M) by radiologists and divided into an internal (n = 886) and external cohort (n = 196) based on the examination date. In the internal cohort, Swin-Transformer based on different CT protocols were trained and tested for their ability to LI-RADS grading and distinguish HCC from non-HCC, and then validated in the external cohort. We further developed a combined model with the optimal protocol and clinical information for distinguishing HCC from non-HCC. RESULTS: In the test and external validation cohorts, the three-phase protocol without pre-contrast showed κ values of 0.6094 and 0.4845 for LI-RADS grading, and its accuracy was 0.8371 and 0.8061, while the accuracy of the radiologist was 0.8596 and 0.8622, respectively. The AUCs in distinguishing HCC from non-HCC were 0.865 and 0.715 in the test and external validation cohorts, while those of the combined model were 0.887 and 0.808. CONCLUSION: The Swin-Transformer based on three-phase CT protocol without pre-contrast could feasibly simplify LI-RADS grading and distinguish HCC from non-HCC. Furthermore, the DL model have the potential in accurately distinguishing HCC from non-HCC using imaging and highly characteristic clinical data as inputs. CLINICAL RELEVANCE STATEMENT: The application of deep learning model for multiphase CT has proven to improve the clinical applicability of the Liver Imaging Reporting and Data System and provide support to optimize the management of patients with liver diseases. KEY POINTS: • Deep learning (DL) simplifies LI-RADS grading and helps distinguish hepatocellular carcinoma (HCC) from non-HCC. • The Swin-Transformer based on the three-phase CT protocol without pre-contrast outperformed other CT protocols. • The Swin-Transformer provide help in distinguishing HCC from non-HCC by using CT and characteristic clinical information as inputs.

Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study.

PURPOSE: To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth. MATERIAL AND METHODS: Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (≥ 50%, ≥ 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results. RESULTS: 296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis ≥ 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis ≥ 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis ≥ 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis ≥ 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion. CONCLUSIONS: CerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance.

Antithrombotic Therapy by Regulating the ROS-Mediated Thrombosis Microenvironment and Specific Nonpharmaceutical Thrombolysis Using Prussian Blue Nanodroplets.

In thrombotic diseases, the effects of reactive oxygen species (ROS)-mediated oxidative stress as a "perpetrator" in thrombosis must be resolved. Accordingly, an insufficient understanding of thrombus therapy prompted the authors to pursue a more comprehensive and efficient antithrombotic treatment strategy. A Prussian blue (PB)-based nanodroplet system (PB-PFP@PC) is designed using PB and perfluorinated pentane (PFP) in the core, and a targeting peptide (CREKA, Cys-Arg-Glu-Lys-Ala) is attached to poly(lactic-coglycolic acid) (PLGA) as the delivery carrier shell. Upon near-infrared (NIR) laser irradiation, PB and PFP jointly achieve an unprecedented dual strategy for drug-free thrombolysis: photothermal therapy (PTT) combined with optical droplet vaporization (ODV). PB, a nanoenzyme, also regulates the vascular microenvironment via its antioxidant activity to continuously scavenge abnormally elevated ROS and correspondingly reduce inflammatory factors in the thrombus site. This study provides a demonstration of not only the potential of ODV in thrombus therapy but also the mechanism underlying PTT thrombolysis due to thermal ablation-induced fibrin network structural damage. Moreover, PB catalyzes ROS to generate oxygen (O2 ), which combines with the ODV effect, enhancing the ultrasound signal. Thus, regulation of the thrombosis microenvironment combined with specific nonpharmaceutical thrombolysis by PB nanodroplets provides a more comprehensive and efficient antithrombotic therapeutic strategy.

Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography.

OBJECTIVE: The aim of this study was to investigate the effects of plaque-related factors on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS). METHODS: Patients who underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) were retrospectively included in this study. The degree of stenosis in each vessel was collected from CCTA and ICA, and the information on plaque-related factors (plaque length, plaque type, and coronary artery calcium score (CAC)) of the vessels with plaques was collected from CCTA. RESULTS: In total, 1224 vessels in 306 patients (166 men; 65.7 ± 10.1 years) were analyzed. Of these, 391 vessels in 249 patients showed significant stenosis using ICA as the gold standard. Using per-vessel as the unit, the area under the curves of coronary stenosis ≥ 50% for AI-CADS, doctor, and AI-CADS + doctor was 0.764, 0.837, and 0.853, respectively. The accuracies in interpreting the degree of coronary stenosis were 56.0%, 68.1%, and 71.2%, respectively. Seven hundred fifty vessels showed plaques on CCTA; plaque type did not affect the interpretation results by AI-CADS (chi-square test: p = 0.0093; multiple logistic regression: p = 0.4937). However, the interpretation results for plaque length (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0061) and CACs (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0001) were significantly different. CONCLUSION: AI-CADS has an ability to distinguish ≥ 50% coronary stenosis, but additional manual interpretation based on AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS. KEY POINTS: • AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence. • Additional manual interpretation on the basis of AI-CADS is necessary. • The plaque length and CACs will affect the diagnostic performance of AI-CADS.

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard. METHODS: Patients with lumbar VCFs were retrospectively enrolled from January 2011 to May 2020. All patients underwent DR and MRI scanning. VCFs were categorized as fresh or old according to MRI results, and the VCF grade and type were assessed. The raw DR data were sent to InferScholar Center for annotation. A DL-based prediction model was built, and its diagnostic performance was evaluated. The DeLong test was applied to assess differences in ROC curves between different models. RESULTS: A total of 1877 VCFs in 1099 patients were included in our study and randomly divided into development (n = 824 patients) and test (n = 275 patients) datasets. The ensemble model identified fresh and old VCFs, reaching an AUC of 0.80 (95% confidence interval [CI], 0.77-0.83), an accuracy of 74% (95% CI, 72-77%), a sensitivity of 80% (95% CI, 77-83%), and a specificity of 68% (95% CI, 63-72%). Lateral (AUC, 0.83) views exhibited better performance than anteroposterior views (AUC, 0.77), and the best performance among respective subgroupings was obtained for grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups. CONCLUSION: The proposed DL model achieved adequate performance in identifying fresh VCFs from DR. KEY POINTS: • The ensemble deep-learning model identified fresh VCFs from DR, reaching an AUC of 0.80, an accuracy of 74%, a sensitivity of 80%, and a specificity of 68% with the reference standard of MRI. • The lateral views (AUC, 0.83) exhibited better performance than anteroposterior views (AUC, 0.77). • The grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups showed the best performance among their respective subgroupings.

Preoperative estimation of the survival of patients with unresectable hepatocellular carcinoma achieving complete response after conventional transcatheter arterial chemoembolization: assessments of clinical and LI-RADS MR features.

PURPOSE: To identify the associations of clinical and magnetic resonance (MR) features with overall survival (OS) in patients with unresectable hepatocellular carcinoma (HCC) achieving complete response (CR) after conventional transcatheter arterial chemoembolization (TACE) and to further develop an individual nomograph to estimate the survival probability. MATERIALS AND METHODS: A total of 112 patients with unresectable HCC treated with TACE as first-line treatment were retrospectively evaluated. Potential risk factors associated with OS were identified by univariate and multivariate Cox analyses. The survival model was developed by multivariate Cox proportional hazard model. The area under the receiver operating characteristic curve was calculated to assess the performance of each marker and of the whole model. Discrimination was performed using Kaplan-Meier curves, and the survival curves were compared by the log-rank test. A nomogram derived from the survival model was established. RESULTS: Multivariate Cox analyses indicated that nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and Barcelona clinic liver cancer (BCLC) stage were independent risk indicators associated with OS. The survival model showed acceptable diagnostic power, with an area under the curve (AUC) of 0.687. Kaplan-Meier curves demonstrated that the model discriminated well, as the high-risk and low-risk groups had median survival times of 21.6 months and 34.8 months, respectively (log-rank test, P = 0.01). CONCLUSIONS: Nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and BCLC stage were potential biomarkers to evaluate the survival with favorable performance and discriminate HCC patients with CR under conventional TACE treatment.

Individualized Prediction of Early Alzheimer's Disease Based on Magnetic Resonance Imaging Radiomics, Clinical, and Laboratory Examinations: A 60-Month Follow-Up Study.

BACKGROUND: Accurately predicting whether and when mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is of vital importance to help developing individualized treatment plans to defer the occurrence of irreversible dementia. PURPOSE: To develop and validate radiomics models and multipredictor nomogram for predicting the time to progression (TTP) from MCI to AD. STUDY TYPE: Retrospective. POPULATION: One hundred sixty-two MCI patients (96 men and 66 women [median age, 72; age range, 56-88 years]) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. FIELD STRENGTH/SEQUENCE: T1 -weighted imaging and T2 -weighted fluid-attenuation inversion recovery imaging acquired at 3.0 T. ASSESSMENT: During the 5-year follow-up, 68 patients converted to AD and 94 remained stable. Patients were randomly divided into the training (n = 112) and validation datasets (n = 50). Radiomic features were extracted from the whole cerebral cortex and subcortical nucleus of MR images. A radiomics model was established using least absolute shrinkage and selection operator (LASSO) Cox regression. The clinical-laboratory model and radiomics-clinical-laboratory model were developed by multivariate Cox proportional hazard model. The performance of each model was assessed by the concordance index (C-index). A multipredictor nomogram derived from the radiomics-clinical-laboratory model was constructed for individualized TTP estimation. STATISTICAL TESTS: LASSO cox regression, univariate and multivariate Cox regression, Kaplan-Meier analysis and Student's t test were performed. RESULTS: The C-index of the radiomics, clinical-laboratory and radiomics-clinical-laboratory models were 0.924 (95% confidence interval [CI]: 0.894-0.952), 0.903 (0.868-0.938), 0.950 (0.929-0.971) in the training cohort and 0.811 (0.707-0.914), 0.901 (0824-0.977), 0.907 (0.836-0.979) in the validation cohort, respectively. A multipredictor nomogram with 15 predictors was established, which had high accuracy for individual TTP prediction with the C-index of 0.950 (0.929-0.971). DATA CONCLUSION: The prediction of individual TTP from MCI to AD could be accurately conducted using the radiomics-clinical-laboratory model and multipredictor nomogram. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 2.

A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage.

OBJECTIVES: To develop and validate a noncontrast computed tomography (NCCT)-based clinical-radiomics nomogram to identify spontaneous intracerebral hemorrhage (sICH) patients with a poor 90-day prognosis on admission. METHODS: In this double-center retrospective study, data from 435 patients with sICH (training cohort: n = 244; internal validation cohort: n = 104; external validation cohort: n = 87) were reviewed. The radiomics score (Rad-score) was calculated based on the coefficients of the selected radiomics features. A clinical-radiomics nomogram was developed by using independent predictors of poor outcome at 90 days through multivariate logistic regression analysis in the training cohort and was validated in the internal and external cohorts. RESULTS: At 90 days, 200 of 435 (46.0%) patients had a poor prognosis. The clinical-radiomics nomogram was developed by six independent predictors namely midline shift, NCCT time from sICH onset, Glasgow Coma Scale score, serum glucose, uric acid, and Rad-score. In identifying patients with poor prognosis, the clinical-radiomics nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.81 in the training cohort, an AUC of 0.78 in the internal validation cohort, and an AUC of 0.73 in the external validation cohort. The calibration curve revealed that the clinical-radiomics nomogram showed satisfactory calibration in the training and internal validation cohorts (both p > 0.05), but slightly poor agreement in the external validation cohort (p < 0.05). CONCLUSIONS: The clinical-radiomics nomogram is a valid computer-aided tool that may provide personalized risk assessment of 90-day functional outcome for sICH patients. KEY POINTS: • The proposed Rad-score was significantly associated with 90-day poor functional outcome in patients with sICH. • The clinical-radiomics nomogram showed satisfactory calibration and the most net benefit for discriminating 90-day poor outcome. • The clinical-radiomics nomogram may provide personalized risk assessment of 90-day functional outcome for sICH patients.

The role of chest CT in management of asymptomatic SARS-CoV-2 infections: A longitudinal multi-center study in Chongqing, China.

Background: Multiple societies including the Fleischner Society do not recommend that CT is routinely used in asymptomatic SARS-CoV-2 infections; however, this advice is based on the limited evidence. In this study, we aim to confirm whether it is necessary to do CT scans in SARS-CoV-2 asymptomatic infections by summarizing the longitudinal chest CT and clinical features of asymptomatic SARS-CoV-2 infections. Methods: A total of 33 individuals (14 men and 19 women) with asymptomatic SARS-CoV-2 infections were retrospectively enrolled. Clinical data of CT positive and negative groups were compared. Longitudinal chest CT scans were reviewed for CT features and analyzed for temporal change. Results: Thirty-two (97%) individuals had positive results for first RT-PCR testing. For clinical data, only monocyte count showed a significant difference between CT positive and negative groups. For first chest CT, only eighteen (54.5%) individuals had abnormal manifestations, common CT features were GGO (88.9%) and consolidation (33.3%), the median number of segments involved was 3.0 (1.0-7.5). No case in CT negative group was abnormal on the follow-up CT. Three patterns of evolution throughout series of CT were observed in CT positive group, including gradual improvement (12, 66.7%), mismatch to improvement (3, 16.7%) and mild progression to improvement (3, 16.7%). On last CT scans, most cases had radiographic improvement but residual abnormalities. Significant differences were exhibited in density, long diameter, number of lung segments involved, and percentage of consolidation between the first and last CT scans. All cases had stable conditions and finally confirmed negative for SARS-CoV-2 RT-PCR tests without developing into severe pneumonia. Conclusion: Considering poor performance of CT in screening, stable conditions during followup, and good outcomes in asymptomatic SARS-CoV-2 infections, we confirm that it is unnecessary to do CT scans in asymptomatic SARS-CoV-2 infections.

MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization.

BACKGROUND: Preoperative estimation of hepatocellular carcinoma (HCC) recurrence after conventional transcatheter arterial chemoembolization (c-TACE) is crucial for subsequent follow-up and therapy decisions. PURPOSE: To evaluate the associations of radiomics models based on pretreatment contrast-enhanced MRI, a clinical-radiological model and a combined model with the recurrence-free survival (RFS) of patients with HCC after c-TACE, and to develop a radiomics nomogram for individual RFS estimations and risk stratification. STUDY TYPE: Retrospective. POPULATION: In all, 184 consecutive HCC patients. FIELD STRENGTH/SEQUENCE: 1.5T or 3.0T, including T2 WI, T1 WI, and contrast-enhanced T1 WI. ASSESSMENT: All HCC patients were randomly divided into the training (n = 110) and validation datasets (n = 74). Radiomics signatures capturing intratumoral and peritumoral expansion (1, 3, and 5 mm) were constructed, and the radiomics models were set up using least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical-radiological features were identified by univariate and multivariate Cox regression. The clinical-radiological model and the combined model fusing the radiomics signature with the clinical-radiological risk factors were developed by a multivariate Cox proportional hazard model. A radiomics nomogram derived from the combined model was established. STATISTICAL TESTS: LASSO Cox regression, univariate and multivariate Cox regression, Kaplan-Meier analysis were performed. The discrimination performance of each model was quantified by the C-index. RESULTS: Among the different peritumoral expansion models, only the 3-mm peritumoral expansion model (C-index, 0.714) showed a comparable performance (P = 0.4087) to that of the portal venous phase intratumoral model (C-index, 0.727). The combined model showed the best performance and the C-index was 0.802. Kaplan-Meier analysis showed that the cutoff values of the combined model relative to a median value (1.7426) perfectly stratified these patients into high-risk and low-risk subgroups. DATA CONCLUSION: The combined model is more valuable than the clinical-radiological model or radiomics model alone for evaluating the RFS of HCC patients after c-TACE, and the radiomics nomogram can be used to preoperatively and individually estimate RFS. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;52:461-473.

MRI-Based Radiomics Models Developed With Features of the Whole Liver and Right Liver Lobe: Assessment of Hepatic Inflammatory Activity in Chronic Hepatic Disease.

BACKGROUND: The noninvasive assessment of hepatic inflammatory activity (HIA) is crucial for making clinical decisions and monitoring therapeutic efficacy in chronic liver disease (CLD). PURPOSE: To develop MRI-based radiomics models by extracting features from the whole liver and localized regions of the right liver lobe, compare the efficiency of two radiomics models, and further develop a radiomics nomogram for the assessment of HIA in CLD. STUDY TYPE: Retrospective. POPULATION: In all, 137 consecutive patients. FIELD STRENGTH/SEQUENCE: 1.5T/T2 -weighted imaging. ASSESSMENT: All patients (nonsignificant HIA, n = 98; significant HIA, n = 39) were randomly divided into a training (n = 95) and a test cohort (n = 42). Radiomics features were extracted from the regions covering the whole liver (ROI-w) and localized regions of the right liver lobe (ROI-r). Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses were used to select features and develop radiomics models. A combined model fusing the valuable radiomics features with clinical-radiological predictors was developed. Finally, a radiomics nomogram derived from the combined model was developed. STATISTICAL TESTS: Synthetic minority oversampling technique algorithm, LASSO, receiver operator characteristic curve, and calibration curve analysis were performed. RESULTS: The area under the curve (AUC), sensitivity, and specificity of the ROI-w radiomics model in assessing HIA were 0.858, 0.800, and 0.733, respectively. The ROI-r model were 0.844, 0.733, and 0.867, respectively. No differences were detected between the two radiomics models (P = 0.8329). The combined model fusing valuable ROI-w radiomics features, albumin, and periportal edema exhibited a promising performance (AUC, 0.911). The calibration curves showed good agreement between the actual observations and nomogram predictions. DATA CONCLUSION: The MRI-based radiomics models had a powerful ability to evaluate HIA and the ROI-w radiomics model was comparable to the ROI-r model. Moreover, the radiomics nomogram could be a favorable method to individually estimate HIA in CLD. J. MAGN. RESON. IMAGING 2020;52:1668-1678.

Coronavirus disease 2019: initial chest CT findings.

OBJECTIVES: To systematically analyze CT findings during the early and progressive stages of natural course of coronavirus disease 2019 and also to explore possible changes in pulmonary parenchymal abnormalities during these two stages. METHODS: We retrospectively reviewed the initial chest CT data of 62 confirmed coronavirus disease 2019 patients (34 men, 28 women; age range 20-91 years old) who did not receive any antiviral treatment between January 21 and February 4, 2020, in Chongqing, China. Patients were assigned to the early-stage group (onset of symptoms within 4 days) or progressive-stage group (onset of symptoms within 4-7 days) for analysis. CT characteristics and the distribution, size, and CT score of pulmonary parenchymal abnormalities were assessed. RESULTS: In our study, the major characteristic of coronavirus disease 2019 was ground-glass opacity (61.3%), followed by ground-glass opacity with consolidation (35.5%), rounded opacities (25.8%), a crazy-paving pattern (25.8%), and an air bronchogram (22.6%). No patient presented cavitation, a reticular pattern, or bronchial wall thickening. The CT scores of the progressive-stage group were significantly greater than those of the early-stage group (p = 0.004). CONCLUSIONS: Multiple ground-glass opacities with consolidations in the periphery of the lungs were the primary CT characteristic of coronavirus disease 2019. CT score can be used to evaluate the severity of the disease. If these typical alterations are found, then the differential diagnosis of coronavirus disease 2019 must be considered. KEY POINTS: • Multiple GGOs with consolidations in the periphery of the lungs were the primary CT characteristic of COVID-19. • The halo sign may be a special CT feature in the early-stage COVID-19 patients. • Significantly increased CT score may indicate the aggravation of COVID-19 in the progressive stage.

Novel gadopentetic acid-doped silica nanoparticles conjugated with YPSMA-1 targeting prostate cancer for MR imaging: an in vitro study.

The early diagnosis of prostate cancer (PCa) is particularly important for reducing its high mortality rate. With the development of molecular magnetic resonance imaging (MRI), early diagnosis via non-invasive imaging has become possible. In this study, gadopentetic acid (GA)-doped silica (Gd@SiO2) was first synthesized by a reverse microemulsion method, and amino and carboxyl groups were then successively introduced onto the surface of this Gd@SiO2. After these steps, a monoclonal antibody (YPSMA-1) to prostate-specific membrane antigen (PSMA) was conjugated with carboxyl-modified Gd@SiO2 (Gd@SiO2-COOH) nanoparticles (NPs) by the carbodiimide method. Gd@SiO2-Ab NPs were thus obtained as specific MR contrast agents for PCa-targeted imaging. Transmission electron microscopy showed that the Gd@SiO2-Ab NPs exhibited a dispersed spherical morphology with a relatively uniform size distribution. The Gd@SiO2-Ab NPs showed high stability and high the longitudinal relaxation rate (r1). Cell-targeting experiments in vitro demonstrated the high potential of the synthesized NPs to target PSMA receptor-positive PCa cells. In vitro cytotoxicity assays showed that the Gd@SiO2-Ab NPs exhibited good biological safety. These results suggest that the synthesized Gd@SiO2-Ab NPs have great potential as specific MR contrast agents for PSMA receptor-positive PCa cells.

[Preparation of a glypican-3-targeting hepatocellular carcinoma MR probe and its molecular imaging in HepG2 cells].

OBJECTIVE: To prepare a glypican-3 (GPC3)-targeting hepatocellular carcinoma MR molecular probe and to evaluate its targeting specificity using HepG2 cells. METHODS: Poly(lactic-co-glycolic acid) (PLGA) nanoparticles were prepared by a double emulsion solvent evaporation method, and the surfaces were connected with anti-GPC3 mono-antibody and paramagnetic substance Gd3+. The physical properties of the probes were investigated using fluorescence microscopy, electron microscopy, Malvern particle size analysis, inductively coupled plasma atomic emission spectroscopy (ICP-AES) and 1.5T MR imaging. The specificity of the probes to target cultured HepG2 cells was determined by laser confocal microscopy. The signal characteristics, including signal-to-noise ratio (SNR), after co-incubation with HepG2 cells were analyzed by 1.5T MR imaging. Significance of differences between multiple groups (target group, non-target group, and control group) was assessed by one-way analysis of variance, and between two groups was assessed by the LSD-t test. A difference was considered to be statistically significant at P less than 0.05. RESULTS: The GPC3-targeting hepatocellular carcinoma MR molecular probes were successfully prepared. The nanoparticles had a spherical shape, size of 495 +/- 17.5 nm, uniform size distribution, good dispersibility, no obvious aggregation, and could significantly increase the T1 signal. Using the ICP-AES measurement, 1 mol PLGA carried about 12 mol Gd3+, and as the Gd3+ concentration increased, the T1 signal increased. The prepared MR molecular probes could specifically target HepG2 cells, and could increase the T1 signal. The SNR value of the target group was 3.45 +/- 0.21, of the non-target group was 1.43 +/- 0.07, and of the control group was 1.12 +/- 0.03. The SNR value of the target group was significantly higher than that of the non-target group and the control group (P less than 0.05); there was no significant difference between the non-target group and the control group (P more than 0.05). CONCLUSION: PLGA nanoparticles, anti-GPC3 mono-antibody and paramagnetic Gd3+ can be used to successfully prepare GPC3-targeting hepatocellular carcinoma MR molecular probes which are capable of specifically targeting HepG2 cells in vitro and being detected by 1.5T MR imaging. These MR molecular probes may represent a useful noninvasive imaging method for detecting early hepatocellular carcinoma in vivo.

Body physical parameters instead of water equivalent diameter to calculate size-specific dose estimate in adult chest CT.

This study aimed to investigate body physical parameters as substitutes for water equivalent diameter (Dw) while calculating size-specific dose estimates (SSDEs) during adult chest computed tomography (CT). A retrospective analysis was conducted on 776 patients. Patients were divided into training set (542 patients) and validation set (234 patients) according to a ratio of 7:3. The correlations between physical parameters and Dw were analyzed. The differences between SSDEsubstitutes and the reference SSDE (SSDEreference) were compared. Strong positive correlations were observed between body mass index (BMI) and Dw as well as between weight and Dw in overall, male, and female patients (all p < 0.001). The correlations between BMI and Dw were stronger than those between weight and Dw in overall, male, and female subjects (all p < 0.001). SSDEweight and SSDEBMI were not significantly different from SSDEreference (p > 0.05). The RMSEs of overall patients between SSDEweight and SSDEreference as well as between SSDEBMI and SSDEreference were 0.237 and 0.2, respectively. The use of sex-specific regression equations for BMI caused a slightly reduction in RMSE. Weight and BMI can be used as surrogate parameters for Dw when calculating SSDE in adult chest CT exams, with BMI being the preferred substitute parameter.

Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study.

PURPOSE: The purpose of this study was to investigate the added value of artificial intelligence (AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography (CTA). MATERIALS AND METHODS: Patients who underwent head and neck CTA examinations at two hospitals were retrospectively included. CTA examinations were randomized into group 1 (without AI-washout-with AI) and group 2 (with AI-washout-without AI), and six readers (two radiology residents, two non-neuroradiologists, and two neuroradiologists) independently interpreted each CTA examination without and with AI solutions. Additionally, reading time was recorded for each patient. Digital subtraction angiography was used as the standard of reference. The diagnostic performance for AS at lesion and patient levels with four AS thresholds (30 %, 50 %, 70 %, and 100 %) was assessed by calculating sensitivity, false-positive lesions index (FPLI), specificity, and accuracy. RESULTS: A total of 268 patients (169 men, 63.1 %) with a median age of 65 years (first quartile, 57; third quartile, 72; age range: 28-88 years) were included. At the lesion level, AI improved the sensitivity of all readers by 5.2 % for detecting AS ≥ 30 % (P < 0.001). Concurrently, AI reduced the FPLI of all readers and specifically neuroradiologists for detecting non-occlusive AS (all P < 0.05). At the patient level, AI improved the accuracy of all readers by 4.1 % (73.9 % [1189/1608] without AI vs. 78.0 % [1254/1608] with AI) (P < 0.001). Sensitivity for AS ≥ 30 % and the specificity for AS ≥ 70 % increased for all readers with AI assistance (P = 0.01). The median reading time for all readers was reduced from 268 s without AI to 241 s with AI (P< 0.001). CONCLUSION: AI-assisted diagnosis improves the performance of radiologists in detecting head and neck AS, and shortens reading time.

Total sleep deprivation alters spontaneous brain activity in medical staff during routine clinical work: a resting-state functional MR imaging study.

Objectives: To assess the effect of total sleep deprivation (TSD) on spontaneous brain activity in medical staff during routine clinical practice. Methods: A total of 36 medical staff members underwent resting-state functional MRI (rs-fMRI) scans and neuropsychological tests twice, corresponding to rested wakefulness (RW) after normal sleep and 24 h of acute TSD. The rs-fMRI features, including the mean fractional amplitude of low-frequency fluctuation (mfALFF), z-score transformed regional homogeneity (zReHo), and functional connectivity (zFC), were compared between RW and TSD. Correlation coefficients between the change in altered rs-fMRI features and the change in altered scores of neuropsychological tests after TSD were calculated. Receiver operating characteristic (ROC) and logistic regression analyses were performed to evaluate the diagnostic efficacy of significantly altered rs-fMRI features in distinguishing between RW and TSD states. Results: Brain regions, including right superior temporal gyrus, bilateral postcentral gyrus, left medial superior frontal gyrus, left middle temporal gyrus, right precentral gyrus, and left precuneus, showed significantly enhanced rs-fMRI features (mfALFF, zReHo, zFC) after TSD. Moreover, the changes in altered rs-fMRI features of the right superior temporal gyrus, bilateral postcentral gyrus, left middle temporal gyrus, and left precuneus were significantly correlated with the changes in several altered scores of neuropsychological tests. The combination of mfALFF (bilateral postcentral gyrus) and zFC (left medial superior frontal gyrus and left precuneus) showed the highest area under the curve (0.870) in distinguishing RW from TSD. Conclusion: Spontaneous brain activity alterations occurred after TSD in routine clinical practice, which might explain the reduced performances of these participants in neurocognitive tests after TSD. These alterations might be potential imaging biomarkers for assessing the impact of TSD and distinguishing between RW and TSD states.