Quantitative MR imaging biomarkers for distinguishing inflammatory pancreatic mass and pancreatic cancer-a systematic review and meta-analysis.

OBJECTIVES: To evaluate the diagnostic performance of quantitative magnetic resonance (MR) imaging biomarkers in distinguishing between inflammatory pancreatic masses (IPM) and pancreatic cancer (PC). METHODS: A literature search was conducted using PubMed, Embase, the Cochrane Library, and Web of Science through August 2023. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the risk of bias and applicability of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the DerSimonian-Laird method. Univariate meta-regression analysis was used to identify the potential factors of heterogeneity. RESULTS: Twenty-four studies were included in this meta-analysis. The two main types of IPM, mass-forming pancreatitis (MFP) and autoimmune pancreatitis (AIP), differ in their apparent diffusion coefficient (ADC) values. Compared with PC, the ADC value was higher in MFP but lower in AIP. The pooled sensitivity/specificity of ADC were 0.80/0.85 for distinguishing MFP from PC and 0.82/0.84 for distinguishing AIP from PC. The pooled sensitivity/specificity for the maximal diameter of the upstream main pancreatic duct (dMPD) was 0.86/0.74, with a cutoff of dMPD ≤ 4 mm, and 0.97/0.52, with a cutoff of dMPD ≤ 5 mm. The pooled sensitivity/specificity for perfusion fraction (f) was 0.82/0.68, and 0.82/0.77 for mass stiffness values. CONCLUSIONS: Quantitative MR imaging biomarkers are useful in distinguishing between IPM and PC. ADC values differ between MFP and AIP, and they should be separated for consideration in future studies. CLINICAL RELEVANCE STATEMENT: Quantitative MR parameters could serve as non-invasive imaging biomarkers for differentiating malignant pancreatic neoplasms from inflammatory masses of the pancreas, and hence help to avoid unnecessary surgery. KEY POINTS: • Several quantitative MR imaging biomarkers performed well in differential diagnosis between inflammatory pancreatic mass and pancreatic cancer. • The ADC value could discern pancreatic cancer from mass-forming pancreatitis or autoimmune pancreatitis, if the two inflammatory mass types are not combined. • The diameter of main pancreatic duct had the highest specificity for differentiating autoimmune pancreatitis from pancreatic cancer.

Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.

PURPOSE: We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS: Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS: Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION: The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.

[Intraspinal Metastasis of Thymic Carcinoma:Report of One Case].

Intraspinal metastasis from malignant carcinomas in other body parts is rarely reported.Intraspinal metastases are often epidural,with primary tumors mostly from the lung and prostate.The extramedullary subdural metastasis of thymic carcinoma is particularly rare and prone to misdiagnosis due to overlapping imaging features with primary intraspinal tumors.This article reports one case of intraspinal metastasis of thymic carcinoma,with the main diagnostic clues including a history of thymic carcinoma,fast growth rate,and irregular shape.

Association between Anatomical Variations of the Circle of Willis and Covert Vascular Brain Injury in the General Population.

BACKGROUND AND PURPOSE: The circle of Willis (COW) is a circulatory anastomosis located at the base of the brain. Little is known about the association between covert vascular brain injury and COW configurations in the general population. We explored this relationship in a community-based Chinese sample. METHODS: A total of 1,055 patients (mean age, 54.8 ± 8.9 years; 36.0% men) without intracranial arterial stenosis were included in the analysis. Magnetic resonance imaging was performed to evaluate the presence of imaging markers of covert vascular brain injury, including white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), enlarged perivascular spaces, and brain atrophy. Magnetic resonance angiography was used to classify the COW configurations according to the completeness, symmetry, and presence of the fetal posterior cerebral artery (FTP). The association between vascular lesions and variations in COW was analyzed. RESULTS: Among the 1,055 patients, 104 (9.9%) had a complete COW. Completeness correlated with age (p = 0.001). Incomplete COW was positively associated with WMH severity (OR = 2.071; 95% CI, 1.004-4.270) and CMB presence (OR = 1.542; 95% CI, 1.012-2.348), independent of age and sex. The presence of FTP was associated with lacunes (OR = 1.878; 95% CI, 1.069-3.298), more severe WMHs (OR = 1.739; 95% CI, 1.064-2.842), and less severe enlarged perivascular spaces (OR = 0.562; 95% CI, 0.346-0.915). CONCLUSIONS: COW configuration was significantly related to various covert vascular brain injuries.

Coronary Artery Stent Evaluation by CTA: Impact of Deep Learning Reconstruction and Subtraction Technique.

BACKGROUND. Coronary CTA with hybrid iterative reconstruction (HIR) is prone to false-positive results for in-stent restenosis due to stent-related blooming artifact. OBJECTIVE. The purpose of this study is to assess the impact of deep learning reconstruction (DLR), subtraction images, and the combination of DLR and subtraction images on the diagnostic performance of coronary CTA for the detection of in-stent restenosis. METHODS. This prospective study included patients with coronary stents who underwent coronary CTA between March 2020 and August 2021. CTA used a technique with two breath-holds (noncontrast and contrast-enhanced acquisitions). Conventional and subtraction images were reconstructed for HIR and DLR. The maximum visible instent lumen diameter was measured. Two readers independently evaluated images for in-stent restenosis (≥ 50% stenosis). A simulated assessment of combined conventional and subtraction images was generated, reflecting assessment of conventional and subtraction images in the presence or absence of severe misregistration artifact, respectively. Invasive angiography served as reference standard. RESULTS. The study enrolled 30 patients (22 men and eight women; mean age, 63.6 ± 7.4 [SD] years) with a total of 59 stents; severe misregistration artifact was present for 32 stents. Maximum visible in-stent lumen diameter was higher for DLR than for HIR (2.3 ± 0.5 vs 2.1 ± 0.5 mm, p < .001), and among stents without severe misregistration artifact, it was higher for subtraction than conventional DLR (3.0 ± 0.5 vs 2.4 ± 0.5, p < .001). Among conventional CTA with HIR, conventional CTA with DLR, combination (conventional and subtraction) approach with HIR, and combination (conventional and subtraction) approach with DLR, the highest patient-level diagnostic performance measures were as follows: for reader 1, sensitivity was identical (62.5%), specificity was highest for combination with DLR (90.1%), PPV was highest for combination with DLR (71.4%), NPV was highest for combination with DLR (87.0%), and accuracy was highest for combination with DLR (83.3%); for reader 2, sensitivity was identical (50.0%), specificity was highest for combination with HIR or DLR (both 95.5%), PPV was highest for combination with HIR or DLR (both 80.0%), NPV was highest for combination with HIR or DLR (84.0%), and accuracy was highest for combination with HIR or DLR (both 83.3%). CONCLUSION. The combined DLR and subtraction technique yielded optimal diagnostic performance for detecting in-stent restenosis by coronary CTA. CLINICAL IMPACT. The described technique could guide patient selection for invasive coronary stent evaluation.

Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer.

OBJECTIVE: To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. MATERIAL AND METHODS: We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated. RESULTS: The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000). CONCLUSION: Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.

[Quality of Images Reconstructed by Deep Learning Reconstruction Algorithm for Head and Neck CT Angiography at 100 kVp].

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.

[CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer].

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

[Quality Evaluation of Temporomandibular Joint Images from 3 High-resolution Dynamic Sequences].

Objective To compare the image quality of three high-resolution dynamic MRI methods for evaluating the motion of temporomandibular joint disc and condyle. Methods Twenty-five patients with suspected temporomandibular joint disorders were examined by single-shot fast spin-echo (SSFSE),fast imaging employing steady-state acquisition (FIESTA),and spoiled gradient echo (SPGR) on the oblique sagittal position.Two radiologists performed subjective and objective evaluation on the images with double-blind method.The subjective evaluation included the signal intensity of mandibular condyle,articular disc,soft tissue around articular disc,and lateral pterygoid muscle,the contrast between articular disc and condyle,the contrast between articular disc and surrounding soft tissue,condylar motion,and disc movement.The objective evaluation indexes included image signal intensity,signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR).The subjective and objective indexes of the image quality were compared between the three sequences. Results The SSFSE sequence had lower signal intensity of articular disc and higher signal intensity of condyle and surrounding soft tissue than FIESTA and SPGR sequences (all P<0.001).The SPGR sequence showed higher signal intensity of lateral pterygoid muscle than the SSFSE and FIESTA sequences (P=0.017,P<0.001).Among the three sequences,SSFSE sequence showed the clearest articular disc structure (χ2=41.952,P<0.001),the strongest contrast between articular disc and condyle (χ2=35.379,P<0.001),the strongest contrast between articular disc and surrounding soft tissue (χ2=27.324,P<0.001),and the clearest movement of articular disc (χ2=44.655,P<0.001).SSFSE and FIESTA sequences showed higher proportion of disc displacement and reduction than SPGR sequence (all P<0.001).The CNR (χ2=21.400,P<0.001),SNR (χ2=34.880,P<0.001),and condyle signal intensity (F=337.151,P<0.001) demonstrated differences among SSFSE,FIESTA,and SPGR sequences.The CNR of SSFSE sequence was higher than that of FIESTA sequence (P<0.001),while it had no significant difference between SSFSE and SPGR sequences (P=0.472).In addition,the SSFSE sequence had higher SNR and signal intensity than FIESTA and SPGR sequences (all P<0.001). Conclusion The best image quality can be observed from SSFSE sequence where both the structure and movement of temporomandibular joint are well displayed.Therefore,SSFSE is preferred for the examination of temporomandibular joint movement.

The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values.

OBJECTIVES: To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFRML) values. METHODS: Thirty-three consecutive patients with known or suspected coronary artery disease who underwent coronary CTA and subsequent invasive coronary angiography were enrolled. DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), model-based iterative reconstruction (MBIR) Cardiac, and MBIR Cardiac sharp for objective image qualities of coronary CTA. Invasive fractional flow reserve (FFR) and quantitative flow ratio (QFR) were used as the reference standards. The diagnostic performances of different reconstruction approach-based CT-FFRML were calculated. RESULTS: A total of 182 lesions in 33 patients were enrolled for analysis. The image quality of DLR was superior to the others. There were no significant differences in the CT-FFRML values among these five approaches (all p > 0.05). Of the 182 lesions, 17 had invasive FFR results, and 70 had QFR results. Using FFR as a reference, MBIR Cardiac, MBIR Cardiac sharp, and DLR achieved equal diagnostic performance, slightly higher than the other reconstruction approaches (MBIR Cardiac, MBIR Cardiac sharp, and DLR: AUC = 0.82, FBP and AIDR: AUC = 0.78, all p > 0.05). Using QFR as a reference, the AUCs of FBP, SBIR, MBIR Cardiac, MBIR Cardiac sharp, and DLR were 0.83, 0.81, 0.86, 0.84, and 0.83, respectively (all p > 0.05). CONCLUSIONS: Our study showed that the DLR algorithm improved image quality, but there were no significant differences in the CT-FFRML values and diagnostic performance among different reconstruction approaches. KEY POINTS: • Deep learning-based image reconstruction (DLR) improves the image quality of coronary CTA. • CT-FFRML values and diagnostic performance of DLR revealed no significant differences compared to other reconstruction approaches.

MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer.

OBJECTIVES: To develop and validate a clinical-radiomics model that incorporates radiomics signatures and pretreatment clinicopathological parameters to identify multimodality therapy candidates among patients with early-stage cervical cancer. METHODS: Between January 2017 and February 2021, 235 patients with IB1-IIA1 cervical cancer who underwent radical hysterectomy were enrolled and divided into training (n = 194, training:validation = 8:2) and testing (n = 41) sets according to surgical time. The radiomics features of each patient were extracted from preoperative sagittal T2-weighted images. Significance testing, Pearson correlation analysis, and Least Absolute Shrinkage and Selection Operator were used to select radiomic features associated with multimodality therapy administration. A clinical-radiomics model incorporating radiomics signature, age, 2018 Federation International of Gynecology and Obstetrics (FIGO) stage, menopausal status, and preoperative biopsy histological type was developed to identify multimodality therapy candidates. A clinical model and a clinical-conventional radiological model were also constructed. A nomogram and decision curve analysis were developed to facilitate clinical application. RESULTS: The clinical-radiomics model showed good predictive performance, with an area under the curve, sensitivity, and specificity in the testing set of 0.885 (95% confidence interval: 0.781-0.989), 78.9%, and 81.8%, respectively. The AUC, sensitivity, and specificity of the clinical model and clinical-conventional radiological model were 0.751 (0.603-0.900), 63.2%, and 63.6%, 0.801 (0.661-0.942), 73.7%, and 68.2%, respectively. A decision curve analysis demonstrated that when the threshold probability was > 20%, the clinical-radiomics model or nomogram may be more advantageous than the treat all or treat-none strategy. CONCLUSIONS: The clinical-radiomics model and nomogram can potentially identify multimodality therapy candidates in patients with early-stage cervical cancer. KEY POINTS: • Pretreatment identification of multimodality therapy candidates among patients with early-stage cervical cancer helped to select the optimal primary treatment and reduce severe complication risk and costs. • The clinical-radiomics model achieved a better prediction performance compared with the clinical model and the clinical-conventional radiological model. • An easy-to-use nomogram exhibited good performance for individual preoperative prediction.

Different Types of Circulatory Inflammatory Biomarkers Associated with Cerebral Arterial Atherosclerosis and Dolichoectasia.

BACKGROUND: Although inflammation is found to be related to arteriopathy pathogenesis, it is yet to be determined the distinct correlations of specific inflammatory biomarker types contributing to different cerebral large vessel diseases. We aimed to investigate the association between multiple inflammatory biomarkers and cerebral atherosclerosis and dolichoectasia in a community-based sample. METHODS: A total of 960 participants of the Shunyi study were included. A panel of 14 circulatory inflammatory biomarkers was assessed and then grouped in three sets as systemic, endothelial-related, and media-related inflammation, based on underlying different inflammatory cascades. Intracranial atherosclerotic stenosis (ICAS), dolichoectasia estimated by magnetic resonance angiography, and carotid plaques estimated by ultrasound were also performed. RESULTS: Endothelial-related inflammatory group was related to the presence of ICAS (R2 = 0.215, p = 0.024) and carotid plaques (R2 = 0.342, p = 0.013). Backward stepwise elimination showed that E-selectin was prominent (ß = 0.67, 95% CI: 0.54-0.85, p = 0.001; ß = 0.79, 95% CI: 0.68-0.93, p = 0.005). Systemic inflammatory group was associated with an increased basilar artery diameter (R2 = 0.051, p < 0.001), and backward stepwise elimination showed that IL-6 was prominent (ß = 0.07, 95% CI: 0.03-0.11, p < 0.001). CONCLUSION: Different types of inflammatory biomarkers were associated with atherosclerosis and dolichoectasia, respectively, implying dissimilar inflammatory processes. Further confirming of their distinct anti-inflammatory roles as potential therapeutic targets is warrant.

Assessment of Changes in the Cesarean Scar and Uterus Between One and Two Years after Cesarean Section Using 3D T2w SPACE MRI.

Objective To evaluate changes in morphology of the cesarean scar and uterus between one and two years after cesarean section using high-resolution, three dimensional T2-weighted sampling perfection with application optimized contrast using different flip angle evolutions Magnetic Resonance Imaging (3D T2w SPACE MRI). Methods This prospective study was performed to investigate morphological changes in the cesarean scars and uterus from one to two years after cesarean section using high-resolution, 3D T2w SPACE MRI. The healthy volunteers having no childbearing history were recruited as the controls. All data were measured by two experienced radiologists. All data with normal distribution between the one-year and two-year groups were compared using a paired-sample t test or independent t test. Results Finally, 46 women took a pelvic MR examination one year after cesarean section, and a subset of 15 completed the same examination again after two years of cesarean section. Both the uterine length and the anterior wall thickness after two years of cesarean section (5.75 ± 0.46 and 1.45 ± 0.35 cm) were significantly greater than those measured at one year (5.33 ± 0.59 and 1.25 ± 0.27 cm) (t = -2.363 and -2.175, P= 0.033 and 0.048). No significant difference was shown in myometrial thickness two years after cesarean section (1.45 ±0.35 cm) with respect to the control group (1.58 ± 0.21 cm, P= 0.170). Nine women who underwent MRI twice were considered to have scar diverticula one year after cesarean section, and still had diverticula two years after cesarean section. The thickness, height, and width of the uterine scar showed no significant change from one to two years (all P > 0.05). Conclusions 3D T2w SPACE MRI provides overall morphologic details and shows dynamic changes in the scar and the uterus between one and two years after cesarean section. Scar morphology after cesarean section reached relatively stable one year after cesarean section, and uterine morphology was closer to normal two years after cesarean section.

[Guidelines for Diagnosis and Treatment of Pancreatic Cystic Neoplasms Based on Radiology].

As the detection rate of pancreatic cystic neoplasms (PCN) increases,recommendations or guidelines for the diagnosis and treatment of PCN have been released from professional organizations.From the perspective of radiology,we compared seven guidelines in terms of general introduction,preoperative monitoring methods and strategies,stratification of risk factors,surgical indications,and postoperative follow-ups,aiming to provide references for the evaluation of images and the formulation of individualized approach for the treatment of PCN.

[Characterization of Endocrine Glands Involved in Erdheim-Chester Disease].

Objective To investigate the clinical and imaging characteristics of endocrine glands involved in Erdheim-Chester disease (ECD).Methods A retrospective analysis was performed on 48 ECD cases pathologically diagnosed from January 2014 to October 2020 in Peking Union Medical College Hospital,including 22 cases of endocrine gland involvement.The clinical,imaging,and pathological characteristics were summarized. Results Pituitary was involved in 17 cases (17/48,35.4%),adrenal gland in 8 cases (8/48,16.7%),and both pituitary and adrenal gland in 3 cases (3/48,6.25%).The most common symptom in patients with pituitary involvement was central diabetes insipidus (13/17,76.5%),and the T1-weighted imaging showed posterior pituitary hypersignal disappearance,pituitary stalk thickening,and abnormally enhanced pituitary nodules.The most common symptom in patients with adrenal gland involvement was adrenal function reduction (3/8,37.5%),and the CT scanning showed diffuse thickening of adrenal glands.BRAF V600E mutation was positive in 13 (13/22,59.1%) cases with ECD involving endocrine glands.Conclusion Pituitary and adrenal glands are the most common sites of ECD involving endocrine glands.A definite diagnosis can be achieved by combining clinical,imaging,and pathological characteristics for timely treatment.

[Cervical Primary Ewing's Sarcoma:Report of One Case].

Ewing's sarcoma in the cervix is characterized by extremely rare occurrence,high degree of malignancy,and rapid progression.The diagnosis of this disease is based on pathology and immunohistochemistry. The main image of the case reported in this paper showed the cervical cyst with solid mass,large volume,and uneven density and signal,and the solid part can be strengthened in enhanced scanning.Because of the rapid growth,the lesion is prone to liquefaction necrosis and bleeding.Since the metastasis occurs early,timely diagnosis is essential.

[Effect of Deep Learning-based Contrast-enhanced CT Image Reconstruction on the Image Quality of the Biliary System].

Objective To evaluate the effect of a deep learning reconstruction (DLR) method on the visibility of contrast-enhanced CT images of the biliary system by comparing it with different iterative reconstruction algorithms including the adaptive iterative dose reduction 3D (AIDR 3D) algorithm,forward projected model based iterative reconstruction solution (FIRST),and filtered back projection (FBP) algorithm. Methods A total of 30 patients subjected to abdominal contrast-enhanced CT and diagnosed with dilatation of common bile duct or extrahepatic bile duct were retrospectively included in this study.The images of the portal phase were reconstructed via four different algorithms (FBP,AIDR 3D,FIRST,and DLR).Signal to noise ratio (SNR) and contrast to noise ratio (CNR) of the dilated bile duct,liver parenchyma,measurable bile duct lesions,and image noise were compared between the four datasets.In subjective analyses,two radiologists independently scored the image quality (best:4 points,second:3 points;third:2 points;fourth:1 point) of the four datasets based on the noise and image visual quality of the biliary system.The Friedman and the Bonferroni-Dunn post-hoc tests were performed for comparison. Results The DLR images (bile duct:4.42±0.87;liver parenchyma:3.78±1.47) yielded higher CNR than the FBP (bile duct:2.21±1.02,P<0.001;liver parenchyma:1.43±1.29,P<0.001),AIDR 3D (bile duct:2.81±0.91,P=0.024;liver parenchyma:2.39±1.94,P=0.278),and FIRST (bile duct:2.51±1.24,P<0.001;liver parenchyma:2.45±1.81,P=0.003) images.Furthermore,the DLR images had higher SNR (bile duct:1.39±0.85,liver parenchyma:9.75±1.90) than the FBP (bile duct:0.86±0.63,P<0.001;liver parenchyma:3.31±1.12,P<0.001) and FIRST (bile duct:1.01±0.61,P=0.013;liver parenchyma:5.73±1.37,P<0.001) images,and showed lower noise (10.51±3.53) than the FBP(4.10±3.92,P<0.001),AIDR 3D (15.72±2.41,P=0.032),and FIRST (17.20±3.82,P<0.001) images.SNR and CNR showed no significant differences between FIRST and AIDR 3D images (all P>0.05).DLR images [4(4,4)] obtained higher score than FPB [1(1,1),P<0.001],AIDR3D[3 (2,3),P=0.029],and FIRST[2 (2,3),P<0.001] images. Conclusion DLR algorithm improved the subjective and objective quality of the contrast-enhanced CT image of the biliary system.

[Research Progress of Magnetic Resonance Imaging-based Radiomics in Prostate Cancer].

Radiomics can extract high-throughput and quantitative image features from medical images and mine the information related to the pathophysiology of tumors,which can help clinical decision-making and improve the diagnostic and predictive performance.Radiomics has been widely used in the study of prostate cancer (PCa),demonstrating application values in the diagnosis and differential diagnosis,pathology classification,invasion assessment,efficacy prediction,and prognosis analysis of PCa.Here we reviewed the recent research progress of magnetic resonance imaging-based radiomics in PCa.

RSNA International Trends: A Global Perspective on the COVID-19 Pandemic and Radiology in Late 2020.

The coronavirus disease 2019 pandemic has challenged and changed health care systems around the world. There has been a heterogeneity of disease burden, health care resources, and nonimaging testing availability, both geographically and over time. In parallel, there has been a continued increase in understanding how the disease affects patients, effectiveness of therapeutic options, and factors that modulate transmission risk. In this report, radiology experts in representative countries from around the world share insights gained from local experience. These insights provide a guidepost to help address management challenges as cases continue to rise in many parts of the world and suggest modifications in workflow that are likely to continue after this pandemic subsides.

Use of Chest Imaging in the Diagnosis and Management of COVID-19: A WHO Rapid Advice Guide.

The World Health Organization (WHO) undertook the development of a rapid guide on the use of chest imaging in the diagnosis and management of coronavirus disease 2019 (COVID-19). The rapid guide was developed over 2 months by using standard WHO processes, except for the use of "rapid reviews" and online meetings of the panel. The evidence review was supplemented by a survey of stakeholders regarding their views on the acceptability, feasibility, impact on equity, and resource use of the relevant chest imaging modalities (chest radiography, chest CT, and lung US). The guideline development group had broad expertise and country representation. The rapid guide includes three diagnosis recommendations and four management recommendations. The recommendations cover patients with confirmed or who are suspected of having COVID-19 with different levels of disease severity, throughout the care pathway from outpatient facility or hospital entry to home discharge. All recommendations are conditional and are based on low certainty evidence (n = 2), very low certainty evidence (n = 2), or expert opinion (n = 3). The remarks accompanying the recommendations suggest which patients are likely to benefit from chest imaging and what factors should be considered when choosing the specific imaging modality. The guidance offers considerations about implementation, monitoring, and evaluation, and also identifies research needs. Published under a CC BY 4.0 license. Online supplemental material is available for this article.