Project to improve the management of the head injury patients presenting to the emergency department.

INTRODUCTION: At Sandwell General Hospital, there was no risk stratification tool or pathway for head injury (HI) patients presenting to the emergency department (ED). This resulted in significant delays in the assessment of HI patients, compromising patient safety and quality of care. AIMS: To employ quality improvement methodology to design an effective adult HI pathway that: ensured >90% of high-risk HI patients being assessed by ED clinicians within 15 min of arrival, reduce CT turnaround times, and aiming to keep the final decision making <4 hours. METHODS: SWOT analysis was performed; driver diagrams were used to set out the aims and objectives. Plan-Do-Study-Act cycle was used to facilitate the change and monitor the outcomes. Process map was designed to identify the areas for improvement. A new HI pathway was introduced, imaging and transporting the patients was modified, and early decisions were made to meet the standards. RESULTS: Data were collected and monitored following the interventions. The new pathway improved the proportion of patients assessed by the ED doctors within 15 min from 31% to 63%. The average time to CT head scan was decreased from 69 min to 53 min. Average CT scan reporting time also improved from 98 min to 71 min. Overall, the average time to decision for admission or discharge decreased from 6 hours 48 min to 4 hours 24 min. CONCLUSIONS: Following implementation of the new HI pathway, an improvement in the patient safety and quality of care was noted. High-risk HI patients were picked up earlier, assessed quicker and had CT head scans performed sooner. Decision time for admission/discharge was improved. The HI pathway continues to be used and will be reviewed and re-audited between 3 and 6 months to ensure the sustained improvement.

An NHS Trust comparison of reporting radiographers and consultant radiologists ability to identify cancer on chest radiographs with confirmation through a Fast track CT thorax referral system.

INTRODUCTION: The National Institute for Health and Care Excellence (NICE) recommends that GPs initially refer patients with suspected lung cancer for a chest X-ray (CXR). The Radiology department has a 'fast track system' to identify those patients who may have lung cancer on CXR and are referred for a CT thorax with contrast to help determine a cancer diagnosis. This fast track system was put in place to ensure the NICE guidelines and NHS England's standards on a faster cancer diagnosis are being met. This audit studied the ability of radiologists and reporting radiographers to identify lung cancer on CXRs and the accuracy of the fast-track system. METHODS: 846 cases with lung alerts were analysed and 545 CXRs were audited. The CXRs were split into images reported by radiologists (168) and those reported by reporting radiographers (377). CT thorax results were collected through PACS and Cerner computer systems to identify if the 'fast track' system had yielded a "positive", "negative", or "other findings" result for lung cancer. RESULTS: 32.8% (179) of CXRs flagged for lung cancer were positive, 40.6% (221) were negative, and 26.6% (145) had other findings. Chi square statistical test showed no significant difference (p = 0.14) between the two reporting groups in their ability to identify lung cancer on CXRs. 27% (38) of CXRs flagged by radiologists and 35% (125) by reporting radiographers were positive for lung cancer. CONCLUSION: This clinical audit indicates, reporting radiographers and radiologists are not statistically significantly different regarding their ability to identify lung cancer on CXRs, when supported by the fast track system. The fast-track system had a 59.4 % accuracy rate, detected by the number of imaging of reports that identified a serious pathology. This concludes that the system is performing well, yet could still be improved. IMPLICATIONS FOR PRACTICE: This audit provides further evidence for the value of developing and deploying reporting radiographers for projection radiography reporting.

Performance evaluation of computed tomography scanners in Indonesia: recommendation from a nation-wide study.

In this study, an evaluation of the compliance test data from 684 computed tomography (CT)-scanners in Indonesia for the 2019-22 test period was carried out. The study was aimed to describe the performance profile of CT-scanners in Indonesia and evaluate the testing protocol. A total of 87.8% of the CT-scanners unconditionally passed the tests, 8.8% passed the tests with conditions and 3.4% failed the tests. Of the devices conditionally passed the tests, the top two causes were water CT number accuracy (45.2%) and laser position accuracy (41.9%). Meanwhile, 75.0% of the failed devices were due to failing to meet the patient dose test criteria. The failure of the test for the water CT number accuracy parameter was caused by variations in the type of phantom used in the test, where several types of phantoms did not use water as material of the homogeneity module. Failures in laser position accuracy test were caused by the passing criteria that adjust to the minimum slice thickness, so that modern CT-scanner with small detector sizes and collimations tend not to pass. On the other hand, the failure on dose aspects was due to the frequent unavailability of baseline values for comparison. Of these top three failure causes, two of them, namely the CT number and dose test parameters, have been accommodated in the latest regulation (BAPETEN Regulation No. 2/2022) with a change in the evaluation method, while for the laser position accuracy test it is recommended to alter the passing criteria to an absolute value, namely 1 mm.

Updating national diagnostic reference levels for computed tomography in Greece: Challenges on patient protection optimisation.

The escalating use of Computed Tomography (CT) imaging necessitates establishment and periodic revision of Diagnostic Reference Levels (DRLs) to ensure patient protection optimization. This paper presents the outcomes of a national survey conducted from 2019 to 2022, focusing on revising DRLs for adult CT examinations. Dosimetric data from 127 scanners in 120 medical facilities, representing 25% of the country's CT scanners, were collected, emphasizing geographic distribution and technology representation. Τhe parameters used for DRLs were the CTDIvol and the DLP of a typical acquisition of the region of interest (scan DLP). In addition to the 7 CT examination for which the DRL values were revised, establishment of DRLs for neck, cervical spine, pelvic bones-hips, coronary artery calcium (Ca) score and cardiac computed tomography angiography (CCTA) examinations was performed. Revised DRLs exhibited a 15 % average decrease in CTDIvol and a 7 % average decrease in scan DLP from the initial DRLs. This reduction of dosimetric values is relatively low compared to other national studies. The findings revealed wide variations in dosimetric values and scan lengths among scanners, emphasizing the need for standardization and optimization. Incorporation of advanced technologies like Iterative Reconstruction (IR) showcased potential for further dose reduction, yet challenges in uniform implementation persist. The study underscores the importance of ongoing optimisation efforts, particularly in the context of increased CT utilization and evolving technology. The revised DRLs have been officially adopted in Greece, emphasizing the commitment to safe and effective CT practices.

Normative reference data for intracranial volume in children: The results of CT volumetry.

BACKGROUND: Intracranial volume (ICV) is an important indicator of the development of the brain and skull in children. At present, there is a lack of ICV growth standards based on large infant and children samples. Our aim was to assess the normal range of the ICV variation in Russian children using a modern automatic system for constructing the endocranial cavity (Endex) and to provide growth standards of the ICV for clinical practice. METHODS: High-resolution head CT scans were obtained from 673 apparently healthy children (380 boys and 293 girls) aged 0-17 years and transformed into the ICV estimates using the Endex software. The open-source software RefCurv utilizing R and the GAMLSS add-on package with the LMS method was then used for the construction of smooth centile growth references for ICV according to age and sex. RESULTS: We demonstrated that the ICVs estimates calculated using the Endex software are perfectly comparable with those obtained by a conventional technique (i.e. seed feeling). Sex-specific pediatric growth charts for ICV were constructed. CONCLUSIONS: This study makes available for use in clinical practice ICV growth charts for the age from 0 to 17 based on a sample of 673 high-resolution CT images.

Quantitative imaging predicts pancreatic fatty infiltration on routine CT examination.

PURPOSE: The purpose of this study was to assess the performance of quantitative computed tomography (CT) imaging for detecting pancreatic fatty infiltration, using the results of histopathological analysis as reference. MATERIALS AND METHODS: Sixty patients who underwent pancreatic surgery for a pancreatic tumor between 2016 and 2019 were retrospectively included. There were 33 women and 27 men with a mean age of 56 ± 12 (SD) years (age range: 18-79 years). Patients with dilatation of the main pancreatic duct, chronic pancreatitis, or preoperative treatment were excluded to prevent any bias in the radiological-pathological correlation. Pancreatic fatty infiltration was recorded at pathology. Pancreatic surface lobularity, pancreatic attenuation, visceral fat area, and subcutaneous fat area were derived from preoperative CT images. The performance for the prediction of fatty infiltration was assessed using area under receiver operating characteristic curve (AUC) and backward binary logistic regression analysis. Results were validated in a separate cohort of 34 patients (17 women; mean age, 50 ± 14 [SD] years; age range: 18-73). RESULTS: A total of 28/60 (47%) and 17/34 (50%) patients had pancreatic fatty infiltration in the derivation and validation cohorts, respectively. In the derivation cohort, patients with pancreatic fatty infiltration had a significantly higher PSL (P < 0.001) and a lower pancreatic attenuation on both precontrast and portal venous phase images (P = 0.011 and 0.003, respectively), and higher subcutaneous fat area and visceral fat area (P = 0.010 and 0.007, respectively). Multivariable analysis identified pancreatic surface lobularity > 7.6 and pancreatic attenuation on portal venous phase images < 83.5 Hounsfield units as independently associated with fatty infiltration. The combination of these variables resulted in an AUC of 0.85 (95% CI: 0.74-0.95) and 0.83 (95% CI: 0.67-0.99) in the derivation and validation cohorts, respectively. CONCLUSION: CT-based quantitative imaging accurately predicts pancreatic fatty infiltration.

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising.

Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT research. Compared to the normal dose CT, LDCT images are subjected to severe noise and artifacts. Recently in many studies, vision transformers have shown superior feature representation ability over the convolutional neural networks (CNNs). However, unlike CNNs, the potential of vision transformers in LDCT denoising was little explored so far. Our paper aims to further explore the power of transformer for the LDCT denoising problem.Approach. In this paper, we propose a Convolution-free Token2Token Dilated Vision Transformer (CTformer) for LDCT denoising. The CTformer uses a more powerful token rearrangement to encompass local contextual information and thus avoids convolution. It also dilates and shifts feature maps to capture longer-range interaction. We interpret the CTformer by statically inspecting patterns of its internal attention maps and dynamically tracing the hierarchical attention flow with an explanatory graph. Furthermore, overlapped inference mechanism is employed to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models.Main results. Experimental results on Mayo dataset suggest that the CTformer outperforms the state-of-the-art denoising methods with a low computational overhead.Significance. The proposed model delivers excellent denoising performance on LDCT. Moreover, low computational cost and interpretability make the CTformer promising for clinical applications.

Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.

PURPOSE: Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. METHODS: A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. RESULTS: Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. CONCLUSION: The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.

Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience.

PURPOSE: To compare liver fat quantification between MRI and photon-counting CT (PCCT). METHOD: A cylindrical phantom with inserts containing six concentrations of oil (0, 10, 20, 30, 50 and 100%) and oil-iodine mixtures (0, 10, 20, 30 and 50% fat +3 mg/mL iodine) was imaged with a PCCT (NAEOTOM Alpha) and a 1.5 T MRI system (MR 450w, IDEAL-IQ sequence), using clinical parameters. An IRB-approved prospective clinical evaluation included 12 obese adult patients with known fatty liver disease (seven women, mean age: 61.5 ± 13 years, mean BMI: 30.3 ± 4.7 kg/m2). Patients underwent a same-day clinical MRI and PCCT of the abdomen. Liver fat fractions were calculated for four segments (I, II, IVa and VII) using in- and opposed-phase on MRI ((Meanin - Meanopp)/2*Meanin) and iodine-fat, tissue decomposition analysis in PCCT (Syngo.Via VB60A). CT and MRI Fat fractions were compared using two-sample t-tests with equal variance. Statistical analysis was performed using RStudio (Version1.4.1717). RESULTS: Phantom results showed no significant differences between the known fat fractions (P = 0.32) or iodine (P = 0.6) in comparison to PCCT-measured concentrations, and no statistically significant difference between known and MRI-measured fat fractions (P = 0.363). In patients, the mean fat signal fraction measured on MRI and PCCT was 13.1 ± 9.9% and 12.0 ± 9.0%, respectively, with an average difference of 1.1 ± 1.9% between the modalities (P = 0.138). CONCLUSION: First experience shows promising accuracy of liver fat fraction quantification for PCCT in obese patients. This method may improve opportunistic screening for CT in the future.

Location-Dependent Spatiotemporal Antialiasing in Photoacoustic Computed Tomography.

Photoacoustic computed tomography (PACT) images optical absorption contrast by detecting ultrasonic waves induced by optical energy deposition in materials such as biological tissues. An ultrasonic transducer array or its scanning equivalent is used to detect ultrasonic waves. The spatial distribution of the transducer elements must satisfy the spatial Nyquist criterion; otherwise, spatial aliasing occurs and causes artifacts in reconstructed images. The spatial Nyquist criterion poses different requirements on the transducer elements' distributions for different locations in the image domain, which has not been studied previously. In this research, we elaborate on the location dependency through spatiotemporal analysis and propose a location-dependent spatiotemporal antialiasing method. By applying this method to PACT in full-ring array geometry, we effectively mitigate aliasing artifacts with minimal effects on image resolution in both numerical simulations and in vivo experiments.

Computed Tomography Dose Level in Selected Five Principal Hospitals in Ethiopia.

Background: X-ray Computed Tomography dose levels have been varying among modalities and scanning body regions due to the absence of incessant routine follow-up. Thus, the study aimed to compute the dose index discrepancies in Ethiopia for the most recurring scan protocols (head, chest, abdomen, and pelvis). Methods: A purposive sampling method was employed to select the hospitals due to the rare existence of functional CT scanners in Ethiopia. From the selected hospitals, a total of 1,385 (249 heads, 804 chests, 132 abdomens, and 200 pelvis) were collected in terms of standard dose metric values in the period of December 2019-March 2020. Patients' DLP was computed into mean value using IBM SPSS Statistics 20 software. From the mean DLP, we can compute the effective dose. Results: Patients' dose level disparity was observed in this study though it is below the ICRP standard level for all body regions except for pelvis DLP (593.37 mGy-cm) at Black Lion. The dose level for the head and chest are computed within the recommended level at all hospitals. Effective doses for the pelvis at four hospitals (Teklehaimanot, Black Lion, ALERT, Paul's, and Ayder hospitals) were computed as 6.45, 8.90, 5.08, 6.54, and 6.84 mSv respectively, and the effective doses for abdomen at Ayder Hospital was obtained to be 8.90 mSv, which is above the recommended value. Conclusion: X-ray CT scanners are somewhat properly functioning although some sort of justification and optimization for pelvis and abdomen examinations are strongly recommended to implement as low as reasonably achievable principle.

Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

A Retrospectively Study: Diagnosis of Pathological Types of Malignant Lung Tumors by Dual-layer Detector Spectral Computed Tomography.

Object: By retrospectively analyzing the energy spectrum of squamous cell carcinoma, adenocarcinoma, small cell lung cancer (SCLC), and pulmonary metastases that underwent dual-layer detector spectral computed tomography (DLCT) 3-phase scan of the chest, we explored the value of a multiparameter energy spectrum in the assessment of pathological types of lung tumors. Methods: Cases of squamous cell carcinoma (n = 20), adenocarcinoma (n = 24), SCLC (n = 26), and metastases (n = 14) were collected. Then the largest cross-sectional area (LCA) of the lesion, computed tomography (CT) values in the plain scan phase, arterial and venous phases (HU, HUa, and HUv), iodine concentration, and effective atomic number in the arterial and venous phases (ICa, ICv, Zeff[a], and Zeff[v]) were measured and compared among the nonsmall cell lung cancer (NSCLC), SCLC and metastases, and other 3 groups of SCLC, squamous cell carcinoma, and adenocarcinoma. Results: Only the LCA is statistically different among SCLC, NSCLC, and metastases (P < .05). And the treated subgroup analysis did not show significant differences among the groups. However, the untreated subgroup analysis showed that there was a significant difference between NSCLC and metastases in LCA, SCLC and metastases in ICa, NSCLC and SCLC in HUv, NSCLC and SCLC in Zeff(v) (P < .05). Conclusion: The energy spectrum parameters of DLCT have a certain clinical value in distinguishing NSCLC from SCLC in the Zeff(v) and distinguishing SCLC from metastases in the ICa.

Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS: Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS: In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION: The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.

Diagnostic accuracy of ultra-low-dose chest computed tomography in an emergency department.

BACKGROUND: This study examined whether ultra-low-dose chest computed tomography (ULD-CT) could improve detection of acute chest conditions. PURPOSE: To determine (i) whether diagnostic accuracy of ULD-CT is superior to supine chest X-ray (sCXR) for acute chest conditions and (ii) the feasibility of ULD-CT in an emergency department. MATERIAL AND METHODS: From 1 February to 31 July 2019, 91 non-traumatic patients from the Emergency Department were prospectively enrolled in the study if they received an sCXR. An ULD-CT and a non-contrast chest CT (NCCT) scan were then performed. Three radiologists assessed the sCXR and ULD-CT examinations for cardiogenic pulmonary edema, pneumonia, pneumothorax, and pleural effusion. Resources and effort were compared for sCXR and ULD-CT to evaluate feasibility. Diagnostic accuracy was calculated for sCXR and ULD-CT using NCCT as the reference standard. RESULTS: The mean effective dose of ULD-CT was 0.05±0.01 mSv. For pleural effusion and cardiogenic pulmonary edema, no difference in diagnostic accuracy between ULD-CT and sCXR was observed. For pneumonia and pneumothorax, sensitivities were 100% (95% confidence interval [CI] 69-100) and 50% (95% CI 7-93) for ULD-CT and 60% (95% CI 26-88) and 0% (95% CI 0-0) for sCXR, respectively. Median examination time was 10 min for ULD-CT vs. 5 min for sCXR (P<0.001). For ULD-CT 1-2 more staff members were needed compared to sCXR (P<0.001). ULD-CT was rated more challenging to perform than sCXR (P<0.001). CONCLUSION: ULD-CT seems equal or better in detecting acute chest conditions compared to sCXR. However, ULD-CT examinations demand more effort and resources.

Not so FAST-Chest ultrasound underdiagnoses traumatic pneumothorax.

BACKGROUND: Ultrasonography for trauma is a widely used tool in the initial evaluation of trauma patients with complete ultrasonography of trauma (CUST) demonstrating equivalence to computed tomography (CT) for detecting clinically significant abdominal hemorrhage. Initial reports demonstrated high sensitivity of CUST for the bedside diagnosis of pneumothorax. We hypothesized that the sensitivity of CUST would be greater than initial supine chest radiograph (CXR) for detecting pneumothorax. METHODS: A retrospective analysis of patients diagnosed with pneumothorax from 2018 through 2020 at a Level I trauma center was performed. Patients included had routine supine CXR and CUST performed prior to intervention as well as confirmatory CT imaging. All CUST were performed during the initial evaluation in the trauma bay by a registered sonographer. All imaging was evaluated by an attending radiologist. Subgroup analysis was performed after excluding occult pneumothorax. Immediate tube thoracostomy was defined as tube placement with confirmatory CXR within 8 hours of admission. RESULTS: There were 568 patients screened with a diagnosis of pneumothorax, identifying 362 patients with a confirmed pneumothorax in addition to CXR, CUST, and confirmatory CT imaging. The population was 83% male, had a mean age of 45 years, with 85% presenting due to blunt trauma. Sensitivity of CXR for detecting pneumothorax was 43%, while the sensitivity of CUST was 35%. After removal of occult pneumothorax (n = 171), CXR was 78% sensitive, while CUST was 65% sensitive (p < 0.01). In this subgroup, CUST had a false-negative rate of 36% (n = 62). Of those patients with a false-negative CUST, 50% (n = 31) underwent tube thoracostomy, with 85% requiring immediate placement. CONCLUSION: Complete ultrasonography of trauma performed on initial trauma evaluation had lower sensitivity than CXR for identification of pneumothorax including clinically significant pneumothorax requiring tube thoracostomy. Using CUST as the primary imaging modality in the initial evaluation of chest trauma should be considered with caution. LEVEL OF EVIDENCE: Diagnostic Test study, Level IV.

Validation of the ECHS non trauma cranial CT rule in Australia: A prospective cohort study.

INTRODUCTION: Computed tomography (CT) is a commonly used imaging modality in Emergency Departments (EDs), however its use is questionable in many low yield settings. The Emergency CT Head score (ECHS) is a recently published clinical tool that assists in stratifying the need for CT brain (CTB) for patients presenting without a history of trauma. We sought to validate this tool in an Australian ED setting. METHODS: We prospectively evaluated 412 patients who received CTB without a history of trauma at a large Australian ED. We assessed them for the 4 main ECHS data points: focal neurological deficit on physical examination, new acute onset headache, transient neurological deficit, and a combination of new onset seizures with an altered conscious state. We examined their association with acute and chronic CTB findings. We then applied the ECHS to our data, calculating its sensitivity and its appropriateness at this single site via the calculation of a receiver operating curve (ROC). RESULTS: 10.2% of all CTB performed were positive for an acute or chronic abnormality. Only sex (male) and focal motor deficit were independent predictors of positive CTB at univariate analysis. The ECHS did not perform as anticipated in our population, with a ROC area under the curve of 0.498. An ECHS score of >0, which has been proposed as the threshold to not require imaging, had sensitivity of only 83.3% in our population. CONCLUSIONS: Further research and validation is required in order to safely implement the ECHS clinical score in the Australian ED setting.

Comparison of lung image quality between CT Ark and Brilliance 64 CT during COVID-19.

AIM: This study is to compare the lung image quality between shelter hospital CT (CT Ark) and ordinary CT scans (Brilliance 64) scans. METHODS: The patients who received scans with CT Ark or Brilliance 64 CT were enrolled. Their lung images were divided into two groups according to the scanner. The objective evaluation methods of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used. The subjective evaluation methods including the evaluation of the fine structure under the lung window and the evaluation of the general structure under the mediastinum window were compared. Kappa method was used to assess the reliability of the subjective evaluation. The subjective evaluation results were analyzed using the Wilcoxon rank sum test. SNR and CNR were tested using independent sample t tests. RESULTS: There was no statistical difference in somatotype of enrolled subjects. The Kappa value between the two observers was between 0.68 and 0.81, indicating good consistency. For subjective evaluation results, the rank sum test P value of fine structure evaluation and general structure evaluation by the two observers was ≥ 0.05. For objective evaluation results, SNR and CNR between the two CT scanners were significantly different (P<0.05). Notably, the absolute values ​​of SNR and CNR of the CT Ark were larger than Brilliance 64 CT scanner. CONCLUSION: CT Ark is fully capable of scanning the lungs of the COVID-19 patients during the epidemic in the shelter hospital.

Adrenal wash-out CT: moderate diagnostic value in distinguishing benign from malignant adrenal masses.

OBJECTIVE: Reliable results of wash-out CT in the diagnostic workup of adrenal incidentalomas are scarce. Thus, we evaluated the diagnostic accuracy of delayed wash-out CT and determined thresholds to accurately differentiate adrenal masses. DESIGN: Retrospective, single-center cohort study including 216 patients with 252 adrenal lesions who underwent delayed wash-out CT. Definitive diagnoses based on histopathology (n = 92) or comprehensive follow-up. METHODS: Size, average attenuation values of the adrenal lesions in all CT scan phases, and absolute and relative percentage wash-out (APW/RPW) were determined by an expert radiologist blinded for clinical data. Adrenal lesions with unenhanced attenuation values >10 Hounsfield units (HU) built a subgroup (n = 142). Diagnostic accuracy was calculated. RESULTS: The study group consisted of 171 adenomas, 32 other benign tumors, 11 pheochromocytomas, 9 adrenocortical carcinomas, and 29 other malignant tumors. All (potentially) malignant and 46% of benign lesions showed unenhanced attenuation values >10 HU. In this most relevant subgroup, the established thresholds of 60% for APW and 40% for RPW misclassified 35.9 and 35.2% of the masses, respectively. When we applied optimized cutoffs (APW >83%; RPW >58%) and excluded pheochromocytomas, we missed only one malignant tumor by APW and none by RPW. However, only 11 and 15% of the benign tumors were correctly identified. CONCLUSIONS: Wash-out CT with the established thresholds for APW and RPW is insufficient to reliably diagnose adrenal masses. Using the proposed cutoff of 58% for RPW, malignant tumors will be correctly identified, but the added value is limited, namely 15% of patients with benign tumors can be prevented from additional imaging or even unnecessary surgery.

MRI versus CT for the detection of pulmonary nodules: A meta-analysis.

BACKGROUND: Computed tomography (CT) is the current gold standard for the detection of pulmonary nodules but has high radiation burden. In contrast, many radiologists tried to use magnetic resonance imaging (MRI) to replace CT because MRI has no radiation burden associated. Due to the lack of high-level evidence of comparison of the diagnostic accuracy of MRI versus CT for detecting pulmonary nodules, it is unknown whether CT can be replaced successfully by MRI. Therefore, the aim of this study was to compare the diagnostic accuracy of MRI versus CT for detecting pulmonary nodules. METHODS: Electronic databases PubMed, EmBase, and Cochrane Library were systematically searched from their inception to September 2017 to identify studies in which CT/MRI was used to diagnose pulmonary nodules. According to true positive, true negative, false negative, and false positive extracted from the included studies, we calculate the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve (AUC) using Stata version 14.0 software (STATA Corp, TX). RESULTS: A total of 8 studies involving a total of 653 individuals were included. The pooled sensitivity, specificity, PLR, NLR, and AUC were 0.91 (95% confidence interval [CI]: 0.80-0.96), 0.76 (95%CI: 0.58-0.87), 3.72 (95%CI: 2.05-6.76), 0.12 (95%CI: 0.06-0.27), and 0.91 (95%CI: 0.88-0.93) for MRI respectively, while the pooled sensitivity, specificity, PLR, NLR, and AUC for CT were 1.00 (95%CI: 0.95-1.00), 0.99 (95%CI: 0.78-1.00), 79.35 (95%CI: 3.68-1711.06), 0.00 (95%CI: 0.00-0.06), and 1.00 (95%CI: 0.99-1.00), respectively. Further, we compared the diagnostic accuracy of CT versus MRI and found that compared with MRI, CT shows statistically higher sensitivity (odds ratio [OR] for MRI vs CT: 0.91; 95%CI: 0.85-0.98; P value .010), specificity (OR: 0.82; 95%CI: 0.69-0.97; P value .019), PLR (OR: 0.29; 95%CI: 0.10-0.83; P value 0.02), AUC (OR: 0.91; 95%CI: 0.89-0.94; P value < .001), and lower NLR (OR: 8.72; 95%CI: 1.57-48.56; P value .013). CONCLUSION: Our study suggested both CT and MRI have a high diagnostic accuracy in diagnosing pulmonary nodules, while CT was superior to MRI in sensitivity, specificity, PLR, NLR, and AUC, indicating that in terms of the currently available evidence, MRI could not replace CT in diagnosing pulmonary nodules.