Non-enhanced CT Research Articles

Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelligence prediction model

ObjectiveBy extracting early chest CT radiomic features of COVID-19 patients, we explored their correlation with laboratory indicators and oxygenation index (PaO2/FiO2), thereby developed an Artificial Intelligence (AI) model based on radiomic features to predict the deterioration of oxygenation function in COVID-19 patients.MethodsThis retrospective study included 384 patients with COVID-19, whose baseline information, laboratory indicators, oxygenation-related parameters, and non-enhanced chest CT images were collected. Utilizing the PaO2/FiO2 stratification proposed by the Berlin criteria, patients were divided into 4 groups, and differences in laboratory indicators among these groups were compared. Radiomic features were extracted, and their correlations with laboratory indicators and the PaO2/FiO2 were analyzed, respectively. Finally, an AI model was developed using the PaO2/FiO2 threshold of less than 200 mmHg as the label, and the model’s performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Group datas comparison was analyzed using SPSS software, and radiomic features were extracted using Python-based Pyradiomics.ResultsThere were no statistically significant differences in baseline characteristics among the groups. Radiomic features showed differences in all 4 groups, while the differences in laboratory indicators were inconsistent, with some PaO2/FiO2 groups showed differences and others not. Regardless of whether laboratory indicators demonstrated differences across different PaO2/FiO2 groups, they could all be captured by radiomic features. Consequently, we chose radiomic features as variables to establish an AI model based on chest CT radiomic features. On the training set, the model achieved an AUC of 0.8137 (95% CI [0.7631–0.8612]), accuracy of 0.7249, sensitivity of 0.6626 and specificity of 0.8208. On the validation set, the model achieved an AUC of 0.8273 (95% CI [0.7475–0.9005]), accuracy of 0.7739, sensitivity of 0.7429 and specificity of 0.8222.ConclusionThis study found that the early chest CT radiomic features of COVID-19 patients are strongly associated not only with early laboratory indicators but also with the lowest PaO2/FiO2. Consequently, we developed an AI model based on CT radiomic features to predict deterioration in oxygenation function, which can provide a reliable basis for further clinical management and treatment.

To bridge or not to bridge: the role of intravenous thrombolysis in mechanical thrombectomy for large cerebral infarctions through a two-center cohort study and meta-analysis

BackgroundThe effectiveness and safety of intravenous thrombolysis before mechanical thrombectomy (MT) in large cerebral infarctions remains uncertain. This study compares bridging MT, which includes intravenous thrombolysis, to direct MT without it. MethodsData from 298 patients with anterior circulation large cerebral infarctions, assessed via non-enhanced CT (ASPECTS 0-5), who underwent MT in two-center cohort studies, were analyzed. Primary outcomes focused on independent ambulation (modified Rankin Scale scores 0-3) at 90 days post-stroke. Safety outcomes included parenchymal hemorrhage (PH) rates and mortality. We conducted a sensitivity analysis considering the timing from symptom onset to imaging within 4.5 hours. Additionally, a meta-analysis of 17 studies involving 3527 patients assessed the interventions' effectiveness and safety, with further scrutiny of high-quality studies (Newcastle-Ottawa Scale ratings 7-9) to increase robustness of results. ResultsNo significant differences were found in 90-day independent ambulation between the bridging MT and the direct MT group (adjusted odds ratio [aOR] 1.15, 95% CI 0.68-1.94). Rates of PH and mortality were also similar across groups. These outcomes were consistent in the subgroup imaged within 4.5 hours of symptom onset. The meta-analysis supported these outcomes, showing no improvement in ambulation (aOR 1.16, 95% CI 0.82-1.64) or reduction in PH with bridging MT. Further analysis of high-quality studies supported these results. ConclusionsThe cohort study and meta-analysis provide Class II evidence indicating no significant differences in functional outcomes or hemorrhagic risks between bridging and direct MT for large cerebral infarctions. This suggests that direct MT might be a viable alternative to bridging MT.

Differentiation and characterization of non-thymomatous ocular myasthenia gravis children with CT.

To compare thymic features using unenhanced computed tomography (CT) images between children with non-thymomatous ocular myasthenia gravis (OMG) and healthy children and determine the image feature cut-off values to allow differentiation of the two populations. We conducted a retrospective study involving 66 children who underwent chest CT, with 33 having non-thymomatous OMG and 33 healthy children without any abnormality. We recorded CT features of the thymus in each child, including the mean CT attenuation value, thymus area, area ratio, anteroposterior (AP) length, width (W), thickness (T), and transverse diameter (TD). These characteristics were analyzed to distinguish OMG from healthy controls. The receiver operating characteristic (ROC) analysis was used to clarify the best assessment, and the optimal cut-off values were determined. The mean CT attenuation value, the thymus area, area ratio, and TD were significantly different (P < 0.05) between the two groups. The mean CT attenuation value was the most significant characteristic in differentiating OMG and healthy controls (area under the curve [AUC] = 0.70). Using 50.41 as the cut-off value for mean CT attenuation, a specificity of 0.70 and sensitivity of 0.82 was observed for distinguishing the two groups (P = 0.003). Optimal differentiation was achieved by combining more than one characteristic including CT attenuation, thymus area, and TD with an optional area ratio (AUC = 0.72). Non-enhanced CT of the thymus complemented the clinical workup of children with non-thymomatous OMG. The cut-off values of the CT features may allow robust differentiation of OMG children.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Study on screening and diagnosis of aortic dissection based on non-enhanced CT and deep learning

Abstract Objective To develop a machine learning algorithm to detect aortic dissection on non-contrast-enhanced CT and evaluate the diagnostic ability of the algorithm compared with those of radiologists. Methods This study developed a machine learning algorithm using single-center data collected between May 1st, 2022, and April 30th, 2023. Included in the study were 220 patients (110 with AD and 110 without AD). An AD detection algorithm was developed using a 3D full-resolution U-net architecture. We have continuously trained and developed an algorithm based on machine learning to segment the true and false lumens of the aorta and then determine whether there is aortic dissection. The diagnostic capabilities of our algorithm and three radiologists were also compared. Results The developed algorithm achieved an accuracy of 95.8%, a sensitivity of 93.2%, and a specificity of 92.6%. For radiologists, accuracy, sensitivity, and specificity were 88.6%, 90.6%, and 94.2%, respectively. The algorithm's performance was not significantly different from the mean performance of radiologists in terms of accuracy, sensitivity, or specificity. Conclusion The proposed algorithm showed comparable diagnostic performance to radiologists for detecting AD on non-contrast-enhanced CT, which suggests that the proposed algorithm has the potential to reduce misdiagnosis of AD to improve clinical outcomes.Aortic SegmentationAlgorithm development

Role of Multi-Detector Computed Tomography in Evaluating Traumatic Brain Injuries in Taif City, Kingdom of Saudi Arabia

Background: Traumatic brain injury (TBI) represents a major percentage of admissions to hospital due to frequent trauma to this region and is considered a life-threatening situation because of that, MDCT is necessary to provide rapid diagnosis and best management that leads to successful treatment. Multi-detector Computed Tomography (MDCT) has a critical and significant rule for early evaluation and patient triage. Aim: This study aimed to evaluate the findings that occur due to head injuries using MDCT. Material and Method: An analytical retrospective study of 200 patients with head injuries were examined by CT, the study extended from January 2024 to September 2024. 128 slice CT systems (Siemens Medical system) were applied to investigate patients with head trauma at King Abdul-Aziz Specialist Hospital (KAASH )in Ta'if City, Saudi Arabia. Results: A total of 200 head trauma 136 (68%) males and 64(32%) females, were examined via head CT and presented with various types of trauma. The findings have noticed that brain injuries were significantly higher in the male population (68%) than in the female population (32%). Based on age groups, the incidence percentage of TBI was 34%, occurring in the age group 18-27 years. The most common cause of injury in patients presenting with head trauma was road traffic accidents, comprising 63% of cases. The isolated skull fracture was most frequently seen 35% of cases. Our study reveals that Subdural hematoma (52%) was the most frequent lesion noted in traumatic patients. Conclusion: Non-enhanced computed tomography( NECT) is considered the gold standard and the most informative diagnostic modality in the evaluation of Traumatic brain injuries and it is essentially to triage patients to observation, surgical, or medical management.

Intrathoracic Sleeve Migration Following Sleeve Gastrectomy: Incidence and Outcomes.

With laparoscopic sleeve gastrectomy (LSG) taking its place as the primary metabolic bariatric procedure in the past decade, de novo or persistent gastroesophageal reflux disease (GERD) has come into the spotlight as one of the most debilitating postoperative complications. Among the causes of GERD, intrathoracic migration (ITM) of the sleeve has become an understated yet significant phenomenon. This study aims to analyze the actual incidence of spontaneous ITM at our center, as well as its relationship to the baseline characteristics of patients and perioperative outcomes. A retrospective chart review of 206 patients who had undergone LSG at our center from July 2019 to December 2022 was done. At 1-year follow-up, a non-enhanced abdominopelvic computed tomography (CT) scan and esophagogastroduodenoscopy (EGD) were performed, and these exams were repeated annually. Baseline characteristics and perioperative outcomes including the clinical disease course of GERD were compared. The incidence of ITM was 14% (n = 29). There was a significant correlation between ITM and postoperative reflux symptoms (p = 0.001). The mean duration of anti-reflux medication use was also significantly longer in the ITM group than in the no ITM group at 17 and 11months, respectively (p = 0.004). A significantly higher number of patients in the ITM group were diagnosed with esophagitis on postoperative EGD than in the no ITM group (p = 0.002). The incidence of ITM using non-enhanced CT was 14%. ITM was significantly correlated to clinical reflux and EGD-confirmed esophagitis.

Value of spectral parameters in the differential diagnosis of benign and malignant breast nodules in non-enhanced chest CT.

Dual-layer spectral computed tomography (DSCT) is capable of acquiring both conventional and spectral images during one routine scan, and is widely used for the quantitative and qualitative analyses of substances, differential diagnosis, and disease staging. However, limited research has been conducted on its performance in the differential diagnosis of benign and malignant breast nodules using non-enhanced scans. This study aimed to assess the diagnostic performance of multiple quantitative parameters derived from non-enhanced DSCT in differentiating benign from malignant breast nodules. This retrospective cross-sectional study examined a total of 121 breast nodules from 114 patients (malignant group: n=68; benign group: n=53) identified during chest physical examination or routine admission for the treatment of breast diseases at The First Affiliated Hospital of Soochow University from March 2023 to December 2023. All the patients underwent DSCT scanning and pathological diagnosis. The DSCT quantitative parameters, including the effective atomic number (Zeff), computed tomography (CT) attenuation values at 40-70 keV, and the slope of the spectral Hounsfield unit curve (λHU), in non-enhanced images were measured. The λHU was calculated as follows: λHU = CT70 keV - CT40 keV/30 HU. Additionally, typical radiological features were analyzed. A DSCT parameter diagnostic model and a conventional CT diagnostic model were assessed using receiver operating characteristic (ROC) curves. The Delong test was used to assess and compare the diagnostic performance of each model. The DSCT parameters, including the Zeff (P<0.001), λHU (P<0.001), and CT attenuation values at 40 keV (P<0.001) and 50 keV (P=0.001), as well as the presence of the lobular sign (P<0.001) and spicule sign (P<0.001), exhibited statistically significant differences between the benign and malignant groups. The logistic regression analysis revealed that the Zeff [odds ratio (OR): 9.22; 95% confidence interval (CI): 2.11-40.35; P=0.003], λHU (OR: 0.64; 95% CI: 0.52-0.79; P<0.001), 40 keV CT attenuation value (OR: 8.69; 95% CI: 3.28-23.06; P<0.001), 50 keV CT attenuation value (OR: 0.01; 95% CI: 0.001-0.07; P<0.001), and lobular sign (OR: 3.95; 95% CI: 1.52-10.31; P=0.005) were independent predictors of malignancy. Compared to the benign group, the malignant group had a higher likelihood of presenting with the lobular sign and higher Zeff values but lower λHU values. The ROC curve indicated that the Zeff had the highest diagnostic efficacy [area under the curve (AUC) of the ROC =0.792, 95% CI: 0.71-0.87]. Further, the DSCT parameter diagnostic model had improved diagnostic efficacy with an AUC of 0.899 (95% CI: 0.84-0.96), which was higher than the AUC of the conventional CT diagnostic model (AUC =0.796, 95% CI: 0.72-0.87). The Delong test revealed a statistically significant difference between these two models (P=0.04). DSCT parameters derived from non-enhanced DSCT images, such as the Zeff value and λHU, can be used to differentiate benign and malignant breast nodules, and the differential diagnosis efficacy of the DSCT parameters is higher than that of conventional CT parameters.

Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department

Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology can assist in interpreting non-enhanced brain CT scans, but its real-world impact on clinical decision-making is uncertain. This study assessed a deep learning-based intracranial hemorrhage detection algorithm (DLHD) in a simulated clinical environment with ten emergency medical professionals from a tertiary hospital’s ED. The participants reviewed CT scans with clinical information in two steps: without and with DLHD. Diagnostic performance was measured, including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Consistency in clinical decision-making was evaluated using the kappa statistic. The results demonstrated that DLHD minimally affected experienced participants’ diagnostic performance and decision-making. In contrast, inexperienced participants exhibited significantly increased sensitivity (59.33–72.67%, p < 0.001) and decreased specificity (65.49–53.73%, p < 0.001) with the algorithm. Clinical decision-making consistency was moderate among inexperienced professionals (k = 0.425) and higher among experienced ones (k = 0.738). Inexperienced participants changed their decisions more frequently, mainly due to the algorithm’s false positives. The study highlights the need for thorough evaluation and careful integration of deep learning tools into clinical workflows, especially for less experienced professionals.

Non-alcoholic fatty liver disease: A new predictor of recurrent ischemic stroke and transient ischemic attack in patients with carotid atherosclerosis

PurposeNon-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. This study focused on assessing the predictive significance of NAFLD for recurrent stroke and transient ischemic attack (TIA) risk to determine the value of NAFLD. MethodThis study included 742 participants (mean age: 64.26 ± 9.42 years, 497 males) with carotid atherosclerosis who underwent carotid CT angiography (CTA) between January 2013 and December 2021 in this retrospective study. NAFLD was diagnosed by non-enhanced abdominal CT. The clinical endpoint was a recurrent ischemic stroke or TIA. Cox proportional hazards and Kaplan-Meier analysis assessed whether NAFLD was associated with the endpoint. We accessed the predictive values of NAFLD, clinical, plaque characteristics, and combined model by the C statistics. The predictive performance of the combined model was assessed by receiver operating characteristic curve (ROC) analysis. ResultsA total of 742 participants (mean age: 64.26 ± 9.42 years, 497 males) were included. During 2.9 years of follow-up (interquartile range, 2.1–3.9), 166 patients reached the clinical endpoint. Multivariable cox analyses showed NAFLD was associated with recurrent stroke or TIA in all groups (all P<0.05). Patients with NAFLD had a lower event-free survival (EFS) rate than those without NAFLD (P<0.05). The combined model, including NAFLD, clinical data and plaque features, showed the best performance in predicting the clinical endpoint (AUC=0.79). ConclusionsNAFLD contributes to the prediction of recurrent ischemic stroke or TIA. NAFLD may be a novel imaging marker that offers a new perspective on preventing cardiovascular disease in the clinic.

Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis

Abstract Purpose To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs. Methods Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland–Altman analysis and intraclass correlation coefficients (ICC). Results The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between − 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970–0.999 and 0.994–0.999, respectively), uniformity (ICCs, 0.985–0.998), entropy (ICCs, 0.931–0.993), elongation (ICCs, 0.978–0.992), and flatness (ICCs, 0.973–0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210–0.831), kurtosis (ICCs, 0.053–0.933), and sphericity (ICCs, 0.368–0.819) displayed relatively low and inconsistent agreement. Conclusion Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.

The change of epicardial adipose tissue characteristics and vulnerability for atrial fibrillation upon drastic weight loss

ABSTRACT Background Obesity increases the risk of atrial fibrillation (AF). We hypothesize that ‘obese’ epicardial adipose tissue (EAT) is, regardless of comorbidities, associated with markers of AF vulnerability Methods Patients >40y of age undergoing bariatric surgery and using <2 antihypertensive drugs and no insulin were prospectively included. Study investigations were conducted before and 1y after surgery. Heart rhythm and p-wave duration were measured through ECGs and 7-d-holters. EAT-volume and attenuation were determined on non-enhanced CT scans. Serum markers were quantified by ELISA. Results Thirty-seven patients underwent surgery (age: 52.1 ± 5.9y; 27 women; no AF). Increased p-wave duration correlated with higher BMI, larger EAT volumes, and lower EAT attenuations (p < 0.05). Post-surgery, p-wave duration decreased from 109 ± 11 to 102 ± 11ms. Concurrently, EAT volume decreased from 132 ± 49 to 87 ± 52ml, BMI from 43.2 ± 5.2 to 28.9 ± 4.6kg/m2, and EAT attenuation increased from –76.1 ± 4.0 to −71.7 ± 4.4HU (p <0.001). Adiponectin increased from 8.7 ± 0.8 to 14.2 ± 1.0 μg/ml (p <0.001). However, decreased p-wave durations were not related to changed EAT characteristics, BMI or adiponectin. Conclusion In this explorative study, longer p-wave durations related to higher BMIs, larger EAT volume, and lower EAT attenuations. P-wave duration and EAT volume decreased, and EAT attenuation increased upon drastic weightloss. However, there was no relation between decreased p-wave duration and changed BMI or EAT characteristics.

Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans

This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans with normal appearance of the left UUT were chosen for this study. The dataset was divided into training (n = 130) and validation sets (n = 20). The test set contained 29 randomly chosen cases with computed tomography urography (CTU) and NECT scans, all with normal appearance of the left UUT. An experienced radiologist marked out the left renal pelvis and ureter on each scan. Two types of frameworks (entire and sectional) with three types of DL models (basic UNet, UNet3 + and ViT-UNet) were developed, and evaluated. The sectional framework with basic UNet model achieved the highest mean precision (85.5%) and mean recall (71.9%) on the test set compared to all other tested methods. Compared with CTU scans, this method had higher axial UUT recall than CTU (82.5% vs 69.1%, P < 0.01). This method achieved similar or better visualization of UUT than CTU in many cases, however, in some cases, it exhibited a non-ignorable false-positive rate. The proposed DL method demonstrates promising potential in automated 3D UUT segmentation on NECT scans. The proposed DL models could remarkably improve the efficiency of UUT reconstruction, and have the potential to save many patients from invasive examinations such as CTU. DL models could also serve as a valuable complement to CTU.

A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.

Dedicated brain imaging for cancer patients is seldom recommended in the absence of symptoms. There is increasing availability of non-enhanced CT (NE-CT) of the brain, mainly owing to a wider utilization of Positron Emission Tomography-CT (PET-CT) in cancer staging. Brain metastases (BM) are often hard to diagnose on NE-CT. This work aims to develop a 3D Convolutional Neural Network (3D-CNN) based on brain NE-CT to distinguish patients with and without BM. We retrospectively included NE-CT scans for 100 patients with single or multiple BM and 100 patients without brain imaging abnormalities. Patients whose largest lesion was < 5mm were excluded. The largest tumor was manually segmented on a matched contrast-enhanced T1 weighted Magnetic Resonance Imaging (MRI), and shape radiomics were extracted to determine the size and volume of the lesion. The brain was automatically segmented, and masked images were normalized and resampled. The dataset was split into training (70%) and validation (30%) sets. Multiple versions of a 3D-CNN were developed, and the best model was selected based on accuracy (ACC) on the validation set. The median largest tumor Maximum-3D-Diameter was 2.29cm, and its median volume was 2.81cc. Solitary BM were found in 27% of the patients, while 49% had > 5 BMs. The best model consisted of 4 convolutional layers with 3D average pooling layers, dropout layers of 50%, and a sigmoid activation function. Mean validation ACC was 0.983 (SD: 0.020) and mean area under receiver-operating characteristic curve was 0.983 (SD: 0.023). Sensitivity was 0.983 (SD: 0.020). We developed an accurate 3D-CNN based on brain NE-CT to differentiate between patients with and without BM. The model merits further external validation.

Association Between IV Contrast Media Exposure and Acute Kidney Injury in Patients Requiring Emergency Admission: A Nationwide Observational Study in Japan.

This study aimed to elucidate the association between IV contrast media CT and acute kidney injury (AKI) and in-hospital mortality among patients requiring emergency admission. In this retrospective observational study, we examined AKI within 48 hours after CT, renal replacement therapy (RRT) dependence at discharge, and in-hospital mortality in patients undergoing contrast-enhanced CT or nonenhanced CT. We performed 1:1 propensity score matching to adjust for confounders in the association between IV contrast media use and outcomes. Subgroup analyses were performed according to age, sex, diagnosis at admission, ICU admission, and preexisting chronic kidney disease (CKD). This study used the Medical Data Vision database between 2008 and 2019. This database is Japan's largest commercially available hospital-based claims database, covering about 45% of acute-care hospitals in Japan, and it also records laboratory results. None. The study included 144,149 patients with (49,057) and without (95,092) contrast media exposure, from which 43,367 propensity score-matched pairs were generated. Between the propensity score-matched groups of overall patients, exposure to contrast media showed no significant risk of AKI (4.6% vs. 5.1%; odds ratio [OR], 0.899; 95% CI, 0.845-0.958) or significant risk of RRT dependence (0.6% vs. 0.4%; OR, 1.297; 95% CI, 1.070-1.574) and significant benefit for in-hospital mortality (5.4% vs. 6.5%; OR, 0.821; 95% CI, 0.775-0.869). In subgroup analyses regarding preexisting CKD, exposure to contrast media was a significant risk for AKI in patients with CKD but not in those without CKD. In this large-scale observational study, IV contrast media was not associated with an increased risk of AKI but concurrently showed beneficial effects on in-hospital mortality among patients requiring emergency admission.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

Radiological signs of stone impaction add no value in predicting spontaneous stone passage

Stone size and location are key factors in predicting spontaneous stone passage (SSP), but little attention has been paid to the influence of radiological signs of stone impaction (RSSI). This research aims to determine whether RSSI, alongside stone size, can predict SSP and to evaluate the consistency of ureteral wall thickness (UWT) measurements among observers. In this retrospective study, 160 patients with a single upper or middle ureteral stone on acute non-enhanced computed tomography (NCCT) were analysed. Patient data were collected from medical records. Measurements of RSSI, including UWT, ureteral diameters, and average attenuation above and below the stone, were taken on NCCT by four independent readers blind to the outcomes. The cohort consisted of 70% males with an average age of 51 ± 15. SSP occurred in 61% of patients over 20 weeks. The median stone length was 5.7 mm (IQR: 4.5–7.3) and was significantly shorter in patients who passed their stones at short- (4.6 vs. 7.1, p < 0.001) and long-term (4.8 vs. 7.1, p < 0.001) follow-up. For stone length, the area under the receiver operating characteristic curve (AUC) for predicting SSP was 0.90 (CI 0.84–0.96) and only increased to 0.91 (CI 0.85–0.95) when adding ureteral diameters and UWT. Ureteral attenuation did not predict SSP (AUC < 0.5). Interobserver variability for UWT was moderate, with ± 2.0 mm multi-reader limits of agreement (LOA). The results suggest that RSSI do not enhance the predictive value of stone size for SSP. UWT measurements exhibit moderate reliability with significant interobserver variability.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model

BackgroundThe diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis.Methods420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists’ diagnoses.ResultsThe DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS.ConclusionsA CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.

Zero-echo time MRI: an alternative method for the diagnosis of urinary stones in children.

To evaluate the potential of zero-echo time-magnetic resonance imaging (ZTE-MRI) in the assessment of urolithiasis and compare ZTE-MRI with computed tomography (CT) in pediatric patients. This was a single-center, prospective cross-sectional study conducted between April 2023 and December 2023. 23 patients (12 girls, 11 boys; mean age: 12.3, range 1-18) with urinary tract stones detected on non-enhanced abdominal CT were enrolled. The images were evaluated independently by two radiologists for the presence, and number of stones in the kidneys, ureters, and bladder. In the second session, two radiologists evaluated whether urinary tract stones could be detected by MRI compared to CT, and the maximum diameter of the stones was measured. The CT and MRI results were compared with the Wilcoxon test. The agreement between the results of the observers was examined using Spearman's rho correlation coefficient and the intraclass correlation coefficient. A total of 58 urinary tract stones were detected by CT and 39 of these were detected by MRI. Most of the stones that MRI could not detect were < 5 mm and the detection sensitivity of MRI increased in correlation with stone size (p < 0.001). There was poor intermodality agreement for stones < 5 mm, substantial agreement for stones 5-10 mm, and almost perfect agreement for stones > 10 mm. Interobserver agreement for stone detection on MRI was almost perfect for stones > 10 mm and 5-10 mm and was substantial for stones < 5 mm. ZTE-MRI is a promising modality for detecting urinary stones without radiation exposure in children. Zero-echo time-magnetic resonance imaging is a potential method for identifying urinary stones in children and other populations who are particularly sensitive to radiation. Urinary system stone disease in children is increasing and imaging is needed for managing urolithiasis. Zero-echo time-magnetic resonance imaging (ZTE-MRI) had an accuracy of 81.8% and 93.7% for stones larger than 5 mm and 10 mm, respectively. ZTE-MRI is a potential non-irradiating method for the diagnosis and management of urolithiasis.