Unenhanced CT Research Articles

Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans

PurposeTo evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI).Materials and methodsThis retrospective study was conducted using a prototype algorithm that evaluated 502 NCCT head scans with ICH in context of TBI.Four board-certified radiologists evaluated in consensus the CT scans to establish the standard of reference for hemorrhage presence and type of ICH. Consequently, all CT scans were independently analyzed by the algorithm and a board-certified radiologist to assess the presence and type of ICH. Additionally, the time to diagnosis was measured for both methods.ResultsA total of 405/502 patients presented ICH classified in the following types: intraparenchymal (n = 172); intraventricular (n = 26); subarachnoid (n = 163); subdural (n = 178); and epidural (n = 15). The algorithm showed high diagnostic accuracy (91.24%) for the assessment of ICH with a sensitivity of 90.37% and specificity of 94.85%. To distinguish the different ICH types, the algorithm had a sensitivity of 93.47% and a specificity of 99.79%, with an accuracy of 98.54%. To detect midline shift, the algorithm had a sensitivity of 100%. In terms of processing time, the algorithm was significantly faster compared to the radiologist’s time to first diagnosis (15.37 ± 1.85 vs 277 ± 14 s, p < 0.001).ConclusionA novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists’ ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN

Objective. Auto-segmentation in mouse micro-CT enhances the efficiency and consistency of preclinical experiments but often struggles with low-native-contrast and morphologically complex organs, such as the spleen, resulting in poor segmentation performance. While CT contrast agents can improve organ conspicuity, their use complicates experimental protocols and reduces feasibility. We developed a 3D Cycle Generative Adversarial Network (CycleGAN) incorporating anatomy-constrained U-Net models to leverage contrast-enhanced CT (CECT) insights to improve unenhanced native CT (NACT) segmentation. Approach. We employed a standard CycleGAN with an anatomical loss function to synthesize virtual CECT images from unpaired NACT scans at two different resolutions. Prior to training, two U-Nets were trained to automatically segment six major organs in NACT and CECT datasets, respectively. These pretrained 3D U-Nets were integrated during the CycleGAN training, segmenting synthetic images, and comparing them against ground truth annotations. The compound loss within the CycleGAN maintained anatomical fidelity. Full image processing was achieved for low-resolution datasets, while high-resolution datasets employed a patch-based method due to GPU memory constraints. Automated segmentation was applied to original NACT and synthetic CECT scans to evaluate CycleGAN performance using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95p). Main results. High-resolution scans showed improved auto-segmentation, with an average DSC increase from 0.728 to 0.773 and a reduced HD95p from 1.19 mm to 0.94 mm. Low-resolution scans benefited more from synthetic contrast, showing a DSC increase from 0.586 to 0.682 and an HD95p reduction from 3.46 mm to 1.24 mm. Significance. Implementing CycleGAN to synthesize CECT scans substantially improved the visibility of the mouse spleen, leading to more precise auto-segmentation. This approach shows the potential in preclinical imaging studies where contrast agent use is impractical.

Abdominal aneurysm sac thrombus CT density and volume after EVAR: which association with underlying endoleak?

BackgroundOur aim was to analyse abdominal aneurysm sac thrombus density and volume on computed tomography (CT) after endovascular aneurysm repair (EVAR).MethodsPatients who underwent EVAR between January 2005 and December 2010 and had at least four follow-up CT exams available over the first five years of follow-up were included in this retrospective single-centre study. Thrombus density and aneurysm sac volume were calculated on unenhanced CT scans. Linear mixed models were used for data analysis.ResultsOut of 82 patients, 44 (54%) had an endoleak on post-EVAR contrast-enhanced CT. Thrombus density significantly increased over time in both the endoleak and non-endoleak groups, with a slope of 0.159 UH/month (95% confidence interval [CI] 0.115–0.202), p < 0.0001) and 0.052 UH/month (95% CI 0.002–0.102, p = 0.041). In patients without endoleak, a significant decrease in aneurysm sac volume was identified over time (slope -0.891 cc/month, 95% CI -1.200 to -0.581); p < 0.001) compared to patients with endoleak (slope 0.284 cc/month, 95% CI -0.031 to 0.523, p = 0.082). The association between thrombus density and aneurysm sac volume was positive in the endoleak group (slope 1.543 UH/cc, 95% CI 0.948–2.138, p < 0.001) and negative in the non-endoleak group (slope -1.450 UH/cc, 95% CI -2.326 to -0.574, p = 0.001).ConclusionWe observed a progressive increase in thrombus density of the aneurysm sac after EVAR in patients with and without endoleak, more pronounced in patients with endoleak. The association between aneurysm volume and thrombus density was positive in patients with and negative in those without endoleak.Relevance statementA progressive increase in thrombus density and volume of abdominal aortic aneurysm sac on unenhanced CT might suggest underlying endoleak lately after EVAR.Key PointsThrombus density of the aneurysm sac after EVAR increased over time.Progressive increase in thrombus density was significantly associated to the underlying endoleak.The association between aneurysm volume and thrombus density was positive in patients with and negative in those without endoleak.Graphical

Impact of different peak tube voltage settings on adrenal adenomas attenuation at unenhanced CT.

To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT. IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion's surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis). We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1-4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92-97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (-1 HU, IQR (-5 to 3.6), vs, 2.5 HU, IQR (-1.5 to 8.5); p < 0.001) whereas we didn't find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones. AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at "standard" 120 kVp. AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis. CT scans are often acquired using peak tube voltage settings different from the "standard" 120 kVp. AA attenuation varies if CT scans are acquired using different tube peak voltage settings. At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.

Unenhanced computed tomography as a diagnostic tool in suspected pulmonary hypertension: a retrospective cross-sectional pilot study.

Computed tomography pulmonary angiography (CTPA) has been proposed to be diagnostic for pulmonary hypertension (PH) in multiple studies. However, the utility of the unenhanced CT measurements diagnosing PH has not been fully assessed. This study aimed to assess the diagnostic utility and reproducibility of cardiac and great vessel parameters on unenhanced computed tomography (CT) in suspected pulmonary hypertension (PH). In total, 42 patients with suspected PH who underwent unenhanced CT thorax and right heart catheterization (RHC) were included in the study. Three observers (a consultant radiologist, a specialist registrar in radiology, and a medical student) measured the parameters by using unenhanced CT. Diagnostic accuracy of the parameters was assessed by area under the receiver operating characteristic curve (AUC). Inter-observer variability between the consultant radiologist (primary observer) and the two secondary observers was determined by intra-class correlation analysis (ICC). Overall, 35 patients were diagnosed with PH by RHC while 7 patients were not. Main pulmonary arterial (MPA) diameter was the strongest (AUC 0.79 to 0.87) and the most reproducible great vessel parameter. ICC comparing the MPA diameter measurement of the consultant radiologist to the specialist registrar's and the medical student's were 0.96 and 0.92, respectively. Right atrial area was the cardiac measurement with highest accuracy and reproducibility (AUC 0.76 to 0.79; ICC 0.980, 0.950) followed by tricuspid annulus diameter (AUC 0.76 to 0.79; ICC 0.790, 0.800). MPA diameter and right atrial areas showed high reproducibility. Diagnostic accuracies of these were within the range of acceptable to excellent, and might have clinical value. Tricuspid annular diameter was less reliable and less diagnostic and was therefore not a recommended diagnostic measurement.

Diagnostic accuracy of low-dose dual-input computed tomography perfusion in the differential diagnosis of pulmonary benign and malignant ground-glass nodules

This study aimed to evaluate the value of low-dose dual-input computed tomography perfusion (CTP) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGO). A retrospective study was conducted in patients with GGO who underwent CTP in our hospital from January 2021 to October 2023. All nodules were confirmed via pathological analysis or disappeared during follow-up. Postprocessing analysis was conducted using the dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CTP (348 mGy.cm) was < 75% of the diagnostic reference level of the unenhanced chest CT (470 mGy.cm). The effective radiation dose was 4.872 mSV. The blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) of malignant nodules were higher than those of the benign nodules (p < 0.05). The FEP had the highest accuracy for the diagnosis of malignant nodules (area under the curve [AUC] = 0.821, 95% confidence interval [CI]: 0.735–0.908) followed by BV (AUV = 0.713, 95% CI 0.608–0.819), BF (AUC = 0.688, 95% CI 0.587–0.797), and MTT (AUC = 0.616, 95% CI 0.506–0.726). When the FEP was ≥ 19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. To distinguish between benign nodules and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI 0.728–0.903), whereas the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI 0.729–0.900). Low-dose dual-input perfusion CT was extremely effective in distinguishing between benign from malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability.

Predicting of Hepatic Steatosis in Living Liver Donor via CT Liver Attenuation Index (LAI) and Fibroscan Controlled Attenuation Parameter (CAP) Correlation with Biopsy Result

Abstract Background The most prevalent persistent parenchymatous liver alterations in healthy individuals are thought to be hepatic steatosis. The liver biopsy is the most crucial procedure for the identification and measurement of hepatic steatosis. By identifying the liver attenuation index (LAI) at an unenhanced CT image, hepatic steatosis can be evaluated without the risk of liver resection. Objective Using liver biopsy histological analysis as a reference standard, to examine the precision of the CT liver attenuation index (LAI) and fibroscan controlled attenuation parameter (CAP) for quantitative evaluation of macro vesicular steatosis in living related liver donors. Methods In this study comparing CT liver attenuation index &amp; fibroscan controlled attenuation parameter with liver pathology of steatosis in subject's candidate for liver living donors 50 subjects in each two study's correlation with biopsy results, conducted at Ain Shams Shams Specialized hospital and other Private hospitals over about 2 years. Results Our study reported that liver – spleen attenuation index of 9 is the cutoff value with sensitivity 100% and specificity 80% that make it a very good method to exclude donor to have steatosis ≥15%, which mean if donor had LS index &amp;lt; 9 we can safely do proceed do liver biopsy. Our study reported that CAP measurement had an AUROC OF 0.780, for detecting steatosis ≥ 15 %, with sensitivity is only 60% with specificity as CT LAI of 80%, which consider low compared to other study, that could be due to small number of donors in out study with steatosis ≥ 15 % (5 case from 50 donors) unlike the other studies. Conclusion When used to estimate the amount of liver fat in liver donors, the examined CAP and CT indices worked equally. But according to multivariate analysis, the only factor strongly linked with hepatic steatosis in alive donors was the CT L-S attenuation index. We contend that the combination of CT L-S attenuation index and CAP allows for the detection of the degree of hepatic steatosis and can be used as an option to liver biopsy, reserving liver biopsy for those with positive steatosis donors.

Submillisievert Abdominal Photon-Counting CT versus Energy-integrating Detector CT for Urinary Calculi Detection: Impact on Diagnostic Confidence.

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group (P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

Prevalence of metabolic syndrome and its association with CT-based central adiposity measures: a cross-sectional study at a tertiary care hospital in Pakistan

ObjectivesTo assess the prevalence of metabolic syndrome (MS) and association of central obesity measures such as body mass index (BMI), visceral fat adiposity (VFA) and superficial fat adiposity (SFA) with...

Limited Additional Value of a Chest CT in Whole-Body Staging with PET-MRI: A Retrospective Cohort Study.

Hybrid PET-MRI systems are being used more frequently. One of the drawbacks of PET-MRI imaging is its inferiority in detecting lung nodules, so it is often combined with a computed tomography (CT) of the chest. However, chest CT often detects additional, indeterminate lung nodules. The objective of this study was to assess the sensitivity of detecting metastatic versus indeterminate nodules with PET-MRI compared to chest CT. A total of 328 patients were included. All patients had a PET/MRI whole-body scan for (re)staging of cancer combined with an unenhanced chest CT performed at our center between 2014 and 2020. Patients had at least a two-year follow-up. Six percent of the patients had lung metastases at initial staging. The sensitivity and specificity of PET-MRI for detecting lung metastases were 85% and 100%, respectively. The incidence of indeterminate lung nodules on chest CT was 30%. The sensitivity of PET-MRI to detect indeterminate lung nodules was poor (23.0%). The average size of the indeterminate lung nodules detected on PET-MRI was 7 ± 4 mm, and the missed indeterminate nodules on PET-MRI were 4 ± 1 mm (p < 0.001). The detection of metastatic lung nodules is fairly good with PET-MRI, whereas the sensitivity of PET-MRI for detecting indeterminate lung nodules is size-dependent. This may be an advantage, limiting unnecessary follow-up of small, indeterminate lung nodules while adequately detecting metastases.

Systemic immune-inflammation index mediates the association between metabolic dysfunction-associated fatty liver disease and sub-clinical carotid atherosclerosis: a mediation analysis.

Limited research has been conducted to quantitatively assess the impact of systemic inflammation in metabolic dysfunction-associated fatty liver disease (MAFLD) and sub-clinical carotid atherosclerosis (SCAS). The systemic immune-inflammation index (SII), which integrates inflammatory cells, has emerged as a reliable measure of local immune response and systemic inflammation Therefore, this study aims to assess the mediating role of SII in the association between MAFLD and SCAS in type 2 diabetes mellitus (T2DM). This study prospectively recruited 830 participants with T2DM from two centers. Unenhanced abdominal CT scans were conducted to evaluate MAFLD, while B-mode carotid ultrasonography was performed to assess SCAS. Weighted binomial logistic regression analysis and restricted cubic splines (RCS) analyses were employed to analyze the association between the SII and the risk of MAFLD and SCAS. Mediation analysis was further carried out to explore the potential mediating effect of the SII on the association between MAFLD and SCAS. The prevalence of both MAFLD and SCAS significantly increased as the SII quartiles increased (P<0.05). MAFLD emerged as an independent factor for SCAS risk across three adjusted models, exhibiting odds ratios of 2.15 (95%CI: 1.31-3.53, P < 0.001). Additionally, increased SII quartiles and Ln (SII) displayed positive associations with the risk of MAFLD and SCAS (P < 0.05). Furthermore, a significant dose-response relationship was observed (P for trend <0.001). The RCS analyses revealed a linear correlation of Ln (SII) with SCAS and MAFLD risk (P for nonlinearity<0.05). Importantly, SII and ln (SII) acted as the mediators in the association between MAFLD and SCAS following adjustments for shared risk factors, demonstrating a proportion-mediated effect of 7.8% and 10.9%. SII was independently correlated with MAFLD and SCAS risk, while also acting as a mediator in the relationship between MAFLD and SCAS.

CT-Based AI model for predicting therapeutic outcomes in ureteral stones after single extracorporeal shock wave lithotripsy through a cohort study.

Exploring the efficacy of an artificial intelligence (AI) model derived from the analysis of CT images to precisely forecast the therapeutic outcomes of singular-session extracorporeal shock wave lithotripsy (ESWL) in the management of ureteral stones. A total of 317 patients diagnosed clinically with ureteral stones were included in this investigation. Unenhanced CT was administered to the participants within the initial fortnight preceding the inaugural ESWL. The internal cohort consisted of 250 individuals from a local healthcare facility, whereas the external cohort comprised 67 participants from another local medical institution. The proposed framework comprises three main components: an automated semantic segmentation model developed using 3D U-Net, a feature extractor that integrates radiomics and autoencoder techniques, and an ESWL efficacy prediction model trained with various machine learning algorithms. All participants underwent thorough postoperative follow-up examinations four weeks hence. The efficacy of ESWL was defined by the absence of stones or residual fragments measuring ≤2mm in KUB X-ray assessments. Model stability and generalizability were judiciously validated through a fivefold cross-validation approach and a multi-center external test strategy. Moreover, Shapley Additive Explanations (SHAP) values for individual features were computed to elucidate the nuanced contributions of each feature to the model's decision-making process. The semantic segmentation model we constructed exhibited an average Dice coefficient of 0.88 ± 0.08 on the external testing set. ESWL classifiers built using Support Vector Machine (SVM), Random Forest (RF), XGBoost (XB), and CatBoost (CB) achieved AUROC values of 0.78, 0.84, 0.85, and 0.90, respectively, on the internal validation set. For the external testing set, SVM, RF, XB, and CB predicted ESWL with AUROC values of 0.68, 0.79, 0.80, and 0.83, respectively, with the last one being the optimal algorithm. The radiomics features and auto-encoder features made significant contributions to the decision-making process of the classification model. This investigation unmistakably underscores the remarkable predictive prowess exhibited by a scrupulously crafted AI model using CT images to precisely anticipate the therapeutic results of a singular session of extracorporeal shock wave lithotripsy for ureteral stones.

Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study

ObjectiveTo develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. Methods183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. ResultsLASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. ConclusionCombined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD. In this study, we evaluated 194 patients aged 50 years or greater (69.2 ± 9.1 years; 170 females) who underwent unenhanced shoulder CT scans and dual-energy X-ray absorptiometry within 1 year of each other between January 1, 2010, and December 31, 2021. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs was obtained using 3D-Slicer. Support vector machines (SVMs) and k-nearest neighbours (kNN) were used to predict low BMD. DeLong test was used to compare the areas under the curve (AUCs). A CT attenuation of 195.4 Hounsfield Units of the clavicle had a sensitivity of 0.577, specificity of 0.781, and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00, and AUC of 0.857, while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = .003) but not better than the kNN model (P = .098). The CT attenuation of the clavicle was best for predicting low BMD; however, a multivariable SVM was superior for predicting low BMD. SVM utilizing the CT attenuations at many sites was best for predicting low BMD.

Performance of CT With Adrenal-Washout Protocol in Heterogeneous Adrenal Nodules: A Multiinstitutional Study.

BACKGROUND. CT with adrenal-washout protocol (hereafter, adrenal-protocol CT) is commonly performed to distinguish adrenal adenomas from other adrenal tumors. However, the technique's utility among heterogeneous nodules is not well established, and the optimal method for placing ROIs in heterogeneous nodules is not clearly defined. OBJECTIVE. The purpose of our study was to determine the diagnostic performance of adrenal-protocol CT to distinguish adenomas from nonadenomas among heterogeneous adrenal nodules and to compare this performance among different methods for ROI placement. METHODS. This retrospective study included 164 patients (mean age, 59.1 years; 61 men, 103 women) with a total of 164 heterogeneous adrenal nodules evaluated using adrenal-protocol CT at seven institutions. All nodules had an available pathologic reference standard. A single investigator at each institution evaluated the CT images. ROIs were placed on portal venous phase images using four ROI methods: standard ROI, which refers to a single large ROI in the nodule's center; high ROI, a single ROI on the nodule's highest-attenuation area; low ROI, a single ROI the on nodule's lowest-attenuation area; and average ROI, the mean of the three ROIs on the nodule's superior, middle, and inferior thirds using the approach for the standard ROI. ROIs were then placed in identical locations on unenhanced and delayed phase images. Absolute washout was determined for all methods. RESULTS. The nodules comprised 82 adenomas and 82 nonadenomas (36 pheochromocytomas, 20 metastases, 12 adrenocortical carcinomas, and 14 nodules with other pathologies). The mean nodule size was 4.5 ± 2.8 (SD) cm (range, 1.6-23.0 cm). Unenhanced CT attenuation of 10 HU or less exhibited sensitivity and specificity for adenoma of 22.0% and 96.3% for standard-ROI, 11.0% and 98.8% for high-ROI, 58.5% and 84.1% for low-ROI, and 30.5% and 97.6% for average-ROI methods. Adrenal-protocol CT overall (unenhanced attenuation ≤ 10 HU or absolute washout of ≥ 60%) exhibited sensitivity and specificity for adenoma of 57.3% and 84.1% for the standard-ROI method, 63.4% and 51.2% for the high-ROI method, 68.3% and 62.2% for the low-ROI method, and 59.8% and 85.4% for the average-ROI method. CONCLUSION. Adrenal-protocol CT has poor diagnostic performance for distinguishing adenomas from nonadenomas among heterogeneous adrenal nodules regardless of the method used for ROI placement. CLINICAL IMPACT. Adrenal-protocol CT has limited utility in the evaluation of heterogeneous adrenal nodules.

Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas

Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the stateoftheart in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

ObjectivesTo investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.Materials and methodsOne hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan–Meier method were used to evaluate the predictive performance of the scoring system.ResultsMultivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).ConclusionECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.Critical relevance statementThe scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.Key PointsPredicting the progression of LAPC in patients receiving IORT is important.Our ECV-based scoring system can risk stratifying patients with initial SD.Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.Graphical

Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders

BackgroundCardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders.MethodsA multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease.ResultsFor metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77).ConclusionsThis study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.