Non-enhanced CT Images Research Articles

CT-based radiomics analysis of different machine learning models for differentiating gnathic fibrous dysplasia and ossifying fibroma.

In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery. We enrolled 220 patients with confirmed FD or OF. We extracted radiomic features from nonenhanced CT images. Following dimensionality reduction and feature selection, we constructed radiomic models using logistic regression, support vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver operating characteristic (ROC) curve analysis. After combining radiomics features with clinical features, we developed a comprehensive model. ROC curve and decision curve analysis (DCA) demonstrated the models' robustness and clinical value. We extracted 1834 radiomic features from CT images, reduced them to eight valuable features, and achieved high predictive efficiency, with area under curves (AUC) exceeding 0.95 for all the models. Ultimately, our combined model, which integrates radiomic and clinical data, displayed superior discriminatory ability (AUC: training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinical efficacy. Our combined model effectively differentiates between FD and OF, offering a noninvasive and efficient approach to clinical decision-making.

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics. The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent. We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.

Prevalence of canine renal crest hyperattenuation in precontrast computed tomography.

Normal canine kidneys are relatively homogeneous soft tissue attenuating structures on nonenhanced CT images. However, visible differences in attenuation between the renal crest and medulla are occasionally observed. This finding and its potential clinical implications have not been previously investigated. This study aimed to estimate the prevalence of renal crest hyperattenuation (RCH) and investigate possible associations with signalment and laboratory parameters. Abdominal CT studies from 100 dogs, with biochemistry and urinalysis data obtained within 48h before the CT acquisition, were categorized by two radiologists into those with and without visible RCH. The attenuation in Hounsfield units (HU) of the renal crest and renal medulla were measured. Signalment, biochemical, and urinalysis data were analyzed for associations with RCH. Correlation coefficients were calculated for measured HU and associated continuous parameters. Prevalence of RCH was 42.42% (42/99 dogs, 95% CI, 33-52%). Urinary specific gravity (USG) was significantly different between dogs with and without RCH (P=.034). Weak positive correlations were identified between left and right renal crest attenuation and USG (r=0.233 and 0.253, respectively; P=.05). Renal crest hyperattenuation is a common finding in dogs undergoing abdominal CT. Although the correlation between the USG and renal crest HU is weak, the dogs with RCH have significantly higher USG. Renal crest hyperattenuation might, therefore, not be associated with renal insufficiency. No other specific associations of RCH were identified with parameters typically altered in a variety of diseases. Further investigation may be warranted for its relevance to specific diseases or if it indeed represents a physiological variant.

The value of CT-based radiomics in predicting hemorrhagic transformation in acute ischemic stroke patients without recanalization therapy.

The aim of this study is to investigate the clinical value of radiomics based on non-enhanced head CT in the prediction of hemorrhage transformation in acute ischemic stroke (AIS). A total of 140 patients diagnosed with AIS from January 2015 to August 2022 were enrolled. Radiomic features from infarcted areas on non-enhanced CT images were extracted using ITK-SNAP. The max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select features. The radiomics signature was then constructed by multiple logistic regressions. The clinicoradiomics nomogram was constructed by combining radiomics signature and clinical characteristics. All predictive models were constructed in the training group, and these were verified in the validation group. All models were evaluated with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Of the 140 patients, 59 experienced hemorrhagic transformation, while 81 remained stable. The radiomics signature was constructed by 10 radiomics features. The clinicoradiomics nomogram was constructed by combining radiomics signature and atrial fibrillation. The area under the ROC curve (AUCs) of the clinical model, radiomics signature, and clinicoradiomics nomogram for predicting hemorrhagic transformation in the training group were 0.64, 0.86, and 0.86, respectively. The AUCs of the clinical model, radiomics signature, and clinicoradiomics nomogram for predicting hemorrhagic transformation in the validation group were 0.63, 0.90, and 0.90, respectively. The DCA curves showed that the radiomics signature performed well as well as the clinicoradiomics nomogram. The DCA curve showed that the clinical application value of the radiomics signature is similar to that of the clinicoradiomics nomogram. The radiomics signature, constructed without incorporating clinical characteristics, can independently and effectively predict hemorrhagic transformation in AIS patients.

Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists.

To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data.

Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies

Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.

CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [18F]FDG PET uptake values.

Utilizing [18F]Fluoro-2-deoxy-D-glucose Positron Emission Tomography/Computed Tomography ([18F]FDG PET/CT) scans on primary colon cancer (CC) patients including with liver metastases (LM), we aimed to determine the relationship between structural CT radiomic features and metabolic PET standard uptake value (SUV) in these patients. A retrospective analysis was performed on 60 patients with primary CC, of which 40 had liver metastases that were more than 2 cm in diameter. [18F]FDG PET/CT was used to calculate SUVmax, and 42 CT radiomic characteristics were extracted from non-enhanced CT images. Tumors were manually segmented on fused PET/CT scans by two experienced nuclear medicine physicians. Sixty primary CC and forty LM lesions were segmented accordingly. In the cases of multiple LM lesions, the lesion with the largest diameter was chosen for segmentation. In a univariate analysis approach, we used Spearman correlation with multiple testing correction (Benjamini-Hutchberg false discovery rate (FDR), α=0.05) to ascertain the relationship between SUVmax and CT radiomic features. Twenty-two (52.3%) and twenty-six (61.9%) CT radiomic features were found to be significantly correlated with SUVmax values of primary CC (n=60) and LM (n=40) lesions, respectively (FDR-corrected p value <0.05 and 0.6 < |ρ| < 1). GLCM_homogeneity (ρ = 0.839), GLCM_dissimilarity (ρ = -0.832), GLZLM_ZLNU (ρ = 0.827), and GLCM_contrast (ρ = -0.815) were the 4 features most correlated with SUVmax in CC. On the other hand, in LM, the 4 features most correlated with SUVmax were GLRLM_LRHGE (ρ = 0.859), GLRLM_LRE (ρ = 0.859), GLRLM_LRLGE (ρ = 0.857), and GLRLM_RP (ρ = -0.820). We investigated the relationship between SUVmax of preoperative primary CC lesions and their LM with CT radiomic features. We found some CT radiomic features having relationships with the metabolic characteristics of lesions. This work suggests that non-invasive predictive imaging biomarkers for precision medicine can be derived from CT radiomic.

Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning.

The paper aims to develop prediction model that integrates clinical, radiomics, and deep features using transfer learning to stratifying between high and low risk of thymoma. Our study enrolled 150 patients with thymoma (76 low-risk and 74 high-risk) who underwent surgical resection and pathologically confirmed in Shengjing Hospital of China Medical University from January 2018 to December 2020. The training cohort consisted of 120 patients (80%) and the test cohort consisted of 30 patients (20%). The 2590 radiomics and 192 deep features from non-enhanced, arterial, and venous phase CT images were extracted and ANOVA, Pearson correlation coefficient, PCA, and LASSO were used to select the most significant features. A fusion model that integrated clinical, radiomics, and deep features was developed with SVM classifiers to predict the risk level of thymoma, and accuracy, sensitivity, specificity, ROC curves, and AUC were applied to evaluate the classification model. In both the training and test cohorts, the fusion model demonstrated better performance in stratifying high and low risk of thymoma. It had AUCs of 0.99 and 0.95, and an accuracy of 0.93 and 0.83, respectively. This was compared to the clinical model (AUCs of 0.70 and 0.51, accuracy of 0.68 and 0.47), the radiomics model (AUCs of 0.97 and 0.82, accuracy of 0.93 and 0.80), and the deep model (AUCs of 0.94 and 0.85, accuracy of 0.88 and 0.80). The fusion model integrating clinical, radiomics and deep features based on transfer learning was efficient for noninvasively stratifying high risk and low risk of thymoma. The models could help to determine surgery strategy for thymoma cancer.

Comparison of two computed tomography perfusion post-processing software to assess infarct volume in patients with acute ischemic stroke.

We used two automated software commonly employed in clinical practice-Olea Sphere (Olea) and Shukun-PerfusionGo (PerfusionGo)-to compare the diagnostic utility and volumetric agreement of computed tomography perfusion (CTP)-predicted final infarct volume (FIV) with true FIV in patients with anterior-circulation acute ischemic stroke (AIS). In all, 122 patients with anterior-circulation AIS who met the inclusion and exclusion criteria were retrospectively enrolled and divided into two groups: intervention group (n = 52) and conservative group (n = 70), according to recanalization of blood vessels and clinical outcome (NIHSS) after different treatments. Patients in both groups underwent one-stop 4D-CT angiography (CTA)/CTP, and the raw CTP data were processed on a workstation using Olea and PerfusionGo post-processing software, to calculate and obtain the ischemic core (IC) and hypoperfusion (IC plus penumbra) volumes, hypoperfusion in the conservative group and IC in the intervention group were used to define the predicted FIV. The ITK-SNAP software was used to manually outline and measure true FIV on the follow-up non-enhanced CT or MRI-DWI images. Intraclass correlation coefficients (ICC), Bland-Altman, and Kappa analysis were used to compare the differences in IC and penumbra volumes calculated by the Olea and PerfusionGo software to investigate the relationship between their predicted FIV and true FIV. The IC and penumbra difference between Olea and PerfusionGo within the same group (p < 0.001) was statistically significant. Olea obtained larger IC and smaller penumbra than PerfusionGo. Both software partially overestimated the infarct volume, but Olea significantly overestimated it by a larger percentage. ICC analysis showed that Olea performed better than PerfusionGo (intervention-Olea: ICC 0.633, 95%CI 0.439-0.771; intervention-PerfusionGo: ICC 0.526, 95%CI 0.299-0.696; conservative-Olea: ICC 0.623, 95%CI 0.457-0.747; conservative-PerfusionGo: ICC 0.507, 95%CI 0.312-0.662). Olea and PerfusionGo had the same capacity in accurately diagnosing and classifying patients with infarct volume <70 ml. Both software had differences in the evaluation of the IC and penumbra. Olea's predicted FIV was more closely correlated with the true FIV than PerfusionGo's prediction. Accurate assessment of infarction on CTP post-processing software remains challenging. Our results may have important practice implications for the clinical use of perfusion post-processing software.

A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions.

United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration

Synthesizing abdominal contrast-enhanced computed tomography (CECT) images from non-enhanced CT (NECT) images is of great importance, in the delineation of radiotherapy target volumes, to reduce the risk of iodinated contrast agent and the registration error between NECT and CECT for transferring the delineations. NECT images contain structural information that can reflect the contrast difference between lesions and surrounding tissues. However, existing methods treat synthesis and registration as two separate tasks, which neglects the task collaborative and fails to address misalignment between images after the standard image pre-processing in training a CECT synthesis model. Thus, we propose an united multi-task learning (UMTL) for joint synthesis and deformable registration of abdominal CECT. Specifically, our UMTL is an end-to-end multi-task framework, which integrates a deformation field learning network for reducing the misalignment errors and a 3D generator for synthesizing CECT images. Furthermore, the learning of enhanced component images and the multi-loss function are adopted for enhancing the performance of synthetic CECT images. The proposed method is evaluated on two different resolution datasets and a separate test dataset from another center. The synthetic venous phase CECT images of the separate test dataset yield mean absolute error (MAE) of 32.78±7.27 HU, mean MAE of 24.15±5.12 HU on liver region, mean peak signal-to-noise rate (PSNR) of 27.59±2.45 dB, and mean structural similarity (SSIM) of 0.96±0.01. The Dice similarity coefficients of liver region between the true and synthetic venous phase CECT images are 0.96±0.05 (high-resolution) and 0.95±0.07 (low-resolution), respectively. The proposed method has great potential in aiding the delineation of radiotherapy target volumes.

Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study.

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.

Development and Validation of a Preoperative CT-Based Nomogram to Differentiate Invasive from Non-Invasive Pulmonary Adenocarcinoma in Solitary Pulmonary Nodules.

PurposeThis study aimed to develop and validate a preoperative CT-based nomogram combined with clinical and radiological features for distinguishing invasive from non-invasive pulmonary adenocarcinoma.Patients and MethodsA total of 167 patients with solitary pulmonary nodules and pathologically confirmed adenocarcinoma treated between January 2020 and December 2020 at Hebei General Hospital were retrospectively assessed. To evaluate the probability of invasive pulmonary adenocarcinoma, we developed three models, the multivariate logistic regression model, the stepwise logistic regression model, and the cross-validation model. The Akaike information criterion (AIC) was used to compare the relative strength of different models, and the area under the curve (AUC) was used to quantify the predictive accuracy. The best performing model was presented as a nomogram, calibrated and evaluated for clinical utility.ResultsThe stepwise logistic regression model revealed highest and mean attenuations of non-enhanced CT images, and lobulation and vacuole presence were predictive factors of invasive pulmonary adenocarcinoma. This model (AIC = 67.528) with the lowest AIC value compared with that of the multivariate logistic regression model (AIC = 69.301) or the cross-validation model (AIC = 81.216) was identified as the best model, and its AUC value (0.9967; 95% CI, 0.9887–1) was higher than those of the other two models. The calibration curve showed optimal agreement in invasive pulmonary adenocarcinoma probability as predicted by the nomogram and the actual value.ConclusionWe developed and validated a nomogram that could estimate the preoperative probability of invasive pulmonary adenocarcinoma in patients with solitary pulmonary nodules, which may be useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection.

CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal haemorrhage after mechanical thrombectomy

ObjectiveTo develop a nonenhanced CT-based radiomic signature for the differentiation of iodinated contrast extravasation from intraparenchymal haemorrhage (IPH) following mechanical thrombectomy.MethodsPatients diagnosed with acute ischaemic stroke who underwent mechanical thrombectomy in 4 institutions from December 2017 to June 2020 were included in this retrospective study. The study population was divided into a training cohort and a validation cohort. The nonenhanced CT images taken after mechanical thrombectomy were used to extract radiomic features. The maximum relevance minimum redundancy (mRMR) algorithm was used to eliminate confounding variables. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was used to generate the radiomic signature. The diagnostic performance of the radiomic signature was evaluated by the area under the curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).ResultsA total of 166 intraparenchymal areas of hyperattenuation from 101 patients were used. The areas of hyperattenuation were randomly allocated to the training and validation cohorts at a ratio of 7:3. The AUC of the radiomic signature was 0.848 (95% confidence interval (CI) 0.780–0.917) in the training cohort and 0.826 (95% CI 0.705–0.948) in the validation cohort. The accuracy of the radiomic signature was 77.6%, with a sensitivity of 76.7%, a specificity of 78.9%, a PPV of 85.2%, and a NPV of 68.2% in the validation cohort.ConclusionsThe radiomic signature constructed based on initial post-operative nonenhanced CT after mechanical thrombectomy can effectively differentiate IPH from iodinated contrast extravasation.Key Points• Radiomic features were extracted from intraparenchymal areas of hyperattenuation on initial post-operative CT scans after mechanical thrombectomy.• The nonenhanced CT-based radiomic signature can differentiate IPH from iodinated contrast extravasation early.• The radiomic signature may help prevent unnecessary rescanning after mechanical thrombectomy, especially in cases where contrast extravasation is highly suggestive.

A case report of a pediatric cerebral venous sinus thrombosis after a head injury detected with nonenhanced CT and MRI imaging

A case report of a pediatric cerebral venous sinus thrombosis after a head injury detected with nonenhanced CT and MRI imaging

Association of Hepatic Steatosis Index with Nonalcoholic Fatty Liver Disease Diagnosed by Non-Enhanced CT in a Screening Population.

The noninvasive diagnosis of hepatic steatosis is of increasing concern. This study investigated the association of hepatic steatosis determined by non-enhanced CT criteria with clinical parameters in a screening population. Asymptomatic patients who underwent abdominal CT at our healthcare center were retrospectively analyzed (n = 339). Two radiologists measured the attenuation values of the liver parenchyma and spleen using non-enhanced CT images. CT criteria for hepatic steatosis were (a) absolute liver attenuation value <48 Hounsfield units (HU), (b) liver-to-spleen attenuation ratio <0.8, and (c) attenuation difference between the liver and spleen <−10. Body mass index (BMI) and hepatic steatosis index (HSI) were calculated, and laboratory findings were recorded. The association of hepatic steatosis with clinical parameters was assessed using univariate and logistic regression analyses. The presence of hepatic steatosis was significantly associated with the levels of serum fasting glucose and triglycerides, the alanine aminotransferase to aspartate aminotransferase (ALT/AST) ratio, BMI, and HSI values using any of the CT criteria. Logistic regression analysis revealed that the serum fasting glucose level and HSI were significantly associated with hepatic steatosis using criterion (a), while the ALT/AST ratio and HSI were associated with hepatic steatosis using criteria (b) and (c). The presence of hepatic steatosis on non-enhanced CT should be considered to indicate possible clinical profile abnormalities regarding metabolic syndrome.

Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children

To explore the value of CT-based radiomics in differential diagnosis of retroperitoneal neuroblastoma (NB) and ganglioneuroblastoma (GNB) in children. A total of 172 children with NB and 48 children with GNB were assigned into the training set and testing set at the ratio of 7∶3 using a random stratified sampling method. Radiomics features were extracted and selected from non-enhanced and post-enhanced CT images. Based on the subset of optimal features, a multivariate regression model was used to establish the radiomics models for each phase and the combined radiomics models. The ROC curves of the models were drawn, and the evaluation indexes such as AUC, accuracy, sensitivity and specificity of these models were calculated and compared. A total of 1218 radiomics features were extracted from the CT images acquired in non-enhanced (NP), arterial (AP) and venous phases (VP), from which 4 features from the NP model, 3 features from the AP model, 2 features from the VP model and 5 features from the combined model were selected. The AUC of the NP model in the training set and testing set was 0.840 (95% CI: 0.778-0.902) and 0.804 (95% CI: 0.699-0.899), respectively, as compared with 0.819 (95%CI: 0.759-0.877) and 0.815 (95%CI: 0.697-0.915) for the AP model, 0.730 (95%CI: 0.649-0.803) and 0.751 (95%CI: 0.619-0.869) for the VP model, and 0.861 (95%CI: 0.809-0.910) and 0.827 (95%CI: 0.726-0.915) for the combined model. Radiomics signature based on non-enhanced and post-enhanced CT images can be helpful for distinguishing retroperitoneal NB and GNB in children. Compared with the first-order histogram features, textural features can better reflect the difference of the lesions. NP, AP and VP models have similar classification efficacy in differentiating retroperitoneal NB and GNB. The efficacy of the combined model is similar to that of the NP and AP models, but superior to that of the VP model.

Lipoprotein(a) is associated with incidence but not progression of aortic valve calcium

Abstract Background Lipoprotein(a) [Lp(a)] has been implicated in the etiology of aortic valve stenosis. Although Lp(a) is strongly associated with the presence of aortic valve calcium (AVC), there are no data evaluating the relationship of Lp(a) with AVC incidence and AVC progression. Purpose To assess whether high Lp(a) levels are associated with AVC incidence and progression. Methods In 922 individuals from the population-based Rotterdam Study (mean age 66.0±4.2 years, 47.7% men) for whom Lp(a) measurements were available, non-enhanced cardiac CT imaging was performed at baseline and after a median follow-up of 14.0 [13.9–14.2] years. AVC incidence was defined as an AVC score &amp;gt;0 on the second scan, in absence of AVC on the first scan. We performed logistic regression analyses to evaluate the relationship of Lp(a) with AVC incidence and linear regression analyses to assess the relationship between Lp(a) and AVC progression. All analyses were corrected for age, sex, body mass index, smoking, non-high-density lipoprotein cholesterol, use of lipid-lowering medication, and hypertension. Additionally, we analyzed the progression conditional on the baseline AVC-score. Results Of the 702 (76.1%) individuals without AVC at baseline, 415 (59.1%) showed incident AVC at follow-up. The 220 (23.9%) individuals with AVC on baseline had a median AVC score of 52 [15–131], with a median yearly progression of 13 [5–38]. Lp(a) concentration was independently associated with AVC incidence (OR 1.32 for each 105 nmol/L Lp(a) increase; 95% CI: 1.03–1.68), but not with AVC progression (β −4.3 AU/year for each 105 nmol/L Lp(a) increase; 95% CI: −12.3–3.7). Conclusions Lp(a) is associated with AVC incidence but not AVC progression, suggesting that Lp(a)-lowering interventions may be futile after AVC has been established. Future studies should focus on whether Lp(a) lowering interventions can prevent the development of AVC in high-risk individuals. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): The Rotterdam Study is supported by Erasmus MC and Erasmus University Rotterdam and the Netherlands Organization for Scientific Research

Hemodynamics-based analysis of factors associated with aneurysm rupture in different sides of the internal carotid artery

Objective: To explore the influence of hemodynamics, morphological and clinical characteristics on rupture risk of the dorsal and non-dorsal internal carotid artery aneurysms (ICAAs). Methods: A total of 111 patients diagnosed with aneurysm by digital subtraction angiography (DSA) or surgery, underwent cranial CT angiography (CTA) were retrospectively collected from January 2010 to December 2016 at the Department of Diagnostic Radiology, Jinling Clinical College of Xuzhou Medical University (General Hospital of Eastern Theater Command). Among them, 41 were males and 70 were females, ranging in age from 32 to 83 (56±11) years old. The patients were divided into the ruptured group (n=54) and unruptured group (n=57) based on the hemorrhagic manifestation on non-enhanced CT images or DSA or surgery of the head. Demographics and the morphological characteristics of the aneurysms were evaluated. Hemodynamic parameters, including wall share stress, wall share stress gradient, and others were obtained in overall using computational fluid dynamics simulation technique. Characteristics were compared between the ruptured and unruptured groups. Logistic regression analysis was applied to evaluate the independent risk factors for rupture, and the hemodynamic characteristics associated with dorsal and non-dorsal aneurysms were analyzed, respectively. Results: Compared with the unruptured group, patients in the ruptured group were younger ((54.2±11.4) years and (58.3±9.9) years, P=0.033), mostly female (74.1% vs 52.6%, P<0.05), with a higher proportion of hypertension (46.3% vs 22.8%, P=0.009). The ruptured internal carotid artery aneurysms (ICAAs) were more frequently located at the dorsal of an arterial arch (57.4% vs 36.8%, P<0.05), and the flow of the blood was more complex, concentrated, unstable, and with a smaller impingement zone (68.5% vs 33.3%,55.6% vs 10.5%,72.2% vs 26.3% and 79.6% vs 36.8%, respectively, all P<0.05). Logistic regression demonstrated that women, hypertension, dorsal, concentrated flow pattern, and unstable flow pattern were an independent risk factors for ICAAs rupture [OR=3.551 (1.080-11.679), 3.900 (1.172-12.976), 4.966 (1.504-16.401), 51.893 (7.913-340.296) and 50.015 (8.423-296.985), respectively, all P<0.05]. The ruptured ICAAs located at non-dorsal had more concentrated, unstable, and with smaller impingement zone (P<0.05), while those at dorsal had more complex, concentrated, and unstable flow patterns, and with smaller impingement zone (all P<0.05). Conclusion: Women, hypertension, dorsal concentration, and unstable flow pattern are independent risk factors for the rupture of ICAAs. The dorsal locations of ICAAs could have a higher risk of rupture.

Three-dimentional radiographic features of 67 maxillary radicular cysts

Objective To analyze the three-dimensional radiographic characteristics of maxillary radi-cular cysts using cone-beam computed tomography (CBCT) and spiral CT. Methods Clinical records, histopathological reports, and CBCT or non-enhanced spiral CT images of 67 consecutive patients with maxillary radicular cysts were retrospectively acquired, and radiographic features, including size, shape, expansion, internal structure and relationship with the surrounding tissues, were analyzed. The lesions were divided into three types according to the involved tooth number, as follows: type Ⅰ (single tooth), the epicenter of the cyst was located at the apex of a nonvital tooth, without involvement of the neighbo-ring tooth; type Ⅱ (adjacent tooth involvement), the cyst was located at the apex of a nonvital tooth with involvement of the mesial and/or distal tooth root; and type Ⅲ (multi-teeth), the cyst involved the apexes of ≥4 teeth. Besides, these cysts were classified as another three types on sagittal views, as follows: centripetal, the root apex was oriented centripetally to the center of the cyst; palatal, the cyst was located mainly at the palatal side of the apex; and labial/buccal, the cyst was located mainly at the labial/buccal side of the apex. Results Totally, 67 patients with maxillary radicular cysts were acquired, including 38 males and 29 females, and their ages ranged from 13 to 77 years. Among them, 46 lesions (68.7%) were located in the anterior maxilla and 65 (97.0%) were round or oval. Labial/buccal cortex expansion was present in 43 cases (64.2%) and palatal cortex expansion in 37 cases (55.2%). The nasal floor was invaded in 27 cases (40.3%), the maxillary sinus was invaginated in 26 cases (38.8%), and root resorption was present in 9 cases (13.4%). The average diameter of lesions was (20.89±8.11) mm mesio-distally and (16.70±5.88) mm bucco-palatally. In spite of the 4 residual cysts, the remaining 63 lesions included 14 type Ⅰ, 26 type Ⅱ and 23 type Ⅲ cysts according to the involved tooth number. Besides, the 63 lesions included 46 centripetal, 15 palatal and 2 buccal cysts on sagittal views. Conclusion The maxillary radicular cysts were frequently well-circumscribed round or oval radiolucency, with significantly different sizes. According to the involved tooth number, it can be divided into single tooth, adjacent tooth involvement and multi-teeth types. On sagittal views, the root-cyst relationship was centripetal in most cases, while a minority of cysts expanded palatally or buccally.