Comparison of different noncontrast computed tomographic markers for predicting early perihematomal edema expansion in patients with intracerebral hemorrhage.

OBJECTIVES: Noncontrast computed tomography (NCCT) imaging markers are associated with early perihematomal edema (PHE) growth. The aim of this study was to compare the predictive value of different NCCT markers in predicting early PHE expansion. METHODS: ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan within 36 h between July 2011 and March 2017 were included in this study. The predictive value of hypodensity, satellite sign, heterogeneous density, irregular shape, blend sign, black hole sign, island sign and expansion-prone hematoma for early perihematomal edema expansion were assessed, separately. RESULTS: 214 patients were included in our final analysis. After adjusting for ICH characteristics, hypodensity, blend sign, island sign and expansion-prone hematoma are still predictors of early perihematomal edema expansion in multivariable logistics regression analysis (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of expansion-prone hematoma was significantly larger than the area under the ROC curve of hypodensity, blend sign and island sign in predicting PHE expansion (P = 0.003, P < 0.001 and P = 0.002, respectively). CONCLUSION: Compared with single NCCT imaging markers, expansion-prone hematoma seems to be optimal predictor for early PHE expansion than any single NCCT imaging marker.

Prediction of Early Perihematomal Edema Expansion Based on Noncontrast Computed Tomography Radiomics and Machine Learning in Intracerebral Hemorrhage.

OBJECTIVES: To investigate the predictive value of noncontrast computed tomography (NCCT) models based on radiomics features and machine learning for early perihematomal edema (PHE) expansion in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: We retrospectively reviewed NCCT data from 214 patients with spontaneous ICH. All radiomics features were extracted from volume of interest of hematomas on admission scans. A total of 8 machine learning methods were applied for constructing models in the training and the test set. Receiver operating characteristic analysis and the areas under the curve were used to evaluate the predictive value. RESULTS: A total of 23 features were finally selected to establish models of early PHE expansion after feature screening. Patients were randomly assigned into training (n = 171) and test (n = 43) sets. The accuracy, sensitivity, and specificity in the test set were 72.1%, 90.0%, and 66.7% for the support vector machine model; 79.1%, 70.0%, and 84.4% for the k-nearest neighbor model; 88.4%, 90.0%, and 87.9% for the logistic regression model; 74.4%, 90.0%, and 69.7% for the extra tree model; 74.4%, 90.0%, and 69.7% for the extreme gradient boosting model; 83.7%, 100%, and 78.8% for the multilayer perceptron (MLP) model; 72.1%, 100%, and 65.6% for the light gradient boosting machine model; and 60.5%, 90.0%, and 53.1% for the random forest model, respectively. CONCLUSIONS: The MLP model seemed to be the best model for prediction of PHE expansion in patients with ICH. NCCT models based on radiomics features and machine learning could predict early PHE expansion and improve the discrimination of identify spontaneous intracerebral hemorrhage patients at risk of early PHE expansion.

A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules.

BACKGROUND: Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PURPOSE: This study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). METHODS: The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. CONCLUSIONS: Radiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm.

Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature's reproducibility. Pearson correlation coefficients for normal distribution features and Spearman's rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

3D island sign on computed tomography predicts early perihematomal edema expansion and poor outcome in patients with intracerebral hemorrhage.

BACKGROUND AND PURPOSE: To determine the prognostics significance of the computed tomography (CT) 3D island sign for predicting early perihematomal edema (PHE) expansion and poor functional outcome in patients presenting with intracerebral hemorrhage (ICH). METHODS: Between July 2011 and March 2017, patients with intracerebral hemorrhage who had undergone baseline CT within 6 h after ICH symptom onsets and follow-up CT in our hospital were included. Two different readers independently assessed the presence of 3D island sign on admission CT scan of each patient. Multivariable logistic regression analysis was used to analyze association between 3D island sign and early perihematomal edema expansion and poor functional outcome, separately. RESULTS: A total of 214 patients who met the inclusion criteria were included in our study, 3D island sign was observed in 60 patients (28.0 %) on admission CT scan. The multivariate logistic regression analysis demonstrated that baseline hematoma volume, time to baseline and follow-up CT scans and the presence of 3D island sign were predictors of early PHE expansion. After adjusting for age, baseline hematoma and edema volume, time to baseline and follow-up CT scans, GCS on admission, presence of intraventricular hemorrhage (IVH) and systolic blood pressure, the 3D island sign was an independently imaging marker for poor outcome (OR, 2.803; 95 % confidence interval, 1.189-6.609; P = 0.018). CONCLUSION: The 3D island sign in patients with intracerebral hemorrhage was a reliable predictor for early perihematomal edema expansion and poor functional outcome. It may serve as a potential therapeutic target for intervention.

Comparison of Different Machine Models Based on Multi-Phase Computed Tomography Radiomic Analysis to Differentiate Parotid Basal Cell Adenoma From Pleomorphic Adenoma.

Objective: This study explored the value of different radiomic models based on multiphase computed tomography in differentiating parotid pleomorphic adenoma (PA) and basal cell tumor (BCA) concerning the predominant phase and the optimal radiomic model. Methods: This study enrolled 173 patients with pathologically confirmed parotid tumors (training cohort: n=121; testing cohort: n=52). Radiomic features were extracted from the nonenhanced, arterial, venous, and delayed phases CT images. After dimensionality reduction and screening, logistic regression (LR), K-nearest neighbor (KNN) and support vector machine (SVM) were applied to develop radiomic models. The optimal radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was performed to analyze clinical-radiological characteristics and to identify variables for developing a clinical model. A combined model was constructed by integrating clinical and radiomic features. Model performances were assessed by ROC curve analysis. Results: A total of 1036 radiomic features were extracted from each phase of CT images. Sixteen radiomic features were considered valuable by dimensionality reduction and screening. Among radiomic models, the SVM model of the arterial and delayed phases showed superior predictive efficiency and robustness (AUC, training cohort: 0.822, 0.838; testing cohort: 0.752, 0.751). The discriminatory capability of the combined model was the best (AUC, training cohort: 0.885; testing cohort: 0.834). Conclusions: The diagnostic performance of the arterial and delayed phases contributed more than other phases. However, the combined model demonstrated excellent ability to distinguish BCA from PA, which may provide a non-invasive and efficient method for clinical decision-making.

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis.

The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules.

BACKGROUND: The presence of pulmonary vessels inside ground-glass nodules (GGNs) of different nature is a very common occurrence. This study aimed to reveal the significance of pulmonary vessels displayed in GGNs in their diagnosis and differential diagnosis. RESULTS: A total of 149 malignant and 130 benign GGNs confirmed by postoperative pathological examination were retrospectively enrolled in this study. There were significant differences in size, shape, nodule-lung interface, pleural traction, lobulation, and spiculation (each p < 0.05) between benign and malignant GGNs. Compared with benign GGNs, intra-nodular vessels were more common in malignant GGNs (67.79% vs. 54.62%, p = 0.024), while the vascular categories were similar (p = 0.663). After adjusting the nodule size and the distance between the nodule center and adjacent pleura [radius-distance ratio, RDR], the occurrences of internal vessels between them were similar. The number of intra-nodular vessels was positively correlated with nodular diameter and RDR. Vascular changes were more common in malignant than benign GGNs (52.48% vs. 18.31%, p < 0.0001), which mainly manifested as distortion and/or dilation of pulmonary veins (61.19%). The occurrence rate, number, and changes of internal vessels had no significant differences among all the pre-invasive and invasive lesions (each p > 0.05). CONCLUSIONS: The incidence of internal vessels in GGNs is mainly related to their size and the distance between nodule and pleura rather than the pathological nature. However, GGNs with dilated or distorted internal vessels, especially pulmonary veins, have a higher possibility of malignancy.

Intraventricular Hemorrhage Growth: Definition, Prevalence and Association with Hematoma Expansion and Prognosis.

BACKGROUND/OBJECTIVES: The objective of this study is to propose a definition of intraventricular hemorrhage (IVH) growth and to investigate whether IVH growth is associated with ICH expansion and functional outcome. METHODS: We performed a prospective observational study of ICH patients between July 2011 and March 2017 in a tertiary hospital. Patients were included if they had a baseline CT scan within 6 h after onset of symptoms and a follow-up CT within 36 h. IVH growth was defined as either any newly occurring intraventricular bleeding on follow-up CT scan in patients without baseline IVH or an increase in IVH volume ≥ 1 mL on follow-up CT scan in patients with initial IVH. Poor outcome was defined as modified Rankin Scale score of 3-6 at 90 days. The association between IVH growth and functional outcome was assessed by using multivariable logistic regression analysis. RESULTS: IVH growth was observed in 59 (19.5%) of 303 patients. Patients with IVH growth had larger baseline hematoma volume, higher NIHSS score and lower GCS score than those without. Of 44 patients who had concurrent IVH growth and hematoma growth, 41 (93.2%) had poor functional outcome at 3-month follow-up. IVH growth (adjusted OR 4.15, 95% CI 1.31-13.20; P = 0.016) was an independent predictor of poor functional outcome (mRS 3-6) at 3 months in multivariable analysis. CONCLUSION: IVH growth is not uncommon and independently predicts poor outcome in ICH patients. It may serve as a promising therapeutic target for intervention.

MRI evaluation of endopelvic fascial swelling and analysis of influencing factors in patients with uterine fibroids after high-intensity focused ultrasound ablation.

Purpose: To evaluate endopelvic fascial swelling in patients with uterine fibroids after high-intensity focused ultrasound (HIFU) ablation on magnetic resonance imaging (MRI) and investigate the factors that influence endopelvic fascial swelling.Methods: MRI and clinical data from 188 patients with uterine fibroids who were treated with HIFU were analyzed retrospectively. The patients were divided into a fascial swelling group and a non-swelling group, and the degree of swelling was graded. Fascial swelling was set as the dependent variable, and factors such as baseline characteristics and HIFU parameters, were set as the independent variables. The relationship between these variables and fascial swelling was analyzed by univariate and multivariate analyses. Correlations between the factors and the degree of fascial swelling were evaluated by Kruskal-Wallis test.Results: The univariate analysis revealed that the fibroid location, distance from the fibroid to the sacrum, sonication time, treatment time, treatment intensity, therapeutic dose (TD), and energy efficiency (EEF) all affected the endopelvic fascial swelling (p < 0.05). Subsequently, multivariate analysis showed that the distance from the fibroid to the sacrum was significantly correlated with fascial swelling (p < 0.05). Moreover, TD and sonication time were significantly positively correlated with the degree of fascial swelling (p < 0.05). The incidence of sacrococcygeal pain was significantly correlated with fascial swelling (p < 0.05).Conclusion: The distance from the fibroid to the sacrum was a protective factor for fascial swelling. TD and sonication time were significantly positively correlated with the degree of fascial swelling.

Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations.

BACKGROUND: The computed tomography (CT) features of small solid lung cancers and their changing regularity as they grow have not been well studied. The purpose of this study was to analyze the CT features of solid lung cancerous nodules (SLCNs) with different sizes and their variations. METHODS: Between February 2013 and April 2018, a consecutive cohort of 224 patients (225 nodules) with confirmed primary SLCNs was enrolled. The nodules were divided into four groups based on tumor diameter (A: diameter ≤ 1.0 cm, 35 lesions; B: 1.0 cm < diameter ≤ 1.5 cm, 60 lesions; C: 1.5 cm < diameter ≤ 2.0 cm, 63 lesions; and D: 2.0 cm < diameter ≤ 3.0 cm, 67 lesions). CT features of nodules within each group were summarized and compared. RESULTS: Most nodules in different groups were located in upper lobes (groups A - D:50.8%-73.1%) and had a gap from the pleura (groups A - D:89.6%-100%). The main CT features of smaller (diameter ≤ 1 cm) and larger (diameter > 1 cm) nodules were significantly different. As nodule diameter increased, more lesions showed a regular shape, homogeneous density, clear but coarse tumor-lung interface, lobulation, spiculation, spinous protuberance, vascular convergence, pleural retraction, bronchial truncation, and beam-shaped opacity (p < 0.05 for all). The presence of halo sign in all groups was similar (17.5%-22.5%; p > 0.05). CONCLUSIONS: The CT features vary among SLCNs with different sizes. Understanding their changing regularity is helpful for identifying smaller suspicious malignant nodules and early determining their nature in follow-up.

Effect of Slab Thickness on the Detection of Pulmonary Nodules by Use of CT Maximum and Minimum Intensity Projection.

OBJECTIVE. The purpose of this study was to investigate the effect of slab thickness on the detection of pulmonary nodules by use of maximum-intensity-projection (MIP) and minimum-intensity-projection (MinIP) to process CT images. MATERIALS AND METHODS. Chest CT data of 221 patients with pulmonary nodules were retrospectively analyzed. Nodules were categorized into two groups according to density: solid nodules (SNs) and subsolid nodules (SSNs). Pulmonary nodules were independently evaluated by two radiologists using axial CT images with 1-mm and 5-mm section thickness and MIP and MinIP images. MIP images for SN detection and MinIP images for SSN detection were separately reconstructed with four (5, 10, 15, 20 mm) and three (3, 8, 15 mm) slab thicknesses. The numbers and locations of detected nodules were recorded, and interobserver agreement was assessed. For each reader, the differences in nodule detection rates were evaluated in different series of images. RESULTS. Among the different series of images, interobserver agreements for detecting nodules were all good to excellent (κ ≥ 0.687). For total SNs and SNs with a diameter < 5 mm, detection rates on 10-mm MIP images were significantly higher than in other series of images (reader 1, 84.5% and 83.8%; reader 2, 83.6% and 82.2%). For total SSNs and SSNs < 5 mm, detection rates on 3-mm MinIP images were significantly higher than those in other series of images, except for 1-mm (reader 1, 93.3% and 78.6%; reader 2, 95.0% and 81.0%). CONCLUSION. Ten-millimeter MIP images are extremely efficient for detecting SNs. Three-millimeter MinIP images are more useful for visualizing SSNs, the efficiency being comparable to that achieved by use of 1-mm axial images.

[Changes of some biochemical markers and cardiac function in New Zealand rabbits with chronic heart failure].

OBJECTIVE: This article investigated the changes of some biochemical markers and cardiac function in chronic heart failure (CHF), and provided the basis for the diagnosis of CHF. METHODS: New Zealand rabbit CHF model was established using adriamycin (ADR). Twenty New Zealand rabbits were randomly divided into model group (n=15) and control group (n=5), injected with ADR and saline solution via the ear vein respectively, 2 times a week, lasting for 8 weeks. After that, myocardial enzymes, carotid artery pressure, echocardiogram (ECG) and phonocardiogram (PCG) of all New Zealand rabbits were detected and recorded. RESULTS: Compared with control group, all parameters of the model group were changed significantly (P<0.05). CONCLUSIONS: CHF leads to myocardial damage in New Zealand rabbits, decreased systolic and diastolic function, cardiac reserve index can be used to assess cardiac function.