Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

DARU-Net: A dual attention residual U-Net for uterine fibroids segmentation on MRI.

PURPOSE: Uterine fibroid is the most common benign tumor in female reproductive organs. In order to guide the treatment, it is crucial to detect the location, shape, and size of the tumor. This study proposed a deep learning approach based on attention mechanisms to segment uterine fibroids automatically on preoperative Magnetic Resonance (MR) images. METHODS: The proposed method is based on U-Net architecture and integrates two attention mechanisms: channel attention of squeeze-and-excitation (SE) blocks with residual connections, spatial attention of pyramid pooling module (PPM). We did the ablation study to verify the performance of these two attention mechanisms module and compared DARU-Net with other deep learning methods. All experiments were performed on a clinical dataset consisting of 150 cases collected from our hospital. Among them, 120 cases were used as the training set, and 30 cases are used as the test set. After preprocessing and data augmentation, we trained the network and tested it on the test dataset. We evaluated segmentation performance through the Dice similarity coefficient (DSC), precision, recall, and Jaccard index (JI). RESULTS: The average DSC, precision, recall, and JI of DARU-Net reached 0.8066 ± 0.0956, 0.8233 ± 0.1255, 0.7913 ± 0.1304, and 0.6743 ± 0.1317. Compared with U-Net and other deep learning methods, DARU-Net was more accurate and stable. CONCLUSION: This work proposed an optimized U-Net with channel and spatial attention mechanisms to segment uterine fibroids on preoperative MR images. Results showed that DARU-Net was able to accurately segment uterine fibroids from MR images.

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.

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.

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma.

OBJECTIVE: This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). METHODS: 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. CONCLUSION: The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study.

PURPOSE: To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). METHODS: This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. RESULTS: SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798-0.952), 0.859 (95% CI 0.748-0.935), and 0.828 (95% CI 0.731-0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. CONCLUSION: A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Nonenhanced MRI-based radiomics model for preoperative prediction of nonperfused volume ratio for high-intensity focused ultrasound ablation of uterine leiomyomas.

OBJECTIVES: To develop and assess nonenhanced MRI-based radiomics model for the preoperative prediction of nonperfused volume (NPV) ratio of uterine leiomyomas after high-intensity focused ultrasound (HIFU) treatment. METHODS: Two hundred and five patients with uterine leiomyomas treated by HIFU were enrolled and allocated to training (N =164) and testing cohorts (N = 41). Pyradiomics was used to extract radiomics features from T2-weighted images and apparent diffusion coefficient (ADC) map generated from diffusion-weighted imaging (DWI). The clinico-radiological model, radiomics model, and radiomics-clinical model which combined the selected radiomics features and clinical parameters were used to predict technical outcomes determined by NPV ratios where three classification groups were created (NPV ratio ≤ 50%, 50-80% or ≥ 80%). The receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration and decision curve analyses were performed to illustrate the prediction performance and clinical usefulness of model in the training and testing cohorts. RESULTS: The multi-parametric MRI-based radiomics model outperformed T2-weighted imaging (T2WI)-based radiomics model, which achieved an average AUC of 0.769 (95% confidence interval [CI], 0.701-0.842), and showed satisfactory prediction performance for NPV ratio classification. The radiomics-clinical model demonstrated best prediction performance for HIFU treatment outcome, with an average AUC of 0.802 (95% CI, 0.796-0.850) and an accuracy of 0.762 (95% CI, 0.698-0.815) in the testing cohort, compared to the clinico-radiological and radiomics models. The decision curve also indicated favorable clinical usefulness of the radiomics-clinical model. CONCLUSIONS: Nonenhanced MRI-based radiomics has potential in the preoperative prediction of NPV ratio for HIFU ablation of uterine leiomyomas.

Prediction of Clinical Outcome for High-Intensity Focused Ultrasound Ablation of Uterine Leiomyomas Using Multiparametric MRI Radiomics-Based Machine Leaning Model.

OBJECTIVES: This study sought to develop a multiparametric MRI radiomics-based machine learning model for the preoperative prediction of clinical success for high-intensity-focused ultrasound (HIFU) ablation of uterine leiomyomas. METHODS: One hundred and thirty patients who received HIFU ablation therapy for uterine leiomyomas were enrolled in this retrospective study. Radiomics features were extracted from T2-weighted (T2WI) image and ADC map derived from diffusion-weighted imaging (DWI). Three feature selection algorithms including least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF algorithm were used to select radiomics features, respectively, which were fed into four machine learning classifiers including k-nearest neighbors (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) for the construction of outcome prediction models before HIFU treatment. The performance, predication ability, and clinical usefulness of these models were verified and evaluated using receiver operating characteristics (ROC), calibration, and decision curve analyses. RESULTS: The radiomics analysis provided an effective preoperative prediction for HIFU ablation of uterine leiomyomas. Using SVM with ReliefF algorithm, the multiparametric MRI radiomics model showed the favorable performance with average accuracy of 0.849, sensitivity of 0.814, specificity of 0.896, positive predictive value (PPV) of 0.903, negative predictive value (NPV) of 0.823, and the area under the ROC curve (AUC) of 0.887 (95% CI = 0.848-0.939) in fivefold cross-validation, followed by RF with ReliefF. Calibration and decision curve analyses confirmed the potential of model in predication ability and clinical usefulness. CONCLUSIONS: The radiomics-based machine learning model can predict preoperatively HIFU ablation response for the patients with uterine leiomyomas and contribute to determining individual treatment strategies.

A prospective cohort study on radiological and physiological outcomes of recovered COVID-19 patients 6 months after discharge.

BACKGROUND: This study investigated patients' long-term radiological and physiological outcomes with coronavirus disease 2019 (COVID-19). METHODS: A total of 52 patients (26 men and 26 women, 32 with moderate COVID-19 and 20 with severe COVID-19, with a median age of 50.5 years) who had COVID-19 participated in this study. Follow-up thin-section chest computed tomography (CT) scans were performed at 1, 3, and 6 months after discharge. Cardiopulmonary exercise testing was performed on 37 patients 6 months after discharge. The clinical data and the chest CT findings were recorded and analyzed. RESULTS: The predominant chest CT patterns of abnormalities observed at 6 months after discharge were parenchymal band, interlobular septal thickening, and traction bronchiectasis. The cumulative percentage of the complete radiological resolution was 17%, 42%, 67%, and 75% at discharge and 1, 3, and 6 months after discharge, respectively. A subgroup analysis revealed that 88% of patients with moderate type and 55% of patients with severe type COVID-19 achieved complete radiological resolution at 6 months after discharge, and the difference between the 2 groups was significant (P<0.001). The following risk factors were found to be associated with an incomplete radiological resolution at 6 months after discharge: an age >50 years old, the severe type of COVID-19, a hospital stay >18 days, mechanical ventilation, steroid therapy, immunoglobin therapy, an opacity score at discharge >4, and a volume of opacity at discharge >235 mL. CONCLUSIONS: Chest CT lesions were absorbed without any sequelae in most patients with COVID-19; however, fibrotic-like changes and cardiopulmonary insufficiency were still present in a considerable proportion of COVID-19 survivors at 6 months after discharge, especially in patients with severe type COVID-19.

Radiation dose levels in chest computed tomography scans of coronavirus disease 2019 pneumonia: A survey of 2119 patients in Chongqing, southwest China.

ABSTRACT: To investigate computed tomography (CT) diagnostic reference levels for coronavirus disease 2019 (COVID-19) pneumonia by collecting radiation exposure parameters of the most performed chest CT examinations and emphasize the necessity of low-dose CT in COVID-19 and its significance in radioprotection.The survey collected RIS data from 2119 chest CT examinations for 550 COVID-19 patients performed in 92 hospitals from January 23, 2020 to May 1, 2020. Dose data such as volume computed tomography dose index, dose-length product, and effective dose (ED) were recorded and analyzed. The radiation dose levels in different hospitals have been compared, and average ED and cumulative ED have been studied.The median dose-length product, volume computed tomography dose index, and ED measurements were 325.2 mGy cm with a range of 6.79 to 1098 mGy cm, 9.68 mGy with a range of 0.62 to 33.80 mGy, and 4.55 mSv with a range of 0.11 to 15.37 mSv for COVID-19 CT scanning protocols in Chongqing, China. The distribution of all observed EDs of radiation received by per patient undergoing CT protocols during hospitalization yielded a median cumulative ED of 17.34 mSv (range, 2.05-53.39 mSv) in the detection and management of COVID-19 patients. The average number of CT scan times for each patient was 4.0 ±â€Š2.0, and the average time interval between 2 CT scans was 7.0 ±â€Š5.0 days. The average cumulative ED of chest CT examinations for COVID-19 patients in Chongqing, China greatly exceeded public limit and the annual dose limit of occupational exposure in a short period.For patients with known or suspected COVID-19, a chest CT should be performed on the principle of rapid-scan, low-dose, single-phase protocol instead of routine chest CT protocol to minimize radiation doses and motion artifacts.

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.

Increased Internal Carotid Artery Tortuosity is a Risk Factor for Spontaneous Cervicocerebral Artery Dissection.

OBJECTIVE: Spontaneous cervicocerebral artery dissection (sCCD) is an important cause of ischaemic stroke that often occurs in young and middle aged patients. The purpose of this study was to investigate the correlation between tortuosity of the carotid artery and sCCD. METHODS: Patients with confirmed sCCD who underwent computed tomography angiography (CTA) were reviewed retrospectively. Age and sex matched patients having CTA were used as controls. The tortuosity indices of the cervical arteries were measured from the CTA images. The carotid siphon and the extracranial internal carotid artery (ICA) were evaluated according to morphological classification. The carotid siphons were classified into five types. The extracranial ICA was categorised as simple tortuosity, coiling or kinking. Independent risk factors for sCCD were investigated using multivariable analysis. RESULTS: The study included sixty-six patients with sCCD and 66 controls. There were no differences in vascular risk factors between the two groups. The internal carotid tortuosity index (ICTI) (25.24 ± 12.37 vs. 15.90 ± 8.55, respectively; p < .001) and vertebral tortuosity index (VTI) (median 11.28; interquartile range [IQR] 6.88, 18.80 vs. median 8.38; IQR 6.02, 12.20, respectively; p = .008) were higher in the patients with sCCD than in the controls. Type III and Type IV carotid siphons were more common in the patients with sCCD (p = .001 and p < .001, respectively). The prevalence of any vessel tortuosity, coiling and kinking of the extracranial ICA was higher in the patients with sCCD (p < .001, p = .018 and p = .006, respectively). ICTI (odds ratio [OR] 2.964; p = .026), VTI (OR 5.141; p = .009), and Type III carotid siphons (OR 4.654; p = .003) were independently associated with the risk of sCCD. CONCLUSION: Arterial tortuosity is associated with sCCD, and greater tortuosity of the cervical artery may indicate an increased risk of arterial dissection.

A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram.

Purpose: To explore the application value of multiparametric computed tomography (CT) radiomics in non-invasive differentiation between aldosterone-producing and cortisol-producing functional adrenocortical adenomas. Methods: This retrospective review analyzed 83 patients including 41 patients with aldosterone-producing adenoma and 42 patients with cortisol-producing adenoma. The quantitative radiomics features were extracted from the complete unenhanced, arterial, and venous phase CT images. A comparative study of several frequently used machine learning models (linear discriminant analysis, logistic regression, random forest, and support vector machine) combined with different feature selection methods was implemented in order to determine which was most advantageous for differential diagnosis using radiomics features. Then, the integrated model using the combination of radiomic signature and clinic-radiological features was built, and the associated calibration curve was also presented. The diagnostic performance of these models was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC). Result: In the radiomics-based machine learning model, logistic regression model with LASSO (least absolute shrinkage and selection operator) outperformed the other models, which yielded a sensitivity of 0.935, a specificity of 0.823, and an accuracy of 0.887 [AUC = 0.882, 95% confidence interval (CI) = 0.819-0.945]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.915, a specificity of 0.928, and an accuracy of 0.922 (AUC = 0.902, 95% CI = 0.822-0.982), and it was better than that of the radiomics model alone. Conclusion: This study found that the combination of multiparametric radiomics signature and clinic-radiological features can non-invasively differentiate the subtypes of hormone-secreting functional adrenocortical adenomas, which may have good potential for facilitating the diagnosis and treatment in clinical practice.

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.

Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study.

PURPOSE: To investigate the effective dose (E) and convolution kernel's effects on the detection of pulmonary nodules in different artificial intelligence (AI) software systems. METHODS: Simulated nodules of various sizes and densities in the Lungman phantom were CT scanned at different levels of E (3 - 5, 1 - 3, 0.5 - 1, and <0.5 mSv) and were reconstructed with different kernels (B30f, B60f, and B80f). The number of nodules and corresponding volumes in different images were detected by four AI software systems (A, B, C, and D). Sensitivity, false positives (FPs), false negatives (FNs), and relative volume error (RVE) were calculated and compared to the aspects of the E and convolution kernel. RESULTS: System B had the highest median sensitivity (100 %). The median FPs of systems B (1) and D (1) was lower than A (11.5) and C (5). System D had the smallest RVE (13.12 %). When the E was <0.5 mSv, system D's sensitivity decreased, while the FPs and FNs of systems A and B increased significantly (P < 0.05). When the kernel was changed from B80f to B30f, the FPs of system A decreased, while that of system C increased, and the RVE of systems A, B, and C increased (P < 0.05). CONCLUSION: AI software systems B and D have high detection efficiency under normal or low dose conditions and show better stability. However, the detection efficiency of systems A and C would be affected by the E or convolution kernel, but the E would not affect the volume measurement of four systems.

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.

Gated recurrent unit-based heart sound analysis for heart failure screening.

BACKGROUND: Heart failure (HF) is a type of cardiovascular disease caused by abnormal cardiac structure and function. Early screening of HF has important implication for treatment in a timely manner. Heart sound (HS) conveys relevant information related to HF; this study is therefore based on the analysis of HS signals. The objective is to develop an efficient tool to identify subjects of normal, HF with preserved ejection fraction and HF with reduced ejection fraction automatically. METHODS: We proposed a novel HF screening framework based on gated recurrent unit (GRU) model in this study. The logistic regression-based hidden semi-Markov model was adopted to segment HS frames. Normalized frames were taken as the input of the proposed model which can automatically learn the deep features and complete the HF screening without de-nosing and hand-crafted feature extraction. RESULTS: To evaluate the performance of proposed model, three methods are used for comparison. The results show that the GRU model gives a satisfactory performance with average accuracy of 98.82%, which is better than other comparison models. CONCLUSION: The proposed GRU model can learn features from HS directly, which means it can be independent of expert knowledge. In addition, the good performance demonstrates the effectiveness of HS analysis for HF early screening.

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.