Besides ultrasound and nuclear medicine techniques, computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used to examine adrenal lesions in both symptomatic and asymptomatic patients. Some adrenal lesions have characteristic radiological features. If an adrenal nodule is discovered incidentally, determining whether the lesion is benign or malignant is of great importance. According to their biological behavior, lesions can be divided into benign (mainly: adenoma, hyperplasia, pheochromocytoma, cyst, hemorrhage, cystic lymphangioma, myelolipoma, hemangioma, ganglioneuroma, teratoma) and malignant (mainly: metastases, adrenal cortical carcinoma, neuroblastoma, lymphoma) conditions. In this paper, we review CT/MRI findings of common adrenal gland lesions.

This study used diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) to assess glymphatic system function in autism spectrum disorder (ASD) compared to healthy controls. Patients with ASD may have glymphatic system dysfunction, which is related to age. We retrospectively included 30 children with ASD and 25 healthy controls in this study. 3T magnetic resonance imaging scanner was used to perform DTI magnetic resonance imaging on all participants, and the DTI-ALPS index was calculated from the DTI data. Additionally, we evaluated how the DTI-ALPS index differed between the 2 groups. Moreover, we examined the relationships between the bilateral DTI-ALPS index and the age of the participants. The DTI-ALPS index considerably differed between groups. In the left index (1.02 ± 0.12 vs. 1.27 ± 0.25, P < .001) and in the right index (1.03 ± 0.12 vs. 1.32 ± 0.20, P < .001), the DTI-ALPS in ASD patients was significantly lower than that in healthy controls. Furthermore, the DTI-ALPS index was strongly and positively associated with age. In patients with ASD, there is a glymphatic system dysfunction. This is intimately correlated to age. Our findings suggest the importance of the DTI-ALPS approach in assessing the function of the glymphatic system in ASD.

Background: Traditional scoring systems for patients’ outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases. Methods: Patients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO₂/FiO₂, and plateau pressure), Simplified Acute Physiology Score II (SAPS-II), Sepsis-related Organ Failure Assessment (SOFA), OSI, and OI were calculated. With MIMIC-III data, a mortality prediction model was built based on the random forest (RF) algorithm, and the performance was compared to those of existing scoring systems based on logistic regression. The performance of the proposed RF method was also validated with the combined MIMIC-III and eICU-CRD data. The performance of mortality prediction was evaluated by using the area under the receiver operating characteristics curve (AUROC) and performing calibration using the Hosmer-Lemeshow test. Results: With the MIMIC-III dataset (308 patients, for comparisons with the existing scoring systems), the RF model predicted the in-hospital mortality, 30-day mortality, and 1-year mortality with an AUROC of 0.891, 0.883, and 0.892, respectively, which were significantly higher than those of the SAPS-II, APPS, OSI, and OI (all P<0.001). In the multi-source validation (the combined dataset of 2,235 patients in MIMIC-III and 331 patients in eICU-CRD), the RF model achieved an AUROC of 0.905 and 0.736 for predicting in-hospital mortality for the MIMIC-III and eICU-CRD datasets, respectively. The calibration plots suggested good fits for our RF model and these scoring systems for predicting mortality. The platelet count and lactate level were the strongest predictive variables for predicting in-hospital mortality. Conclusions: Compared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.

Objectives: This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs. Methods: A retrospective continuous collection of HICH patients from three medical centers was divided into a training set (n = 555), a validation set (n = 239), and a test set (n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI. Conclusion: The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH. Clinical relevance statement: CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment. Key Points: HE is an important prognostic factor in patients with HICH. The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively. This model provides a tool for a personalized clinical assessment of early HE risk.

Background: The study aimed to explore cortical morphology in benign childhood epilepsy with centrotemporal spikes (BECTS) and the relationship between cortical characteristics and age of onset and intelligence quotient (IQ). Methods: Cortical morphometry with surface-based morphometry (SBM) was used to compare changes in cortical thickness, gyrification, sulcal depth, and fractal dimension of the cerebral cortex between 25 BECTS patients and 20 healthy controls (HCs) with two-sample t-tests [P<0.05, family-wise error (FWE) corrected]. Relationships between abnormal cortical morphological changes and age of onset and IQ, which included verbal intelligence quotient (VIQ), performance intelligence quotient (PIQ), and full-scale intelligence quotient (FIQ) were investigated with Spearman correlation analysis (P<0.05, uncorrected). Results: The BECTS patients showed extensive cortical thinning predominantly in bilateral frontal, temporal regions, and limbic system. Cortical gyrification increased in the left hemisphere and partial right hemisphere, and the decreased cortical gyrification was only in the left hemisphere. The increased sulcal depth was the left fusiform gyrus. There are no statistically significant differences in the fractal dimension. Correlation analysis revealed the negative correlation between age of onset and cortical thickness in the right precentral gyrus. It also revealed the negative correlation between the age of onset and cortical gyrification in the left inferior parietal gyrus. Also, there was negative correlation between VIQ and cortical gyrification in the left supramarginal gyrus of BECTS patients. Conclusions: This study reveals aberrant cortical thickness, cortical gyrification, and sulcal depth of BECTS in areas related to cognitive functions including language, attention and memory, and the correlation between some brain regions and VIQ and age of onset, providing a potential marker of early neurodevelopmental disturbance and cognitive dysfunction in BECTS.

Aims: Alterations in neuronal activity and cerebral hemodynamics have been reported in idiopathic generalized epilepsy (IGE) patients, possibly resulting in neurovascular decoupling; however, no neuroimaging evidence confirmed this disruption. This study aimed to investigate the possible presence of neurovascular decoupling and its clinical implications in childhood IGE using resting‐state fMRI and arterial spin labeling imaging.Methods: IGE patients and healthy participants underwent resting‐state fMRI and arterial spin labeling imaging to calculate degree centrality (DC) and cerebral blood flow (CBF), respectively. Across‐voxel CBF‐DC correlations were analyzed to evaluate the neurovascular coupling within the whole gray matter, and the regional coupling of brain region was assessed with the CBF/DC ratio.Results: The study included 26 children with IGE and 35 sex‐ and age‐matched healthy controls (HCs). Compared with the HCs, the IGE group presented lower across‐voxel CBF‐DC correlations, higher CBF/DC ratio in the right posterior cingulate cortex/precuneus, middle frontal gyrus, and medial frontal gyrus (MFG), and lower ratio in the left inferior frontal gyrus. The increased CBF/DC ratio in the right MFG was correlated with lower performance intelligence quotient scores in the IGE group.Conclusion: Children with IGE present altered neurovascular coupling, associated with lower performance intelligence quotient scores. The study shed a new insight into the pathophysiology of epilepsy and provided potential imaging biomarkers of cognitive performances in children with IGE.