Clinical-radiomics predictors to identify the suitability of transarterial chemoembolization treatment in intermediate-stage hepatocellular carcinoma: A multicenter study.

BACKGROUND: Although transarterial chemoembolization (TACE) is the first-line therapy for intermediate-stage hepatocellular carcinoma (HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. METHODS: A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting (XGBoost) with 5-fold cross-validation. The Shapley additive explanations (SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model's performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. RESULTS: A third of the patients (81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 0.759, 0.885, 0.906 [95% confidence interval (CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894 (95% CI: 0.815-0.972) in the testing cohort, respectively. CONCLUSIONS: The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.

The development of brain functional connectivity networks revealed by resting-state functional magnetic resonance imaging.

Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep conditions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the "three-brain region theory" of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circuit and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, nodes and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB-131115-H0075 on November 15, 2013.

Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans.

PURPOSE: The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle. A computer-aided diagnosis system from CT images is thus developed to augment image interpretation. METHODS: Supervised learning using the probability of distance features of several landmarks was employed to recognize SAS. For each CT image, the SAS was approximated in four steps: (1) Landmarks including brain boundary, midsagittal plane (MSP), anterior and posterior intersection points of brain boundary with the MSP, and superior point of the brain were extracted. (2) Distances to all the landmarks were calculated for every pixel in the CT image, and combined to construct a high-dimensional feature vector. (3) Using head CT images with manually delineated SAS as training dataset, the prior probabilities of distances for pixels within SAS and non-SAS were computed. (4) Any pixel of a head CT scan in the testing dataset was classified as an SAS or non-SAS pixel in a Bayesian decision framework based on its distance features. RESULTS: The proposed method was validated on clinical head CT images by comparison with manual segmentation. The results showed that the automated method is consistent with the gold standard. Compared with elastic registration based on grayscale information, the proposed method was less affected by grayscale variation between normal controls and patients. Compared with manual delineation, the average spatial overlap, relative overlap, and similarity index were, respectively, 89, 63, and 76% for the automatic SAS approximation of the 69 head CT scans tested. The proposed method was tested for SAH detection and yielded a sensitivity of 100% and a specificity of 92%. CONCLUSION: Automated SAH detection with high sensitivity was shown feasible in a prototype computer-aided diagnosis system. The proposed method may be extended for computer-aided diagnosis of several CSF-related diseases relevant to SAS abnormalities.

[Current research status of image-guided neurosurgery].

Image-guided neurosurgery is playing a more and more important role in modern neurosurgical therapy. The current trend is to construct an open platform for image-guided neurosurgery, to make applied researches of various new therapeutic techniques, and finally to integrate them into the clinical image-guided neurosurgery system.