Identifying Minimal Hepatic Encephalopathy: A New Perspective from Magnetic Resonance Imaging.

Type C hepatic encephalopathy (HE) is a condition characterized by brain dysfunction caused by liver insufficiency and/or portal-systemic blood shunting, which manifests as a broad spectrum of neurological or psychiatric abnormalities, ranging from minimal HE (MHE), detectable only by neuropsychological or neurophysiological assessment, to coma. Though MHE is the subclinical phase of HE, it is highly prevalent in cirrhotic patients and strongly associated with poor quality of life, high risk of overt HE, and mortality. It is, therefore, critical to identify MHE at the earliest and timely intervene, thereby minimizing the subsequent complications and costs. However, proper and sensitive diagnosis of MHE is hampered by its unnoticeable symptoms and the absence of standard diagnostic criteria. A variety of neuropsychological or neurophysiological tests have been performed to diagnose MHE. However, these tests are nonspecific and susceptible to multiple factors (eg, aging, education), thereby limiting their application in clinical practice. Thus, developing an objective, effective, and noninvasive method is imperative to help detect MHE. Magnetic resonance imaging (MRI), a noninvasive technique which can produce many objective biomarkers by different imaging sequences (eg, Magnetic resonance spectroscopy, DWI, rs-MRI, and arterial spin labeling), has recently shown the ability to screen MHE from NHE (non-HE) patients accurately. As advanced MRI techniques continue to emerge, more minor changes in the brain could be captured, providing new means for early diagnosis and quantitative assessment of MHE. In addition, the advancement of artificial intelligence in medical imaging also presents the potential to mine more effective diagnostic biomarkers and further improves the predictive efficiency of MHE. Taken together, advanced MRI techniques may provide a new perspective for us to identify MHE in the future. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma.

OBJECTIVE: To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. MATERIALS AND METHODS: A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC). RESULTS: 399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively). CONCLUSIONS: Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.

Development and validation of a radiomics-based nomogram for predicting a major pathological response to neoadjuvant immunochemotherapy for patients with potentially resectable non-small cell lung cancer.

Introduction: The treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics. Methods: A total of 89 eligible participants were included and randomly divided into training (N=64) and validation (N=25) sets. Radiomic features were extracted from tumor volumes of interest in pretreatment CT images. Following data dimension reduction, feature selection, and radiomic signature building, a radiomics-clinical combined nomogram was developed using logistic regression analysis. Results: The radiomics-clinical combined model achieved excellent discriminative performance, with AUCs of 0.84 (95% CI, 0.74-0.93) and 0.81(95% CI, 0.63-0.98) and accuracies of 80% and 80% in the training and validation sets, respectively. Decision curves analysis (DCA) indicated that the radiomics-clinical combined nomogram was clinically valuable. Discussion: The constructed nomogram was able to predict MPR to neoadjuvant immunochemotherapy with a high degree of accuracy and robustness, suggesting that it is a convenient tool for assisting with the individualized management of patients with potentially resectable NSCLC.

Genes Associated with Altered Brain Structure and Function in Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) has been widely reported to cause abnormalities in brain structure and function, but the genetic mechanisms behind these changes remain largely unexplored. Our research aims to investigate the relationship between sleep characteristics, cognitive impairments, genetic factors, and brain structure and function in OSA. Using structural and resting-state functional magnetic resonance imaging data, we compared cortical morphology and spontaneous brain activity between 28 patients with moderate-to-severe OSA and 34 healthy controls (HCs) utilizing voxel-based morphology (VBM) and the amplitude of low-frequency fluctuations (ALFF) analyses. In conjunction with the Allen Human Brain Atlas, we used transcriptome-neuroimaging spatial correlation analyses to investigate gene expression patterns associated with changes in gray matter volume (GMV) and ALFF in OSA. Compared to the HCs, the OSA group exhibited increased ALFF values in the left hippocampus (t = 5.294), amygdala (t = 4.176), caudate (t = 4.659), cerebellum (t = 5.896), and decreased ALFF values in the left precuneus (t = -4.776). VBM analysis revealed increased GMV in the right inferior parietal lobe (t = 5.158) in OSA. Additionally, functional enrichment analysis revealed that genes associated with both ALFF and GMV cross-sampling were enriched in gated channel activity and synaptic transmission, glutamatergic synapse, and neuron.

Image-guided percutaneous ablation for lung malignancies.

Image-guided percutaneous lung ablation has proven to be an alternative and effective strategy in the treatment of lung cancer and other lung malignancies. Radiofrequency ablation, microwave ablation, and cryoablation are widely used ablation modalities in clinical practice that can be performed along or combined with other treatment modalities. In this context, this article will review the application of different ablation strategies in lung malignancies.