The purpose of this study was to evaluate gray matter (GM) and white matter (WM) volume alterations in whole-brain structures in patients with schizophrenia and healthy controls using voxel-based ...morphometry (VBM), and further to assess the correlation between GM and WM volume variations and symptom severity in schizophrenia. A total of 22 patients with schizophrenia and 22 age-matched healthy controls participated. Magnetic resonance image data were processed using SPM8 software with diffeomorphic anatomical registration via an exponentiated Lie algebra (DARTEL) algorithm. Patients with schizophrenia exhibited significantly decreased GM volumes of the insula, superior temporal gyrus (STG), gyrus rectus, and anterior cingulate cortex (ACC) compared with healthy controls. The GM volumes of the STG and gyrus rectus were negatively correlated with the positive scales on the Positive and Negative Syndrome Scale (PANSS) and those of the STG and ACC were negatively correlated with the negative scales. The durations of illness in schizophrenia were negatively correlated with the GM volumes of the insula, STG, and ACC. Patients with schizophrenia exhibited significantly decreased WM volumes of the superior frontal gyrus, inferior temporal gyrus, and STG. The WM volumes of the STG were negatively correlated with the duration of illness. Our findings suggest that GM and WM volume abnormalities in the STG are associated with the psychopathology of schizophrenia.
Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate ...discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability.
The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers.
We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods.
Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910).
RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.
To assess the association between fibrotic interstitial lung abnormalities (ILAs) and long-term survival in patients with resected Stage IA non-small cell lung cancer (NSCLC).
Data of patients who ...underwent curative resection of pathological Stage IA NSCLC between 2010 and 2015 were retrospectively analysed. ILAs were evaluated using pre-operative high-resolution CT scans. The association between ILAs and cause-specific mortality was assessed via Kaplan-Meier analysis and the log-rank test. Cox proportional hazards regression was performed to determine the risk factors for cause-specific death.
Overall, 228 patients were identified (63.27 ± 8.54 years, 133 men 58.3%). ILAs were detected in 24 patients (10.53%). Fibrotic ILAs were observed in 16 patients (7.02%), and there was a significantly higher cause-specific mortality rate among patients with fibrotic ILAs compared with patients with no ILAs (
< 0.001). Patients with fibrotic ILAs had a significantly higher cause-specific mortality rate than patients without ILAs at 5 post-operative years (survival rate: 61.88%
93.03%,
< 0.001). The presence of afibrotic ILA was an independent risk factor for cause-specific death (adjusted hazard ratio = 3.22; 95% confidence interval: 1.10, 9.44;
= 0.033).
The presence of afibrotic ILA was a risk factor for cause-specific death in patients with resected Stage IA NSCLC. Radiologists and clinicians should be familiar with the relatively new concept of ILAs and understand the close association between ILA status and long-term survival in resected Stage IA NSCLC. Patients presenting fibrotic ILAs should receive appropriate surveillance and management to optimise prognosis.
Fibrotic ILAs are important findings implicated inthe long-term survival of patients with resected Stage IA NSCLC. Specific management is required for this group.
This study aimed to evaluate the computed tomography (CT) features of solitary pulmonary nodule (SPN), which can be a non-invasive diagnostic tool to differentiate between primary lung cancer (LC) ...and solitary lung metastasis (LM) in patients with colorectal cancer (CRC).
This retrospective study included SPNs resected in CRC patients between January 2011 and December 2019. The diagnosis of primary LC or solitary LM was based on histopathologic report by thoracoscopic wedge resection. Chest CT images were assessed by two thoracic radiologists, and CT features were identified by consensus. Predictive parameters for the discrimination of primary LC from solitary LM were evaluated using multivariate logistic regression analysis.
We analyzed CT data of 199 patients (mean age, 65.95 years; 131 men and 68 women). The clinical characteristic of SPNs suggestive of primary LC rather than solitary LM was clinical stages I-II CRC (P < 0.001, odds ratio OR 21.70). The CT features of SPNs indicative of primary LC rather than solitary LM were spiculated margin (quantitative) (P = 0.020, OR 8.34), sub-solid density (quantitative) (P < 0.001, OR 115.56), and presence of an air bronchogram (quantitative) (P = 0.032, OR 5.32).
Quantitative CT features and clinical characteristics of SPNs in patients with CRC could help differentiate between primary LC and solitary LM.
An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.
We aimed to develop and validate a prediction model using CXR based ...on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.
This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration.
The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve AUC 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859).
The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.
This study aimed to investigate real-time early detection of metabolic alteration in a rat model with acute myocardial ischemia-reperfusion (AMI/R) injury and myocardial necrosis, as well as its ...correlation with intracellular pH level using in vivo hyperpolarized 1-
C pyruvate magnetic resonance spectroscopy (MRS). Hyperpolarized
C MRS was performed on the myocardium of 8 sham-operated control rats and 8 rats with AMI/R injury, and 8 sham-operated control rats and 8 rats with AMI-induced necrosis. Also, the correlations of levels of 1-
C metabolites with pH were analyzed by Spearman's correlation test. The AMI/R and necrosis groups showed significantly higher ratios of 1-
C lactate (Lac)/bicarbonate (Bicar) and 1-
C Lac/total carbon (tC), and lower ratios of
C Bicar/Lac + alanine (Ala), and
C Bicar/tC than those of the sham-operated control group. Moreover, the necrosis group showed significantly higher ratios of 1-
C Lac/Bicar and 1-
C Lac/tC, and lower ratios of
C Bicar/Lac + Ala and
C Bicar/tC than those of the AMI/R group. These results were consistent with the pattern for in vivo the area under the curve (AUC) ratios. In addition, levels of 1-
C Lac/Bicar and 1-
C Lac/tC were negatively correlated with pH levels, whereas
C Bicar/Lac + Ala and
C Bicar/tC levels were positively correlated with pH levels. The levels of 1-
C Lac and
C Bicar will be helpful for non-invasively evaluating the early stage of AMI/R and necrosis in conjunction with reperfusion injury of the heart. These findings have potential application to real-time evaluation of cardiac malfunction accompanied by changes in intracellular pH level and enzymatic activity.
Magnetic resonance imaging (MRI) has become a crucial tool for evaluating mediastinal masses considering that several lesions that appear indeterminate on computed tomography and radiography can be ...differentiated on MRI. Using a three-compartment model to localize the mass and employing a basic knowledge of MRI, radiologists can easily diagnose mediastinal masses. Here, we review the use of MRI in evaluating mediastinal masses and present the images of various mediastinal masses categorized using the International Thymic Malignancy Interest Group's three-compartment classification system. These masses include thymic hyperplasia, thymic cyst, pericardial cyst, thymoma, mediastinal hemangioma, lymphoma, mature teratoma, bronchogenic cyst, esophageal duplication cyst, mediastinal thyroid carcinoma originating from ectopic thyroid tissue, mediastinal liposarcoma, mediastinal pancreatic pseudocyst, neurogenic tumor, meningocele, and plasmacytoma.
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