Background and Purpose- Early selection of patients with acute middle cerebral artery infarction at risk for malignant edema is critical to initiate timely decompressive surgery. Net water uptake (NWU) per brain volume is a quantitative imaging biomarker of space-occupying ischemic edema which can be measured in computed tomography. We hypothesize that NWU in early infarct lesions can predict development of malignant edema. The aim was to compare NWU in acute brain infarct against other common predictors of malignant edema. Methods- After consecutive screening of single-center registry data, 153 patients with acute proximal middle cerebral artery occlusion fulfilled the inclusion criteria. A total of 29 (18.2%) patients developed malignant edema defined as end point in follow-up imaging leading to decompressive surgery and death as a direct implication of mass effect. Early infarct lesion volume and NWU were quantified in multimodal admission computed tomography; time from symptom onset to admission imaging was recorded. Results- Mean time from onset to admission imaging was equivalent between patients with and without malignant infarcts (mean±SD: 3.3±1.4 hours and 3.3±1.7 hours, respectively). Edematous tissue expansion by NWU within infarct lesions occurred across all patients in this cohort (NWU: 9.1%±6.8%; median, 7.9%; interquartile range, 8.8%; range, 0.1%-35.6%); 7.0% (±5.2) in nonmalignant and 18.0% (±5.7) in malignant infarcts. Based on univariate receiver operating characteristic curve analysis, NWU >12.7% or an edema rate >3.7% NWU/h identified malignant infarcts with high discriminative power (area under curve, 0.93±0.02). In multivariate binary logistic regression, the probability of malignant infarct was significantly associated with early infarct volume and NWU. Conclusions- Computed tomography-based quantitative NWU in early infarct lesions is an important surrogate marker for developing malignant edema. Besides volume of early infarct, the measurements of lesion water uptake may further support identifying patients at risk for malignant infarction.

To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods: This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results: Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion: Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases.

Aims Long-term sequelae may occur after SARS-CoV-2 infection. We comprehensively assessed organ-specific functions in individuals after mild to moderate SARS-CoV-2 infection compared with controls from the general population. Methods and results Four hundred and forty-three mainly non-hospitalized individuals were examined in median 9.6 months after the first positive SARS-CoV-2 test and matched for age, sex, and education with 1328 controls from a population-based German cohort. We assessed pulmonary, cardiac, vascular, renal, and neurological status, as well as patient-related outcomes. Bodyplethysmography documented mildly lower total lung volume (regression coefficient −3.24, adjusted P = 0.014) and higher specific airway resistance (regression coefficient 8.11, adjusted P = 0.001) after SARS-CoV-2 infection. Cardiac assessment revealed slightly lower measures of left (regression coefficient for left ventricular ejection fraction on transthoracic echocardiography −0.93, adjusted P = 0.015) and right ventricular function and higher concentrations of cardiac biomarkers (factor 1.14 for high-sensitivity troponin, 1.41 for N-terminal pro-B-type natriuretic peptide, adjusted P ≤ 0.01) in post-SARS-CoV-2 patients compared with matched controls, but no significant differences in cardiac magnetic resonance imaging findings. Sonographically non-compressible femoral veins, suggesting deep vein thrombosis, were substantially more frequent after SARS-CoV-2 infection (odds ratio 2.68, adjusted P < 0.001). Glomerular filtration rate (regression coefficient −2.35, adjusted P = 0.019) was lower in post-SARS-CoV-2 cases. Relative brain volume, prevalence of cerebral microbleeds, and infarct residuals were similar, while the mean cortical thickness was higher in post-SARS-CoV-2 cases. Cognitive function was not impaired. Similarly, patient-related outcomes did not differ. Conclusion Subjects who apparently recovered from mild to moderate SARS-CoV-2 infection show signs of subclinical multi-organ affection related to pulmonary, cardiac, thrombotic, and renal function without signs of structural brain damage, neurocognitive, or quality-of-life impairment. Respective screening may guide further patient management.