In patients with acute stroke with an unknown time of onset, intravenous alteplase guided by a mismatch between diffusion-weighted imaging and FLAIR in the region of ischemia resulted in a significantly better functional outcome and numerically more intracranial hemorrhages than placebo at 90 days. (Funded by the European Union Seventh Framework Program; WAKE-UP ClinicalTrials.gov number, NCT01525290; and EudraCT number, 2011-005906-32 .).

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.