RESUMO
BACKGROUND: There is yet no randomized controlled evidence that mechanical thrombectomy (MT) is superior to best medical treatment in patients with large vessel occlusion but minor stroke symptoms (National Institutes of Health Stroke Scale [NIHSS] <6). Prior studies of patients with admission NIHSS scores >6 observed unfavorable functional outcomes despite successful recanalization, commonly termed as futile recanalization (FR), in up to 50% of cases. AIM: The aim of this study is to determine the prevalence of FR in patients with minor stroke and identify associated patient-specific risk factors. METHODS: Our multicenter cohort study screened all patients prospectively enrolled in the German Stroke Registry Endovascular Treatment from 2015 to 2021 (n=13082). Included were patients who underwent MT for anterior circulation vessel occlusion with a baseline NIHSS score of <6 and successful recanalization (modified Thrombolysis in Cerebral Infarction [mTICI] score of 2b-3). FR was defined by a modified Rankin Scale (mRS) score of 2-6 at 90 days. Multivariable logistic regression analysis was conducted to explore factors associated with FR. RESULTS: A total of 674 patients met the inclusion criteria. FR occurred in 268 (40%) patients. Multivariable logistic regression analysis indicates that higher age (adjusted odds ratio: 1.04 [95% confidence interval: 1.02-1.06]), pre-stroke mRS 1 (aOR: 2.70 [1.51-4.84]), transfer from admission hospital to comprehensive stroke center (aOR: 1.67 [1.08-2.56]), longer time from symptom onset/last seen well to admission (aOR: 1.02 [1.00-1.04]), MT under general anesthesia (aOR: 1.78 [1.13-2.82]), higher NIHSS after 24 hours (aOR: 1.09 [1.05-1.14]), and symptomatic intracranial hemorrhage (aOR: 16.88 [2.03-140.14]) increased the odds of FR. There was no significant difference in primary outcome between achieving mTICI 2b or 3. CONCLUSIONS: Unfavorable functional outcomes despite successful vessel recanalization were frequent in acute ischemic stroke patients with low NIHSS scores on admission. We provide patient-specific risk factors that indicate an increased risk of FR and should be considered when treating patients with minor stroke. DATA ACCESS STATEMENT: The data that support the findings of our study are available on reasonable request after approval of the GSR steering committee.
RESUMO
Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer.
