Background: Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for... Show moreBackground: Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline. Methods: We included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process. Results: From 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) <= 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) >= 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach. Conclusions: The combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values. Show less
Parmentier, C.E.J.; Vries, L.S. de; Groenendaal, F. 2022
Hypoxic-ischemic encephalopathy (HIE) is a major cause of neurological sequelae in (near-)term newborns. Despite the use of therapeutic hypothermia, a significant number of newborns still... Show moreHypoxic-ischemic encephalopathy (HIE) is a major cause of neurological sequelae in (near-)term newborns. Despite the use of therapeutic hypothermia, a significant number of newborns still experience impaired neurodevelopment. Neuroimaging is the standard of care in infants with HIE to determine the timing and nature of the injury, guide further treatment decisions, and predict neurodevelopmental outcomes. Cranial ultrasonography is a helpful noninvasive tool to assess the brain before initiation of hypothermia to look for abnormalities suggestive of HIE mimics or antenatal onset of injury. Magnetic resonance imaging (MRI) which includes diffusion-weighted imaging has, however, become the gold standard to assess brain injury in infants with HIE, and has an excellent prognostic utility. Magnetic resonance spectroscopy provides complementary metabolic information and has also been shown to be a reliable prognostic biomarker. Advanced imaging modalities, including diffusion tensor imaging and arterial spin labeling, are increasingly being used to gain further information about the etiology and prognosis of brain injury. Over the past decades, tremendous progress has been made in the field of neonatal neuroimaging. In this review, the main brain injury patterns of infants with HIE, the application of conventional and advanced MRI techniques in these newborns, and HIE mimics, will be described. Show less
OBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on... Show moreOBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on subjective experience and generalized num-bers from the literature, but even experienced surgeons overestimate functional outcome after surgery. Today, there is no reliable and objective way to preoperatively predict an individual patient's risk of experiencing any functional impair-ment. METHODS The authors developed a prediction model for functional impairment at 3 to 6 months after microsurgical resection, defined as a decrease in Karnofsky Performance Status of >= 10 points. Two prospective registries in Swit- zerland and Italy were used for development. External validation was performed in 7 cohorts from Sweden, Norway, Germany, Austria, and the Netherlands. Age, sex, prior surgery, tumor histology and maximum diameter, expected major brain vessel or cranial nerve manipulation, resection in eloquent areas and the posterior fossa, and surgical approach were recorded. Discrimination and calibration metrics were evaluated. RESULTS In the development (2437 patients, 48.2% male; mean age +/- SD: 55 +/- 15 years) and external validation (2427 patients, 42.4% male; mean age +/- SD: 58 +/- 13 years) cohorts, functional impairment rates were 21.5% and 28.5%, respectively. In the development cohort, area under the curve (AUC) values of 0.72 (95% CI 0.69-0.74) were observed. In the pooled external validation cohort, the AUC was 0.72 (95% CI 0.69-0.74), confirming generalizability. Calibration plots indicated fair calibration in both cohorts. The tool has been incorporated into a web-based application available at https://neurosurgery.shinyapps.io/impairment/. CONCLUSIONS Functional impairment after intracranial tumor surgery remains extraordinarily difficult to predict, al- though machine learning can help quantify risk. This externally validated prediction tool can serve as the basis for case by-case discussions and risk-to-benefit estimation of surgical treatment in the individual patient. Show less
