Background and Purpose— Blend, black hole, island signs, and hypodensities are reported to predict hematoma expansion in acute intracerebral hemorrhage. We explored the value of these noncontrast computed tomography signs in predicting hematoma expansion and functional outcome in our cohort of intracerebral hemorrhage. Methods— The TICH-2 (Tranexamic acid for IntraCerebral Hemorrhage-2) was a prospective randomized controlled trial exploring the efficacy and safety of tranexamic acid in acute intracerebral hemorrhage. Baseline and 24-hour computed tomography scans of trial participants were analyzed. Hematoma expansion was defined as an increase in hematoma volume of >33% or >6 mL on 24-hour computed tomography. Poor functional outcome was defined as modified Rankin Scale of 4 to 6 at day 90. Multivariable logistic regression was performed to identify predictors of hematoma expansion and poor functional outcome. Results— Of 2325 patients recruited, 2077 (89.3%) had valid baseline and 24-hour scans. Five hundred seventy patients (27.4%) had hematoma expansion while 1259 patients (54.6%) had poor functional outcome. The prevalence of noncontrast computed tomography signs was blend sign, 366 (16.1%); black hole sign, 414 (18.2%); island sign, 200 (8.8%); and hypodensities, 701 (30.2%). Blend sign (adjusted odds ratio [aOR] 1.53 [95% CI, 1.16–2.03]; P =0.003), black hole (aOR, 2.03 [1.34–3.08]; P =0.001), and hypodensities (aOR, 2.06 [1.48–2.89]; P <0.001) were independent predictors of hematoma expansion on multivariable analysis with adjustment for covariates. Black hole sign (aOR, 1.52 [1.10–2.11]; P =0.012), hypodensities (aOR, 1.37 [1.05–1.78]; P =0.019), and island sign (aOR, 2.59 [1.21–5.55]; P =0.014) were significant predictors of poor functional outcome. Tranexamic acid reduced the risk of hematoma expansion (aOR, 0.77 [0.63–0.94]; P =0.010), but there was no significant interaction between the presence of noncontrast computed tomography signs and benefit of tranexamic acid on hematoma expansion and functional outcome ( P interaction all >0.05). Conclusions— Blend sign, black hole sign, and hypodensities predict hematoma expansion while black hole sign, hypodensities, and island signs predict poor functional outcome. Noncontrast computed tomography signs did not predict a better response to tranexamic acid. Clinical Trial Registration— URL: https://www.isrctn.com . Unique identifier: ISRCTN93732214.

Objectives To test radiomics-based features extracted from noncontrast CT of patients with spontaneous intracerebral haemorrhage for prediction of haematoma expansion and poor functional outcome and compare them with radiological signs and clinical factors. Materials and methods Seven hundred fifty-four radiomics-based features were extracted from 1732 scans derived from the TICH-2 multicentre clinical trial. Features were harmonised and a correlation-based feature selection was applied. Different elastic-net parameterisations were tested to assess the predictive performance of the selected radiomics-based features using grid optimisation. For comparison, the same procedure was run using radiological signs and clinical factors separately. Models trained with radiomics-based features combined with radiological signs or clinical factors were tested. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) score. Results The optimal radiomics-based model showed an AUC of 0.693 for haematoma expansion and an AUC of 0.783 for poor functional outcome. Models with radiological signs alone yielded substantial reductions in sensitivity. Combining radiomics-based features and radiological signs did not provide any improvement over radiomics-based features alone. Models with clinical factors had similar performance compared to using radiomics-based features, albeit with low sensitivity for haematoma expansion. Performance of radiomics-based features was boosted by incorporating clinical factors, with time from onset to scan and age being the most important contributors for haematoma expansion and poor functional outcome prediction, respectively. Conclusion Radiomics-based features perform better than radiological signs and similarly to clinical factors on the prediction of haematoma expansion and poor functional outcome. Moreover, combining radiomics-based features with clinical factors improves their performance. Key Points • Linear models based on CT radiomics-based features perform better than radiological signs on the prediction of haematoma expansion and poor functional outcome in the context of intracerebral haemorrhage. • Linear models based on CT radiomics-based features perform similarly to clinical factors known to be good predictors. However, combining these clinical factors with radiomics-based features increases their predictive performance.

HighlightsLinear decline in cerebellar volume in people with classical A-T across childhood.Divergent volume trajectories in children with and without A-T in the first decade.Alterations in metabolites seen in childhood A-T independent of age and volume.Fractional fourth ventricular volume predicts neurological status in childhood A-T.

Background Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH. Methods We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation. Results Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way. Conclusion Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.