The Effect of Tranexamic Acid on Neurosurgical Intervention in Spontaneous Intracerebral Hematoma: Data From 121 Surgically Treated Participants From the Tranexamic Acid in IntraCerebral Hemorrhage-2 Randomized Controlled Trial.

BACKGROUND AND OBJECTIVES: An important proportion of patients with spontaneous intracerebral hemorrhage (ICH) undergo neurosurgical intervention to reduce mass effect from large hematomas and control the complications of bleeding, including hematoma expansion and hydrocephalus. The Tranexamic acid (TXA) for hyperacute primary IntraCerebral Hemorrhage (TICH-2) trial demonstrated that tranexamic acid (TXA) reduces the risk of hematoma expansion. We hypothesized that TXA would reduce the frequency of surgery (primary outcome) and improve functional outcome at 90 days in surgically treated patients in the TICH-2 data set. METHODS: Participants enrolled in TICH-2 were randomized to placebo or TXA. Participants randomized to either TXA or placebo were analyzed for whether they received neurosurgery within 7 days and their characteristics, outcomes, hematoma volumes (HVs) were compared. Characteristics and outcomes of participants who received surgery were also compared with those who did not. RESULTS: Neurosurgery was performed in 5.2% of participants (121/2325), including craniotomy (57%), hematoma drainage (33%), and external ventricular drainage (21%). The number of patients receiving surgery who received TXA vs placebo were similar at 4.9% (57/1153) and 5.5% (64/1163), respectively (odds ratio [OR] 0.893; 95% CI 0.619-1.289; P-value = .545). TXA did not improve outcome compared with placebo in either surgically treated participants (OR 0.79; 95% CI 0.30-2.09; P = .64) or those undergoing hematoma evacuation by drainage or craniotomy (OR 1.19 95% 0.51-2.78; P-value = .69). Postoperative HV was not reduced by TXA (mean difference -8.97 95% CI -23.77, 5.82; P-value = .45). CONCLUSION: TXA was not associated with less neurosurgical intervention, reduced HV, or improved outcomes after surgery.

Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning.

This study evaluated deep learning algorithms for semantic segmentation and quantification of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular hemorrhage (IVH) on noncontrast CT scans of patients with spontaneous ICH. Models were assessed on 1732 annotated baseline noncontrast CT scans obtained from the Tranexamic Acid for Hyperacute Primary Intracerebral Haemorrhage (ie, TICH-2) international multicenter trial (ISRCTN93732214), and different loss functions using a three-dimensional no-new-U-Net (nnU-Net) were examined to address class imbalance (30% of participants with IVH in dataset). On the test cohort (n = 174, 10% of dataset), the top-performing models achieved median Dice similarity coefficients of 0.92 (IQR, 0.89-0.94), 0.66 (0.58-0.71), and 1.00 (0.87-1.00), respectively, for ICH, PHE, and IVH segmentation. U-Net-based networks showed comparable, satisfactory performances on ICH and PHE segmentations (P > .05), but all nnU-Net variants achieved higher accuracy than the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT) and DeepLabv3+ for all labels (P < .05). The Focal model showed improved performance in IVH segmentation compared with the Tversky, two-dimensional nnU-Net, U-Net, BLAST-CT, and DeepLabv3+ models (P < .05). Focal achieved concordance values of 0.98, 0.88, and 0.99 for ICH, PHE, and ICH volumes, respectively. The mean volumetric differences between the ground truth and prediction were 0.32 mL (95% CI: -8.35, 9.00), 1.14 mL (-9.53, 11.8), and 0.06 mL (-1.71, 1.84), respectively. In conclusion, U-Net-based networks provide accurate segmentation on CT images of spontaneous ICH, and Focal loss can address class imbalance. International Clinical Trials Registry Platform (ICTRP) no. ISRCTN93732214 Supplemental material is available for this article. © RSNA, 2022 Keywords: Head/Neck, Brain/Brain Stem, Hemorrhage, Segmentation, Quantification, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms.

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage.

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.

Noncontrast Computed Tomography Signs as Predictors of Hematoma Expansion, Clinical Outcome, and Response to Tranexamic Acid in Acute Intracerebral Hemorrhage.

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.