Intracranial hemorrhage associated with primary or metastatic brain tumors is a critical condition that requires urgent intervention, often through open surgery. Nevertheless, surgical interventions may not always be feasible due to two main reasons: (1) extensive hemorrhage can obscure the underlying tumor mass, limiting radiological assessment; and (2) intracranial hemorrhage may occasionally present as the first symptom of a brain tumor without prior knowledge of its existence. The current review of case studies suggests that advanced radiological imaging techniques can improve diagnostic power for tumoral hemorrhage. Adding proton magnetic resonance spectroscopy (1H-MRS), which profiles biochemical composition of mass lesions could be valuable: it provides unique information about tumor states distinct from hemorrhagic lesions bypassing the structural obliteration caused by the hemorrhage. Recent advances in 1H-MRS techniques may enhance the modality's reliability in clinical practice. This perspective proposes that 1H-MRS can be utilized in clinical settings to enhance diagnostic power in identifying tumors underlying intracranial hemorrhage.

This observational study aims to provide a detailed clinical and imaging characterization/workup of acute intracerebral hemorrhage (ICH) due to either an underlying metastasis (mICH) or brain tumor (tICH) lesion.

We conducted a retrospective, single-center study, evaluating patients presenting with occult ICH on initial CT imaging, classified as tICH or mICH on follow-up MRI imaging according to the H-Atomic classification. Demographic, clinical and radiological data were reviewed.

We included 116 patients (tICH: 20/116, 17.24%; mICH: 96/116, 82.76%). The most common malignancies causing ICH were lung cancer (27.59%), malignant melanoma (18.10%) and glioblastoma (10.34%). The three most common stroke-like symptoms observed were focal deficit (62/116, 53.45%), dizziness (42/116, 36.21%) and cognitive impairment (27/116, 23.28%). Highest mICH prevalence was seen in the occipital lobe (mICH: 28.13%, tICH: 0.00%; p = 0.004) with tICH more in the corpus callosum (tICH: 10.00%, mICH: 0.00%; p = 0.029). Anticoagulation therapy was only frequent in mICH patients (tICH: 0.00%, mICH: 5.21%; p = 0.586). Hemorrhage (tICH: 12682 mm3, mICH: 5708 mm3, p = 0.020) and edema volumes (tICH: 49389 mm3, mICH: 20972 mm3, p = 0.035) were significantly larger within tICH patients.

More than half of the patients with neoplastic ICH exhibited stroke-like symptoms. Lung cancer was most common in mICH, glioblastoma in tICH. While clinical presentations were similar, significant differences in tumor location and treatments were discernible.

Brain metastases (BM) are the most common intracranial malignancies. They are responsible for death as well as impairment of quality of life and cognitive function. In some cases, BMs can cause intracranial hemorrhage, which is not only responsible for the acute onset of either a new focal neurological deficit or worsening of a preexisting focal deficit but also poses a new challenge in treatment planning and clinical management. The aim of this study was to evaluate the available treatment modalities and their efficacy in hemorrhagic brain metastases (HBMs) with special attention to radiotherapy. In this review, we searched PubMed, BMJ, NCBI, Springer, BMC Cancer, Cochrane, and Google Scholar for articles containing data on the diagnosis and treatment of patients with HBMs, excluding the pediatric population. Treatment strategies consist of neurosurgery, whole brain radiotherapy, and stereotactic techniques (fractionated stereotactic radiosurgery (fSRS)/stereotactic radiosurgery (SRS)). Although the optimal treatment strategy for HBMs has not been established, we found no convincing evidence that radiotherapy, especially fSRS/SRS, is contraindicated in HBMs. We concluded that fSRS/SRS is a promising option for patients with HBM, particularly when surgical intervention poses risks.

Background and Purpose: Fully automated methods for segmentation and volume quantification of intraparenchymal hemorrhage (ICH), intraventricular hemorrhage extension (IVH), and perihematomal edema (PHE) are gaining increasing interest. Yet, reliabilities demonstrate considerable variances amongst each other. Our aim was therefore to evaluate both the intra- and interrater reliability of ICH, IVH and PHE on ground-truth segmentation masks. Methods: Patients with primary spontaneous ICH were retrospectively included from a German tertiary stroke center (Charité Berlin; January 2016−June 2020). Baseline and follow-up non-contrast Computed Tomography (NCCT) scans were analyzed for ICH, IVH, and PHE volume quantification by two radiology residents. Raters were blinded to all demographic and outcome data. Inter- and intrarater agreements were determined by calculating the Intraclass Correlation Coefficient (ICC) for a randomly selected set of patients with ICH, IVH, and PHE. Results: 100 out of 670 patients were included in the analysis. Interrater agreements ranged from an ICC of 0.998 for ICH (95% CI [0.993; 0.997]), to an ICC of 0.979 for IVH (95% CI [0.984; 0.993]), and an ICC of 0.886 for PHE (95% CI [0.760; 0.938]), all p-values < 0.001. Intrarater agreements ranged from an ICC of 0.997 for ICH (95% CI [0.996; 0.998]), to an ICC of 0.995 for IVH (95% CI [0.992; 0.996]), and an ICC of 0.980 for PHE (95% CI [0.971; 0.987]), all p-values < 0.001. Conclusion Manual segmentations of ICH, IVH, and PHE demonstrate good-to-excellent inter- and intrarater reliabilities, with the highest agreement for ICH and IVH and lowest for PHE. Therefore, the degree of variances reported in fully automated quantification methods might be related amongst others to variances in ground-truth masks.

Background: Early differentiation of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) can be difficult in initial radiological evaluation, especially for extensive ICHs. The aim of this study was to evaluate the potential of a machine learning-based prediction of etiology for acute ICHs based on quantitative radiomic image features extracted from initial non-contrast-enhanced computed tomography (NECT) brain scans. Methods: The analysis included NECT brain scans from 77 patients with acute ICH (n = 50 non-neoplastic, n = 27 neoplastic). Radiomic features including shape, histogram, and texture markers were extracted from non-, wavelet-, and log-sigma-filtered images using regions of interest of ICH and perihematomal edema (PHE). Six thousand and ninety quantitative predictors were evaluated utilizing random forest algorithms with five-fold model-external cross-validation. Model stability was assessed through comparative analysis of 10 randomly drawn cross-validation sets. Classifier performance was compared with predictions of two radiologists employing the Matthews correlation coefficient (MCC). Results: The receiver operating characteristic (ROC) area under the curve (AUC) of the test sets for predicting neoplastic vs. non-neoplastic ICHs was 0.89 [95% CI (0.70; 0.99); P < 0.001], and specificities and sensitivities reached >80%. Compared to the radiologists' predictions, the machine learning algorithm yielded equal or superior results for all evaluated metrics. The MCC of the proposed algorithm at its optimal operating point (0.69) was significantly higher than the MCC of the radiologist readers (0.54); P = 0.01. Conclusion: Evaluating quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting non-neoplastic vs. neoplastic ICHs. Utilized in the clinical routine, the proposed approach could improve patient care at low risk and costs.

Background and Purpose- Patients with active malignancy are at risk for intracerebral hemorrhage (ICH). We aimed to characterize perihematomal edema (PHE) and hematoma volumes after spontaneous nontraumatic ICH in patients with cancer without central nervous system involvement. Methods- Patients with active malignancy who developed ICH were retrospectively identified through automated searches of institutional databases. Control patients were identified with ICH and without active cancer. Demographic and cancer-specific data were obtained by chart review. Hematoma and PHE volumes were determined using semiautomated methodology. Univariate and multivariate linear regression models were created to assess which variables were associated with hematoma and PHE expansion. Results- Patients with cancer (N=80) and controls (N=136) had similar demographics (all P>0.20), although hypertension was more prevalent among controls (P=0.004). Most patients with cancer had received recent chemotherapy (n=45, 56%) and had recurrence of malignancy (n=43, 54%). Patients with cancer were thrombocytopenic (median platelet count 90 000 [interquartile range, 17 500-211 500]), and most had undergone blood product transfusion (n=41, 51%), predominantly platelets (n=38, 48%). Thirty-day mortality was 36% (n=29). Patients with cancer had significantly increased PHE volumes (23.67 versus 8.61 mL; P=1.88×10^-9) and PHE-to-ICH volume ratios (2.26 versus 0.99; P=2.20×10^-16). In multivariate analyses, variables associated with PHE growth among patients with cancer were ICH volume (β=1.29 [95% CI, 1.58-1.30] P=1.30×10^-5) and platelet transfusion (β=15.67 [95% CI, 3.61-27.74] P=0.014). Variables associated with 30-day mortality were ICH volume (odds ratio, 1.06 [95% CI, 1.03-1.10] P=6.76×10^-5), PHE volume (odds ratio, 1.07 [95% CI, 1.04-1.09] P=7.40×10^-6), PHE growth (odds ratio, 1.05 [95% CI, 1.01-1.10] P=0.01), and platelet transfusion (odds ratio, 1.48 [95% CI, 1.22-1.79] P=0.0001). Conclusions- Patients with active cancer who develop ICH have increased PHE volumes. PHE growth was independent of thrombocytopenia but associated with blood product transfusion. Thirty-day mortality was associated with PHE and ICH volumes and blood product transfusion.