Relationship Between Non-contrast Computed Tomography Imaging Markers and Perihemorrhagic Edema Growth in Intracerebral Hemorrhage

A comprehensive prediction model predicts perihematomal edema growth in the acute stage after intracerebral hemorrhage

BACKGROUND

Perihematomal edema (PHE) is regarded as a potential intervention indicator of secondary injury following intracerebral hemorrhage (ICH). But it still lacks a comprehensive prediction model for early PHE formation.

METHODS

The included ICH patients have received an initial Computed Tomography scan within 6 hours of symptom onset. Hematoma volume and PHE volume were computed using semiautomated computer-assisted software. The volume of the hematoma, edema around the hematoma, and surface area of the hematoma were calculated. The platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte cell count. All analyses were 2-tailed, and the significance level was determined by P <0.05.

RESULTS

A total of 226 patients were included in the final analysis. The optimal cut-off values for PHE volume increase to predict poor outcomes were determined as 5.5 mL. For clinical applicability, we identified a value of 5.5 mL as the optimal threshold for early PHE growth. In the multivariate logistic regression analyses, we finally found that baseline hematoma surface area (p < 0.001), expansion-prone hematoma (p < 0.001), and PLR (p = 0.033) could independently predict PHE growth. The comprehensive prediction model demonstrated good performance in predicting PHE growth, with an area under the curve of 0.841, sensitivity of 0.807, and specificity of 0.732.

CONCLUSION

In this study, we found that baseline hematoma surface area, expansion-prone hematoma, and PLR were independently associated with PHE growth. Additionally, a risk nomogram model was established to predict the PHE growth in patients with ICH.

Prediction of Early Perihematomal Edema Expansion Based on Noncontrast Computed Tomography Radiomics and Machine Learning in Intracerebral Hemorrhage

OBJECTIVES

To investigate the predictive value of noncontrast computed tomography (NCCT) models based on radiomics features and machine learning for early perihematomal edema (PHE) expansion in patients with spontaneous intracerebral hemorrhage (ICH).

METHODS

We retrospectively reviewed NCCT data from 214 patients with spontaneous ICH. All radiomics features were extracted from volume of interest of hematomas on admission scans. A total of 8 machine learning methods were applied for constructing models in the training and the test set. Receiver operating characteristic analysis and the areas under the curve were used to evaluate the predictive value.

RESULTS

A total of 23 features were finally selected to establish models of early PHE expansion after feature screening. Patients were randomly assigned into training (n = 171) and test (n = 43) sets. The accuracy, sensitivity, and specificity in the test set were 72.1%, 90.0%, and 66.7% for the support vector machine model; 79.1%, 70.0%, and 84.4% for the k-nearest neighbor model; 88.4%, 90.0%, and 87.9% for the logistic regression model; 74.4%, 90.0%, and 69.7% for the extra tree model; 74.4%, 90.0%, and 69.7% for the extreme gradient boosting model; 83.7%, 100%, and 78.8% for the multilayer perceptron (MLP) model; 72.1%, 100%, and 65.6% for the light gradient boosting machine model; and 60.5%, 90.0%, and 53.1% for the random forest model, respectively.

CONCLUSIONS

The MLP model seemed to be the best model for prediction of PHE expansion in patients with ICH. NCCT models based on radiomics features and machine learning could predict early PHE expansion and improve the discrimination of identify spontaneous intracerebral hemorrhage patients at risk of early PHE expansion.

Comparison of different noncontrast computed tomographic markers for predicting early perihematomal edema expansion in patients with intracerebral hemorrhage

OBJECTIVES

Noncontrast computed tomography (NCCT) imaging markers are associated with early perihematomal edema (PHE) growth. The aim of this study was to compare the predictive value of different NCCT markers in predicting early PHE expansion.

METHODS

ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan within 36 h between July 2011 and March 2017 were included in this study. The predictive value of hypodensity, satellite sign, heterogeneous density, irregular shape, blend sign, black hole sign, island sign and expansion-prone hematoma for early perihematomal edema expansion were assessed, separately.

RESULTS

214 patients were included in our final analysis. After adjusting for ICH characteristics, hypodensity, blend sign, island sign and expansion-prone hematoma are still predictors of early perihematomal edema expansion in multivariable logistics regression analysis (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of expansion-prone hematoma was significantly larger than the area under the ROC curve of hypodensity, blend sign and island sign in predicting PHE expansion (P = 0.003, P < 0.001 and P = 0.002, respectively).

CONCLUSION

Compared with single NCCT imaging markers, expansion-prone hematoma seems to be optimal predictor for early PHE expansion than any single NCCT imaging marker.

A machine learning approach for predicting perihematomal edema expansion in patients with intracerebral hemorrhage

OBJECTIVES

Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a machine learning approach.

METHODS

We enrolled 550 patients with spontaneous ICH to study early PHE expansion, and 389 patients to study delayed expansion. Two imaging researchers rated the shape and density of hematoma in non-contrast computed tomography (NCCT). We trained a radiological machine learning (ML) model, a radiomics ML model, and a combined ML model, using data from radiomics, traditional imaging, and clinical indicators. We then validated these models on an independent dataset by using a nested 4-fold cross-validation approach. We compared models with respect to their predictive performance, which was assessed using the receiver operating characteristic curve.

RESULTS

For both early and delayed PHE expansion, the combined ML model was most predictive (early/delayed AUC values were 0.840/0.705), followed by the radiomics ML model (0.799/0.663), the radiological ML model (0.779/0.631), and the imaging readers (reader 1: 0.668/0.565, reader 2: 0.700/0.617).

CONCLUSION

We validated a machine learning approach with high interpretability for the prediction of early and delayed PHE expansion. This new technique may assist clinical practice for the management of neurocritical patients with ICH.

KEY POINTS

• This is the first study to use artificial intelligence technology for the prediction of perihematomal edema expansion. • A combined machine learning model, trained on data from radiomics, clinical indicators, and imaging features associated with hematoma expansion, outperformed all other methods.

Perihematomal Edema After Intracerebral Hemorrhage: An Update on Pathogenesis, Risk Factors, and Therapeutic Advances

Intracerebral hemorrhage (ICH) has one of the worst prognoses among patients with stroke. Surgical measures have been adopted to relieve the mass effect of the hematoma, and developing targeted therapy against secondary brain injury (SBI) after ICH is equally essential. Numerous preclinical and clinical studies have demonstrated that perihematomal edema (PHE) is a quantifiable marker of SBI after ICH and is associated with a poor prognosis. Thus, PHE has been considered a promising therapeutic target for ICH. However, the findings derived from existing studies on PHE are disparate and unclear. Therefore, it is necessary to classify, compare, and summarize the existing studies on PHE. In this review, we describe the growth characteristics and relevant underlying mechanism of PHE, analyze the contributions of different risk factors to PHE, present the potential impact of PHE on patient outcomes, and discuss the currently available therapeutic strategies.