Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH‐2 trial intracerebral hemorrhage population

Haider, Stefan P.; Qureshi, Adnan I; Jain, Abhi; Tharmaseelan, Hishan; Berson, Elisa R; Zeevi, Tal; Majidi, Shahram; Filippi, Christopher G.; Iseke, Simon; Gross, Moritz; Acosta, Julián; Malhotra, Ajay; Kim, Jennifer A; Sansing, Lauren H; Falcone, Guido J; Sheth, Kevin N; Payabvash, Seyedmehdi

doi:10.1111/ene.15000

Cited by 19 publications

(10 citation statements)

References 39 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the application of radiomics can extract more objective features that are not retrievable based on vision and manual measurements. It has consistently been shown to be effective for the prediction of hematoma expansion and poor neurologic outcome in spontaneous IPH patients [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. As for traumatic IPH, we herein demonstrated its usefulness to further expand the limited evidence currently available [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning models generated from selected radiomics features have been shown to correlate well with clinically important diagnostic or prognostic outcomes [ 20 ]. Focusing on spontaneous intracerebral hemorrhages, extensive studies have been performed, which demonstrated the good predictive capability of models derived from radiomics features for hematoma expansion [ 21 , 22 , 23 , 24 , 25 ] and poor neurological outcome [ 24 , 26 , 27 , 28 , 29 , 30 ]. Furthermore, increased c-statistics were observed after the radiomics features were combined with clinical and radiological parameters constantly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

et al. 2022

View full text Add to dashboard Cite

(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Subsequently, radiomics software (Pyradiomics version 2.1.2) 13,14 was used to automatically extract n=14 shape features from ICH masks. [7][8][9][10][11] To mitigate the impact of CT data heterogeneity on the analysis, we adopted voxel dimension resampling of ICH masks and CT images to an isotropic voxel spacing of 1x1x1 mm using B-spline interpolation prior to shape feature quantification. [7][8][9][10][11][13][14][15] Figure 2 visualizes the segmentation workflow and ICH shape features.…”

Section: Hematoma Shape Feature Quantificationmentioning

confidence: 99%

“…[7][8][9][10][11] To mitigate the impact of CT data heterogeneity on the analysis, we adopted voxel dimension resampling of ICH masks and CT images to an isotropic voxel spacing of 1x1x1 mm using B-spline interpolation prior to shape feature quantification. [7][8][9][10][11][13][14][15] Figure 2 visualizes the segmentation workflow and ICH shape features. A list of all shape features is provided in Table 1.…”

Section: Hematoma Shape Feature Quantificationmentioning

confidence: 99%

See 1 more Smart Citation

The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume

Haider

Qureshi

Jain

et al. 2021

International Journal of Stroke

Self Cite

View full text Add to dashboard Cite

Background: Among prognostic imaging variables, the hematoma volume on admission CT has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage (ICH). Aims: To examine whether different features of hematoma shape are associated with functional outcome in deep ICH. Methods: We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearmanâs Rho to assess the correlation between shape features and 3-month modified Rankin scale (mRS) score, and the ROC-AUC to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS>3. Results: Among 14 shape features, the maximum ICH diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >~3.5 cm indicating higher 3-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 vs 0.35 (p=0.006), an AUC[mRS>2] of 0.71 vs 0.68 (p=0.004), and an AUC[mRS>3] of 0.71 vs 0.69 (p=0.029). In multiple regression analysis adjusted for known outcome predictors, the maximum coronal diameter was independently associated with 3-month mRS (p<0.001). Conclusions: A coronal-plane maximum diameter measurement offers greater prognostic value in deep ICH than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials and may substitute volume in prognostic ICH scoring systems.

show abstract

Association of Imaging-based Predictors with Outcome in Different Treatment Options for Intracerebral Hemorrhage

Krzyżewski,

Kwinta,

Stachura

et al. 2024

Clin Neuroradiol

View full text Add to dashboard Cite

Purpose Intracerebral hemorrhage is the deadliest form of stroke. This study aimed to enhance the prediction of 30-day mortality in intracerebral hemorrhage patients by integrating computational parameters. Methods This study retrospectively analyzed 435 patients with spontaneous intracerebral hemorrhage (ICH). Utilizing the acquired computed tomography (CT) images, we extracted the contour and visual representation of ICH. For the extracted contour, the analysis encompassed factors including compactness, fractal dimension, Fourier factor, and circle factor. For the images depicting ICH, we calculated various factors related to density distribution including mean, coefficient of variance, skewness and kurtosis, as well as texture parameters, such as energy, entropy, contrast and homogeneity. To assess the impact of surgical treatment on 30-day mortality, logistic regression analysis was used. Results A total of 126 patients (29.09%) died within 30 days. A total of 62 (14.25%) patients underwent surgical treatment. Multivariate logistic regression analysis revealed that surgical treatment was independently associated with a lower risk of 30-day mortality (odds ratio, OR 0.226, 95% confidence interval, CI 0.049–0.85; p = 0.039). Based on the moderated analysis, we found that the volume of ICH (OR 0.905, 95% CI 0.902–0.908; p < 0.001) and ICH energy (OR 1.389, 95%CI 0.884–0.988; p = 0.010) had positive moderating effect on such associations while the presence of intraventricular blood had negative moderating effect (OR 1.154, 95% CI 1.034–1.628; p = 0.010). Conclusion Patients exhibiting a higher volume and energy of ICH might benefit from surgical treatment; however, this efficacy was found to be diminished in cases involving the presence of intraventricular blood.

show abstract

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH‐2 trial intracerebral hemorrhage population

Cited by 19 publications

References 39 publications

Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume

Association of Imaging-based Predictors with Outcome in Different Treatment Options for Intracerebral Hemorrhage

Contact Info

Product

Resources

About