Clinical-radiomics Nomogram for Risk Estimation of Early Hematoma Expansion after Acute Intracerebral Hemorrhage

Chen, Qian; Zhu, Dongqin; Liu, Jinjin; Zhang, Mingyue; Xu, Haoli; Xiang, Yingying; Zhan, Chenyi; Zhang, Yong; Huang, Shuigen; Yang, Yunjun

doi:10.1016/j.acra.2020.02.021

Cited by 47 publications

(43 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we devised and validated hematoma radiomics signa- Radiomics analysis has been widely applied in oncologic imaging for molecular subtyping, survival prognostication, and prediction of treatment response [8,[21][22][23]. Recent studies suggested that hematoma radiomics features can predict the likelihood of hematoma expansion [24][25][26][27]. In this study, we showed that while hematoma volume and radiomic shape features had strong association with severity of baseline clinical presentation and b R. R. Wilcox percentile bootstrap method for comparing dependent robust correlations [28].…”

Section: Discussionmentioning

confidence: 99%

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

Qureshi

Jain

et al. 2021

Euro J of Neurology

View full text Add to dashboard Cite

Background and purpose: Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT). Methods:We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearmanʼs correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Qureshi

Jain

et al. 2021

Euro J of Neurology

View full text Add to dashboard Cite

show abstract

“…The more sophisticated support vector machine(SVM) algorithm has been applied as well (15). The accuracy, sensitivity, and speci city ranged from 0.64 to 0.88; 0.75 to 0.89 and 0.60 to 0.87, respectively, which covered a wide range, and were highly dependent on the dataset (14)(15)(16)(17)19).…”

Section: Discussionmentioning

confidence: 99%

“…Several radiological predictors on the baseline non-contrast CT (NCCT) for HE had been proposed, such as hematoma volume, shape, hypodensities, density heterogeneity…etc(8-13). The pattern of heterogeneity can be analyzed using the texture features extracted by the radiomics approach, which has been shown capable of capturing various agnostic features to aid-in HE prediction (14)(15)(16)(17)(18)(19). The radiomics features could be further combined with clinical (19) and radiological variables (16,17) to improve HE prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Intraventricular Hemorrhage on Classification of Hematoma Expansion and Development of Radiomics Prediction Models

Liu

Chen

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: To investigate the impact of intraventricular hemorrhage (IVH) on the classification of hematoma expansion (HE), and the development of radiomics models using features extracted from the baseline hematoma to predict HE.Methods: Eighty-four patients with baseline and follow-up non-contrast CT within 4~24 hours were included. The intraparenchymal hemorrhage (IPH) and IVH were separately outlined by an experienced neuroradiologist. HE was defined as an absolute hematoma growth >6 mL or percentage growth >33%. HE was determined based on two criteria, using IPH alone (HEP) or IPH+IVH (HEP+V). The radiomics analysis was performed by using PyRadiomics to extract features, followed by random forest algorithm to select features, and lastly the decision tree to build classification models. Results: The classification of expansion showed 37 (44%) HEP and 47 (56%) non-HEP based on IPH alone, and similar results of 38 (45%) HEP+V and 46 (55%) non-HEP+V based on IPH+IVH. The majority, >94% of HE patients, had a poor outcome (death or mRS>3 at discharge). Three radiomics analysis (RA) models were built. The first model using baseline IPH to predict HEP (RAP-P) showed an accuracy of 80% but loss of correlation with the clinical outcome; the second model using IPH+IVH to predict HEP (RAPV-V) had a slightly higher accuracy of 81% and resumed the poor outcome association with HE; and the third model using IPH+IVH to predict HEP+V (RAPV-PV) had the highest accuracy of 86% with preserved clinical outcome correlation of HE. The sensitivity, specificity, and accuracy of three decision trees (RAP-P, RAPV-P, RAPV-PV) were 0.8/ 0.68/ 0.89; 0.81/ 0.92/ 0.72 and 0.86/ 0.82/ 0.89, respectively.Conclusions: The proposed radiomics approach with additional IVH information could be used to classify the expansion status highly associated with the clinical outcome and provide a robust tool for the enrollment of high-risk ICH cases in the anti-expansion trials.

show abstract

“…Three different prediction models were developed using the clinical and radiomics features (clinical model, radiomics model, and a hybrid model). The final assessment demonstrated that the hybrid model outperformed the other prediction models with an AUC of 0.820 [ 21 ]. Similar promising results have been reported for the radiomics technique in predicting the expansion of the hypertensive ICHs.…”

Section: Reviewmentioning

confidence: 99%

Emerging Applications of Radiomics in Neurological Disorders: A Review

Sotoudeh¹,

Sarrami²,

Roberson³

et al. 2021

Cureus

View full text Add to dashboard Cite

Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.

show abstract

Clinical-radiomics Nomogram for Risk Estimation of Early Hematoma Expansion after Acute Intracerebral Hemorrhage

Cited by 47 publications

References 35 publications

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

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

Impact of Intraventricular Hemorrhage on Classification of Hematoma Expansion and Development of Radiomics Prediction Models

Emerging Applications of Radiomics in Neurological Disorders: A Review

Contact Info

Product

Resources

About