A Clinical Predictive Nomogram for Traumatic Brain Parenchyma Hematoma Progression

Sheng, Jiangtao; Chen, Weiqiang; Zhuang, Dongzhou; Li, Tian; Yang, Jing; Cai, Shirong; Chen, Xiaoxuan; Liu, Xueer; Tian, Fei; Huang, Mindong; Li, Lianjie; Li, Kangsheng

doi:10.1007/s40120-021-00306-8

Cited by 11 publications

(13 citation statements)

References 48 publications

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“…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: 88%

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

et al. 2022

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(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.

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Section: Discussionmentioning

confidence: 88%

“…Despite the good prognostic value of radiomics in spontaneous IPH being reported, research evidence on traumatic IPH is scarce [ 31 , 32 ]. The purpose of this study was to demonstrate the feasibility of radiomics to predict HPC and poor neurologic outcome in traumatic IPH patients.…”

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

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“…Considering the multiple factors associated with TBI, including medical and socioeconomic elements, we proposed a more comprehensive model to reflect the dynamic changes during TBI treatment. In recent years, many nomogram models for TBI have been developed for prognostic prediction based on clinicopathological factors ( 12 , 33 ). For example, a multicenter observational study proposed an algorithm based on nomograms that can predict mortality in real-time during intensive care after TBI ( 34 ).…”

Section: Discussionmentioning

confidence: 99%

“…It has been limited due to a lack of objective standard guidelines and validated risk factor models for poor prognosis to predict the outcomes of interest ( 11 ). The establishment of predictive models for patients with TBI could enhance the speed of the decision-making process for patients, families, and healthcare professionals with objective treatments ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Epidemiological characteristics for patients with traumatic brain injury and the nomogram model for poor prognosis: an 18-year hospital-based study

Guo,

Han,

Chen

et al. 2023

Front. Neurol.

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ObjectiveTraumatic brain injury (TBI) is a global social, economic, and health challenge that is associated with premature death and long-term disability. In the context of rapid development of urbanization, the analysis of TBI rate and mortality trend could provide abundant diagnosis and treatment suggestions, which helps to form future reference on public health strategies.MethodsIn this study, as one of major neurosurgical centers in China, we focused on the regime shift of TBI based on 18-year consecutive clinical data and evaluated the epidemiological features. In our current study, a total of 11,068 TBI patients were reviewed.ResultsThe major cause of TBI was road traffic injuries (44.%), while the main type of injury was cerebral contusion (n = 4,974 [44.94%]). Regarding to temporal changes, a decreasing trend in TBI incidence for patients under 44 years old was observed, while an increasing trend for those aged over 45 years was indicated. Incidences of RTI and assaults decreased, while ground level fall presented increasing incidences. The total number of deaths was 933 (8.43%), with a decreasing trend in overall mortality since 2011. Age, cause of injury, GCS at admission, Injury Severity Score, shock state at admission, trauma-related diagnoses and treatments were significantly associated with mortality. A predictive nomogram model for poor prognosis was developed based on patient's GOS scores at discharge.ConclusionsThe trends and characteristics of TBI patients changed with rapid development of urbanization in the past 18 years. Further larger studies are warranted to verify its clinical suggestions.

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“…The larger the hematoma volume is, the worse the damage to brain tissue. In general, emergency intervention, including intubation, ventilation and neuromonitoring, should be applied for acute ICH ( Sheng et al, 2022 ), and a surgical decision should be made if the hematoma volume exceeds 30 mL ( Rodriguez-Luna et al, 2017 ). Therefore, accurate measurement of ICH volume is of vital significance for determining the severity of brain injuries.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage

Chen

Zhang

et al. 2022

Front. Neurosci.

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The study aims to enhance the accuracy and practicability of CT image segmentation and volume measurement of ICH by using deep learning technology. A dataset including the brain CT images and clinical data of 1,027 patients with spontaneous ICHs treated from January 2010 to December 2020 were retrospectively analyzed, and a deep segmentation network (AttFocusNet) integrating the focus structure and the attention gate (AG) mechanism is proposed to enable automatic, accurate CT image segmentation and volume measurement of ICHs. In internal validation set, experimental results showed that AttFocusNet achieved a Dice coefficient of 0.908, an intersection-over-union (IoU) of 0.874, a sensitivity of 0.913, a positive predictive value (PPV) of 0.957, and a 95% Hausdorff distance (HD95) (mm) of 5.960. The intraclass correlation coefficient (ICC) of the ICH volume measurement between AttFocusNet and the ground truth was 0.997. The average time of per case achieved by AttFocusNet, Coniglobus formula and manual segmentation is 5.6, 47.7, and 170.1 s. In the two external validation sets, AttFocusNet achieved a Dice coefficient of 0.889 and 0.911, respectively, an IoU of 0.800 and 0.836, respectively, a sensitivity of 0.817 and 0.849, respectively, a PPV of 0.976 and 0.981, respectively, and a HD95 of 5.331 and 4.220, respectively. The ICC of the ICH volume measurement between AttFocusNet and the ground truth were 0.939 and 0.956, respectively. The proposed segmentation network AttFocusNet significantly outperforms the Coniglobus formula in terms of ICH segmentation and volume measurement by acquiring measurement results closer to the true ICH volume and significantly reducing the clinical workload.

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A Clinical Predictive Nomogram for Traumatic Brain Parenchyma Hematoma Progression

Cited by 11 publications

References 48 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

Epidemiological characteristics for patients with traumatic brain injury and the nomogram model for poor prognosis: an 18-year hospital-based study

Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage

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