Predictive Value of CT Perfusion in Hemorrhagic Transformation after Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Jing, Xin; Dai, Fangyu; Wang, Binda; Wang, Yiming; Li, Jiaqian; Pan, Lulan; Liu, Jingjing; Liu, Haipeng; He, Shan

doi:10.3390/brainsci13010156

Cited by 10 publications

(2 citation statements)

References 81 publications

(92 reference statements)

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“…Fourth, we did not use multimodal data for our AI-based approach, but only CT-based data. However, for ICH onset prediction, other data such as perfusion indicators [ 47 ], hemodynamic metrics [ 48 ], morphology and anatomical differences of intracranial arterial vessels especially with regard to microvascular collateral circulation [ 49 , 50 ] or CT-derived secondary hemodynamic parameters, and laboratory test results could be used and even combined. Such a multimodal AI-based approach could again significantly improve ICH onset prediction and should be considered in future studies.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Rusche

Wasserthal

Breit

et al. 2023

JCM

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Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Rusche

Wasserthal

Breit

et al. 2023

JCM

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“…Currently, digital subtraction angiography (DSA) remains the gold standard for diagnosing MMD. Concurrently, CTP is a common tool used to assess cerebral hemodynamic status and has been widely used in clinical practice ( 12 , 13 ). CTP has improved patient selection for safe and effective treatment, especially for the evaluation of bypass surgery in patients with MMD.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the mean transit time using machine learning models based on radiomics features from digital subtraction angiography in moyamoya disease or moyamoya syndrome—a development and validation model study

Qin,

Guo,

Peng

et al. 2023

Cardiovasc Diagn Ther

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Background Digital subtraction angiography (DSA) is an important technique for diagnosis of moyamoya disease (MMD) or moyamoya syndrome (MMS), and computed tomography perfusion (CTP) is essential for assessing intracranial blood supply. The aim of this study was to assess whether radiomics features based on images of DSA could predict the mean transit time (MTT; outcome of CTP) using machine learning models. Methods The DSA images and MTT values of adult patients with MMD or MMS, according to the diagnostic guidelines for MMD, as well as control cases, were retrospectively collected in the Guangdong Provincial People’s Hospital between January 2018 and December 2020. A total of 93 features were extracted from the images of each case through 3-dimensional (3D) slicer. After features preprocessing and filtering, 3–4 features were selected by the least absolute shrinkage and selection operator (LASSO) regression algorithm. Prediction models were established using random forest (RF) and support vector machine (SVM) for MTT values. Single-factor receiver operating characteristic (ROC) curve analysis and partial-dependence (PD) profiles were conducted to investigate selected features and prediction models. Results Our results showed that prediction models based on RF models had the best performance in frontal lobe {area under the curve (AUC) [95% confidence interval (CI)] =1.000 (1.000–1.000)], parietal lobe [AUC (95% CI) =1.000 (1.000–1.000)], and basal ganglia/thalamus [AUC (95% CI) =0.922 (0.797–1.000)] in the test set, whereas the SVM model performed the best in the temporal lobe [AUC (95% CI) =0.962 (0.876–1.000)] in the test set. The AUC values in the test set were greater than 0.9. The PD profiles showed good robustness and consistency. Conclusions Prediction models based on radiomics features extracted from DSA images demonstrate excellent performance in predicting MTT in patients with MMD or MMS, which may provide guidance for future clinical practice.

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