Machine Learning Classification of Cerebral Aneurysm Rupture Status                     with Morphologic Variables and Hemodynamic Parameters

Tanioka, Satoru; Ishida, Fujimaro; Yamamoto, Atsushi; Shimizu, Shigetoshi; Sakaida, Hiroshi; Toyoda, Mitsuru; Kashiwagi, Nobuhisa; Suzuki, Hiroyoshi

doi:10.1148/ryai.2019190077

Cited by 46 publications

(43 citation statements)

References 29 publications

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“…Previous machine learning studies on intracranial aneurysm rupture status classification and rupture risk assessment have shown encouraging results. A recent study on morphologic and hemodynamic features of cerebral aneurysm on CTA revealed the projection ratio, irregular shape, and size ratio as important discriminators of ruptured aneurysms ( 26 ). Another study on clinical and imaging features has shown the location and size to have a strong association with aneurysm rupture ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

et al. 2021

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Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

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Section: Discussionmentioning

confidence: 99%

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

et al. 2021

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“…The submitted results on the recognition and segmentation sub-challenges present solutions whose performance is similar to that of human experts. State-of-theart methods [28][29][30][31][32][33][34][35][36][37]44] typically utilize a combination of morphological and CFD features. The top performing solutions submitted to the CADA challenge utilize similar features in combination with machine learning approaches to predict the risk of rupture.…”

Section: Discussionmentioning

confidence: 99%

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Ivantsits

Goubergrits

Kuhnigk

et al. 2021

Lecture Notes in Computer Science

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Rupture of an intracranial aneurysm often results in subarachnoid hemorrhage, a life-threatening condition with high mortality and morbidity. The Cerebral Aneurysm Detection and Analysis (CADA) competition was organized to support the development and benchmarking of algorithms for the detection, analysis, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. Segmentation quality was measured using Jaccard and a combination of different surface distance measurements. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADAchallenge. The detection solutions presented by the community are mostly accurate (F2 score 0.92) with a small number of missed aneurysms with diameters of 3.5 mm. In addition, the delineation of these structures is very good with a Jaccard score of 0.915. The rupture risk estimation methods achieved an F2 score of 0.7. The performance of the detection and segmentation solutions is equivalent to that of human experts. In rupture risk estimation, the best results are obtained by combining different image-based, morphological and computational fluid dynamic parameters using machine learning methods.

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“…ANNs have been widely used in a variety of neurosurgical applications, such as predicting the occurrence of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage [ 6 ], traumatic brain injury outcome and survival [ 7 , 8 ], recurrent lumbar disk herniation [ 9 ], and endoscopic third ventriculostomy success in childhood hydrocephalus [ 10 ]. Regarding cerebral aneurysm surgeries, the majority of the studies deployed these techniques to predict the aneurysm rupture [ 11 , 12 ], or for automated detection of the aneurysms on imaging [ 13 ]. In this study, we aimed to evaluate the feasibility and validity of ANN modeling in predicting the SCT and for determining the prominent clinical features of cerebral aneurysm surgeries.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery

Shahjouei

Ghodsi

Soroush

et al. 2021

JCM

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Background. Temporary artery clipping facilitates safe cerebral aneurysm management, besides a risk for cerebral ischemia. We developed an artificial neural network (ANN) to predict the safe clipping time of temporary artery occlusion (TAO) during intracranial aneurysm surgery. Method. We devised a three-layer model to predict the safe clipping time for TAO. We considered age, the diameter of the right and left middle cerebral arteries (MCAs), the diameter of the right and left A1 segment of anterior cerebral arteries (ACAs), the diameter of the anterior communicating artery, mean velocity of flow at the right and left MCAs, and the mean velocity of flow at the right and left ACAs, as well as the Fisher grading scale of brain CT scans as the input values for the model. Results. This study included 125 patients: 105 patients from a retrospective cohort for training the model and 20 patients from a prospective cohort for validating the model. The output of the neural network yielded up to 960 s overall safe clipping time for TAO. The input values with the greatest impact on safe TAO were mean velocity of blood at left MCA and left ACA, and Fisher grading scale of brain CT scan. Conclusion. This study presents an axillary framework to improve the accuracy of the estimated safe clipping time interval of temporary artery occlusion in intracranial aneurysm surgery.

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Machine Learning Classification of Cerebral Aneurysm Rupture Status with Morphologic Variables and Hemodynamic Parameters

Cited by 46 publications

References 29 publications

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery

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