Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor

Turhon, Mirzat; Li, Mengxing; Kang, Huibin; Huang, Jiliang; Zhang, Fujunhui; Zhang, Ying; Zhang, Yisen; Maimaiti, Aierpati; Gheyret, Dilmurat; Axier, Aximujiang; Aisha, Miamaitili; Yang, Xinjian; Liu, Jian

doi:10.1007/s00330-023-09672-3

Cited by 21 publications

(4 citation statements)

References 26 publications

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“…So far, only a few neurosurgical ML studies were published with an external validation of their models. Good generalisability of external validation is seen in the radiological diagnosis of UIAs 54 or in the prediction of intracranial aneurysm rupture risk based on multi-omics factors 55 . Fuse et al published an external validation of their preoperative prediction model for postoperative outcomes after chronic subdural hematoma evacuation and external validation revealed an excellent ROC-AUC of 0.860 56 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Stroh,

Stefanits,

Maletzky

et al. 2023

Sci Rep

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Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model’s predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p < 0.0001). The present results showed good performance in predicting functional and clinical outcomes after microsurgical therapy of UIAs in the internal data set, especially for the main outcome parameters, mRS and permanent neurological deficit. The external validation showed poor discrimination with ROC-AUC values of 0.61, 0.53 and 0.58 respectively for predicting a postoperative mRS > 2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future.

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Section: Discussionmentioning

confidence: 99%

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Stroh,

Stefanits,

Maletzky

et al. 2023

Sci Rep

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“…However, only age and gender were included as clinical features, necessitating further exploration of the impact of including additional clinical features on result accuracy. Turhon et al ( 37 ) included 1,740 IA patients and constructed traditional ML and deep learning models based on clinical, radiomics, and morphological features. The results indicated that the deep learning-based radiomics model for predicting aneurysm rupture (AUC = 0.929) outperformed traditional ML models (AUC = 0.878), with the inclusion of morphological parameters also enhancing predictive performance.…”

Section: Applicationsmentioning

confidence: 99%

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Wen,

Wang,

Zhong

et al. 2024

Front. Neurol.

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Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

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“…Worldwide, more than 3% of the population without risk factors has an incidental intracranial aneurysm, but in China, 7% are reported [ 2 ]. Due to frequent use of CTA and MRA, the detection rate of UIAs is steadily increasing, and neurovascular centres have the impression that they are overrun with aneurysms.…”

mentioning

confidence: 99%

For whom the bell tolls – do we overestimate wall enhancement of intracranial aneurysms?

Götz

2024

Eur Radiol

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Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor

Cited by 21 publications

References 26 publications

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

For whom the bell tolls – do we overestimate wall enhancement of intracranial aneurysms?

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