Objective Accurate prediction of cerebral aneurysm (CA) rupture is of great significance. We intended to evaluate the accuracy of the point cloud neural network (PC-NN) in predicting CA rupture using MR angiography (MRA) and CT angiography (CTA) data.
Methods 418 CAs in 411 consecutive patients confirmed by CTA (n=180) or MRA (n=238) in a single hospital were retrospectively analyzed. A PC-NN aneurysm model with/without parent artery involvement was used for CA rupture prediction and compared with ridge regression, support vector machine (SVM) and neural network (NN) models based on radiomics features. Furthermore, the performance of the trained PC-NN and radiomics-based models was prospectively evaluated in 258 CAs of 254 patients from five external centers.
Results In the internal test data, the area under the curve (AUC) of the PC-NN model trained with parent artery (AUC=0.913) was significantly higher than that of the PC-NN model trained without parent artery (AUC=0.851; p=0.041) and of the ridge regression (AUC=0.803; p=0.019), SVM (AUC=0.788; p=0.013) and NN (AUC=0.805; p=0.023) radiomics-based models. Additionally, the PC-NN model trained with MRA source data achieved a higher prediction accuracy (AUC=0.936) than that trained with CTA source data (AUC=0.824; p=0.043). In external data of prospective cohort patients, the AUC of PC-NN was 0.835, significantly higher than ridge regression (0.692; p<0.001), SVM (0.701; p<0.001) and NN (0.681; p<0.001) models.
Conclusion PC-NNs can achieve more accurate CA rupture prediction than traditional radiomics-based models. Furthermore, the performance of the PC-NN model trained with MRA data was superior to that trained with CTA data.
- Magnetic Resonance Angiography
- CT Angiography
Data availability statement
Data are available upon reasonable request. Not applicable.
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XL and JW contributed equally.
Contributors XL, JW: Acquisition of data, study concept and design, analysis and interpretation of data and drafting of the manuscript. MW, YZ: Study concept and design, critical revision of the manuscript for important intellectual content, and study supervision. XL, LY, XC, JH, JL, BZ, GH: Acquisition of data, revision of the manuscript. XL, JW: Statistical analysis. YZ: Responsible for the overall content as guarantor
Funding Supported by Shanghai Municipal Education Commission (Gaofeng Clinical Medicine grant support no. 20152528) and Shanghai Jiao Tong University Medical and Research Program (ZH2018ZDA19).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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