You are here

Article Text

Article menu
Download PDFPDF
Neuroimaging
Original research
Prediction of cerebral aneurysm rupture using a point cloud neural network
  1. Xiaoyuan Luo1,
  2. Jienan Wang2,
  3. Xinmei Liang3,
  4. Lei Yan4,
  5. XinHua Chen5,
  6. Jian He6,
  7. Jing Luo7,
  8. Bing Zhao8,
  9. Guangchen He2,
  10. Manning Wang1,
  11. Yueqi Zhu2
  1. 1 Digital Medical Research Center and also with the Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Fudan University, Shanghai, China
  2. 2 Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
  3. 3 Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, China
  4. 4 Department of Interventional Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
  5. 5 Department of Neurosurgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
  6. 6 Department of Nuclear Medicine, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
  7. 7 Department of Neurosurgery, Anhui Medical University Affiliated First Hospital, Hefei, China
  8. 8 Department of Neurosurgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  1. Correspondence to Dr Yueqi Zhu, Department of Radiology, Shanghai Jiaotong University Affiliated sixth People's Hospital, Shanghai, China; zhuyueqi{at}hotmail.com; Dr Manning Wang; mnwang{at}fudan.edu.cn

Abstract

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.

  • Aneurysm
  • Intervention
  • Magnetic Resonance Angiography
  • CT Angiography

Data availability statement

Data are available upon reasonable request. Not applicable.

https://doi.org/10.1136/neurintsurg-2022-018655

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Data availability statement

    Data are available upon reasonable request. Not applicable.

    View Full Text

    Footnotes

    • 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.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.