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Accuracy and reliability of computer-assisted semi-automated morphological analysis of intracranial aneurysms: an experimental study with digital phantoms and clinical aneurysm cases

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Abstract

Purpose

Morphological parameters are very important for predicting aneurysm rupture. However, due to geometric radiographic distortion and plane/angle selection bias, the traditional manual measurements (MM) of aneurysm morphology are inaccurate and suffer from severe variability. Our study is to evaluate the accuracy and reliability of computer-assisted semi-automated measurement (CASAM) of intracranial aneurysms, which is a novel technique in aneurysm measurement.

Methods

An in-house software for CASAM was developed. Classical morphology indices including aneurysm diameter, neck size, height, width, volume, inflow angle, and aspect ratio were measured. To validate the accuracy and robustness of the semi-automated measurements, 20 digital intracranial aneurysm phantoms and 27 clinical aneurysms with different locations and sizes were measured using MM or CASAM.

Results

In the phantom study, although the inter-observer variability of both the MM and CASAM was very low, the manual measurements had higher errors (1.7–19.1%), while the CASAM yielded more accurate results (errors of 1.1–2.5%). The consistency test indicated that the CASAM results were highly consistent with the actual values (concordance correlation coefficient = 0.993). In the clinical study, CASAM showed better intraclass correlation coefficient values compared with MM. The inflow angle had low consistency in both groups.

Conclusions

We successfully developed a computer-assisted method to semi-automatically measure the morphological parameters of aneurysm. According to our study, CASAM of aneurysm morphological parameters is a more precise and reliable way than MM to obtain accurate aneurysm morphological parameters. This method is worthy of further studies to promote its clinical use.

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References

  1. Caranci F, Briganti F, Cirillo L, Leonardi M, Muto M (2013) Epidemiology and genetics of intracranial aneurysms. Eur J Radiol 82(10):1598–1605. https://doi.org/10.1016/j.ejrad.2012.12.026

    Article  CAS  PubMed  Google Scholar 

  2. Korja M, Kaprio J (2016) Controversies in epidemiology of intracranial aneurysms and SAH. Nat Rev Neurol 12(1):50–55. https://doi.org/10.1038/nrneurol.2015.228

    Article  PubMed  Google Scholar 

  3. Brown RD Jr, Broderick JP (2014) Unruptured intracranial aneurysms: epidemiology, natural history, management options, and familial screening. Lancet Neurol 13(4):393–404. https://doi.org/10.1016/S1474-4422(14)70015-8

    Article  PubMed  Google Scholar 

  4. Rinkel GJ, Djibuti M, Algra A, van Gijn J (1998) Prevalence and risk of rupture of intracranial aneurysms: a systematic review. Stroke 29(1):251–256. https://doi.org/10.1161/01.str.29.1.251

    Article  CAS  PubMed  Google Scholar 

  5. Wiebers DO, Whisnant JP, Huston J 3rd, Meissner I, Brown RD Jr., Piepgras DG, Forbes GS, Thielen K, Nichols D, Ofallon WM, Peacock J, Jaeger L, Kassell NF, Kongable-Beckman GL, Torner JC, International Study of Unruptured Intracranial Aneurysms I (2003) Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 362(9378):103–110. https://doi.org/10.1016/s0140-6736(03)13860-3

    Article  PubMed  Google Scholar 

  6. van Gijn J, Kerr RS, Rinkel GJ (2007) Subarachnoid haemorrhage. Lancet 369(9558):306–318. https://doi.org/10.1016/S0140-6736(07)60153-6

    Article  PubMed  Google Scholar 

  7. Suarez JI, Tarr RW, Selman WR (2006) Aneurysmal subarachnoid hemorrhage. N Engl J Med 354(4):387–396. https://doi.org/10.1056/NEJMra052732

    Article  CAS  PubMed  Google Scholar 

  8. Connolly ES Jr, Rabinstein AA, Carhuapoma JR, Derdeyn CP, Dion J, Higashida RT, Hoh BL, Kirkness CJ, Naidech AM, Ogilvy CS, Patel AB, Thompson BG, Vespa P, American Heart Association Stroke C, Council on Cardiovascular R, Intervention, Council on Cardiovascular N, Council on Cardiovascular S, Anesthesia, Council on Clinical C (2012) Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/american Stroke Association. Stroke 43(6):1711–1737. https://doi.org/10.1161/STR.0b013e3182587839

    Article  PubMed  Google Scholar 

  9. Wang GX, Zhang D, Wang ZP, Yang LQ, Yang H, Li W (2018) Risk factors for ruptured intracranial aneurysms. Indian J Med Res 147(1):51–57. https://doi.org/10.4103/ijmr.IJMR_1665_15

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jiang P, Liu Q, Wu J, Chen X, Li M, Li Z, Yang S, Guo R, Gao B, Cao Y, Wang S (2018) A novel scoring system for rupture risk stratification of intracranial aneurysms: a hemodynamic and morphological study. Front Neurosci 12:596. https://doi.org/10.3389/fnins.2018.00596

    Article  PubMed  PubMed Central  Google Scholar 

  11. Murayama Y, Takao H, Ishibashi T, Saguchi T, Ebara M, Yuki I, Arakawa H, Irie K, Urashima M, Molyneux AJ (2016) Risk analysis of unruptured intracranial aneurysms: prospective 10-year cohort study. Stroke 47(2):365–371. https://doi.org/10.1161/STROKEAHA.115.010698

    Article  PubMed  Google Scholar 

  12. Zheng Y, Xu F, Ren J, Xu Q, Liu Y, Tian Y, Leng B (2016) Assessment of intracranial aneurysm rupture based on morphology parameters and anatomical locations. J Neurointerv Surg 8(12):1240–1246. https://doi.org/10.1136/neurintsurg-2015-012112

    Article  PubMed  Google Scholar 

  13. Huhtakangas J, Lehecka M, Lehto H, Jahromi BR, Niemela M, Kivisaari R (2017) CTA analysis and assessment of morphological factors related to rupture in 413 posterior communicating artery aneurysms. Acta Neurochir (Wien) 159(9):1643–1652. https://doi.org/10.1007/s00701-017-3263-4

    Article  Google Scholar 

  14. Larrabide I, Cruz Villa-Uriol M, Cardenes R, Pozo JM, Macho J, San Roman L, Blasco J, Vivas E, Marzo A, Hose DR, Frangi AF (2011) Three-dimensional morphological analysis of intracranial aneurysms: a fully automated method for aneurysm sac isolation and quantification. Med Phys 38(5):2439–2449. https://doi.org/10.1118/1.3575417

    Article  PubMed  Google Scholar 

  15. Kim HC, Rhim JK, Ahn JH, Park JJ, Moon JU, Hong EP, Kim MR, Kim SG, Lee SH, Jeong JH, Choi SW, Jeon JP (2019) Machine learning application for rupture risk assessment in small-sized intracranial aneurysm. J Clin Med. https://doi.org/10.3390/jcm8050683

    Article  PubMed  PubMed Central  Google Scholar 

  16. Antiga L (2002) Patient-specific modeling of geometry and blood flow in large arteries. Politecnico di Milano, Milan. http://lantiga.github.io/media/AntigaPhDThesis.pdf

  17. Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA (2008) An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Comput 46(11):1097–1112. https://doi.org/10.1007/s11517-008-0420-1

    Article  PubMed  Google Scholar 

  18. Antiga L, Ene-Iordache B, Remuzzi A (2003) Centerline computation and geometric analysis of branching tubular surfaces with application to blood vessel modeling. In: 11-th international conference in Central Europe on computer graphics, visualization and computer vision. https://doi.org/10.1002/gj.1001

  19. Antiga L, Ene-Iordache B, Remuzzi A (2003) Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography. IEEE Trans Med Imaging 22(5):674–684

    Article  PubMed  Google Scholar 

  20. Rajabzadeh-Oghaz H, Varble N, Shallwani H, Tutino VM, Mowla A, Shakir HJ, Vakharia K, Atwal GS, Siddiqui AH, Davies JM, Meng H (2018) Computer-assisted three-dimensional morphology evaluation of intracranial aneurysms. World Neurosurg 119:e541–e550. https://doi.org/10.1016/j.wneu.2018.07.208

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hernandez M, Frangi AF (2007) Non-parametric geodesic active regions: method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA. Med Image Anal 11(3):224–241. https://doi.org/10.1016/j.media.2007.01.002

    Article  PubMed  Google Scholar 

  22. Villa-Uriol MC, Larrabide I, Geers AJ, Pozo J, Bogunovic H, Mazzeo M, Omedas P, Barbarito V, Carotenuto L, Riccobene C, Planes X, Martelli Y, Frangi AF (2010) AngioLab: integrated technology for patient-specific management of intracranial aneurysms. Conf Proc IEEE Eng Med Biol Soc 2010:6801–6804. https://doi.org/10.1109/IEMBS.2010.5625974

    Article  CAS  Google Scholar 

  23. Larrabide I, Villa-Uriol MC, Cardenes R, Barbarito V, Carotenuto L, Geers AJ, Morales HG, Pozo JM, Mazzeo MD, Bogunovic H, Omedas P, Riccobene C, Macho JM, Frangi AF (2012) AngioLab–a software tool for morphological analysis and endovascular treatment planning of intracranial aneurysms. Comput Methods Programs Biomed 108(2):806–819. https://doi.org/10.1016/j.cmpb.2012.05.006

    Article  PubMed  Google Scholar 

  24. Piccinelli M, Veneziani A, Steinman DA, Remuzzi A, Antiga L (2009) A framework for geometric analysis of vascular structures: application to cerebral aneurysms. IEEE Trans Med Imaging 28(8):1141–1155. https://doi.org/10.1109/TMI.2009.2021652

    Article  PubMed  Google Scholar 

  25. Xiang J, Antiga L, Varble N, Snyder KV, Levy EI, Siddiqui AH, Meng H (2016) AView: an image-based clinical computational tool for intracranial aneurysm flow visualization and clinical management. Ann Biomed Eng 44(4):1085–1096. https://doi.org/10.1007/s10439-015-1363-y

    Article  PubMed  Google Scholar 

  26. Anderson JR, Thompson WL, Alkattan AK, Diaz O, Klucznik R, Zhang YJ, Britz GW, Grossman RG, Karmonik C (2016) Three-dimensional printing of anatomically accurate, patient specific intracranial aneurysm models. J Neurointerv Surg 8(5):517–520. https://doi.org/10.1136/neurintsurg-2015-011686

    Article  PubMed  Google Scholar 

  27. Wang L, Ye X, Hao Q, Ma L, Chen X, Wang H, Zhao Y (2018) Three-dimensional intracranial middle cerebral artery aneurysm models for aneurysm surgery and training. J Clin Neurosci 50:77–82. https://doi.org/10.1016/j.jocn.2018.01.074

    Article  CAS  PubMed  Google Scholar 

  28. Kaneko N, Mashiko T, Namba K, Tateshima S, Watanabe E, Kawai K (2018) A patient-specific intracranial aneurysm model with endothelial lining: a novel in vitro approach to bridge the gap between biology and flow dynamics. J Neurointerv Surg 10(3):306–309. https://doi.org/10.1136/neurintsurg-2017-013087

    Article  PubMed  Google Scholar 

  29. Nakagawa D, Nagahama Y, Policeni BA, Raghavan ML, Dillard SI, Schumacher AL, Sarathy S, Dlouhy BJ, Wilson S, Allan L, Woo HH, Huston J, Cloft HJ, Wintermark M, Torner JC, Brown RD, Hasan DM (2018) Accuracy of detecting enlargement of aneurysms using different MRI modalities and measurement protocols. J Neurosurg 130(2):559–565. https://doi.org/10.3171/2017.9.JNS171811

    Article  PubMed  Google Scholar 

  30. Rajabzadeh-Oghaz H, Varble N, Davies JM, Mowla A, Shakir HJ, Sonig A, Shallwani H, Snyder KV, Levy EI, Siddiqui AH, Meng H (2017) Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches. Proc SPIE Int Soc Opt Eng. https://doi.org/10.1117/12.2255553

    Article  PubMed  PubMed Central  Google Scholar 

  31. Baharoglu MI, Schirmer CM, Hoit DA, Gao BL, Malek AM (2010) Aneurysm inflow-angle as a discriminant for rupture in sidewall cerebral aneurysms: morphometric and computational fluid dynamic analysis. Stroke 41(7):1423–1430. https://doi.org/10.1161/STROKEAHA.109.570770

    Article  PubMed  Google Scholar 

  32. Lazareska M, Aliji V, Stojovska-Jovanovska E, Businovska J, Mircevski V, Kostov M, Papazova M (2018) Endovascular treatment of wide neck aneurysms. Open Access Maced J Med Sci 6(12):2316–2322. https://doi.org/10.3889/oamjms.2018.443

    Article  PubMed  PubMed Central  Google Scholar 

  33. Heo HY, Ahn JG, Ji C, Yoon WK (2019) Selective temporary stent-assisted coil embolization for intracranial wide-necked small aneurysms using solitaire AB retrievable stent. J Korean Neurosurg Soc 62(1):27–34. https://doi.org/10.3340/jkns.2018.0064

    Article  PubMed  Google Scholar 

  34. Dhar S, Tremmel M, Mocco J, Kim M, Yamamoto J, Siddiqui AH, Hopkins LN, Meng H (2008) Morphology parameters for intracranial aneurysm rupture risk assessment. Neurosurgery 63(2):185–197. https://doi.org/10.1227/01.NEU.0000316847.64140.81

    Article  PubMed  Google Scholar 

  35. Tremmel M, Dhar S, Levy EI, Mocco J, Meng H (2009) Influence of intracranial aneurysm-to-parent vessel size ratio on hemodynamics and implication for rupture: results from a virtual experimental study. Neurosurgery 64(4):622–631. https://doi.org/10.1227/01.NEU.0000341529.11231.69

    Article  PubMed  Google Scholar 

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Funding

This work was supported by the National Key Research Development Program (#2016YFC1300800); the National Natural Science Foundation of China (#81500988); and the Project on research and application of effective intervention techniques for high risk population of stroke from the National Health and Family Planning Commission in China (GN-2016R0004).

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Author notes

  1. Jiewen Geng and Peng Hu have contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China

    Jiewen Geng, Peng Hu, Zhe Ji & Hongqi Zhang

  2. Department of Neurosurgery, The Hospital of Shunyi District Beijing, Beijing, China

    Chuanjie Li

  3. Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Hei Longjiang Province, China

    Li Li

  4. Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, He Fei, Anhui Province, China

    Jie Shen

  5. Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA

    Xue Feng

  6. Department of R&D, UnionStrong (Beijing) Technology Co. Ltd, Beijing, China

    Wenzhi Wang, Guangming Yang & Jianjun Li

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  1. Jiewen Geng
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Contributions

All authors have made a substantial contribution to the conception and design of the studies and/or the acquisition and/or the analysis of the data and/or the interpretation of the data; drafted the work or revised it for significant intellectual content; approved the final version of the manuscript; and agree to be accountable for all aspects of the work, including its accuracy and integrity.

Corresponding author

Correspondence to Hongqi Zhang.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of our institutional ethics committee (Xuanwu Hospital, No. 2017082).

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Informed consent was not required in our study because only anonymized data were used.

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Geng, J., Hu, P., Ji, Z. et al. Accuracy and reliability of computer-assisted semi-automated morphological analysis of intracranial aneurysms: an experimental study with digital phantoms and clinical aneurysm cases. Int J CARS 15, 1749–1759 (2020). https://doi.org/10.1007/s11548-020-02218-8

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  • DOI: https://doi.org/10.1007/s11548-020-02218-8

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