DOI QR코드

DOI QR Code

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

  • Kuchalambal Agadi (Division of Research and Academic Affairs, Larkin Health System) ;
  • Asimina Dominari (Division of Research and Academic Affairs, Larkin Health System) ;
  • Sameer Saleem Tebha (Division of Research and Academic Affairs, Larkin Health System) ;
  • Asma Mohammadi (Division of Research and Academic Affairs, Larkin Health System) ;
  • Samina Zahid (Division of Research and Academic Affairs, Larkin Health System)
  • 투고 : 2021.08.23
  • 심사 : 2022.03.14
  • 발행 : 2023.11.01

초록

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

키워드

과제정보

Previous presentation : the abstract of this manuscript was previously accepted for presentation at: 2021 AANS Annual Scientific Meeting, August 21-25, 2021, Orlando, Florida, USA. E-poster.

참고문헌

  1. Abi-Aad KR, Anderies BJ, Welz ME, Bendok BR : Machine Learning as a potential solution for shift during stereotactic brain surgery. Neurosurgery 82 : E102-E103, 2018 https://doi.org/10.1093/neuros/nyy043
  2. Akbari H, Rathore S, Bakas S, Nasrallah MP, Shukla G, Mamourian E, et al. : Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma. Cancer 126 : 2625-2636, 2020 https://doi.org/10.1002/cncr.32790
  3. American Cancer Society : Survival rates for selected adult brain and spinal cord tumors. Available at : https://www.cancer.org/cancer/brain-spinal-cord-tumors-adults/detection-diagnosis-staging/survival-rates.html
  4. Arksey H, O'Malley L : Scoping studies: towards a methodological framework. Int J Soc Res Methodol 8 : 19-32, 2005 https://doi.org/10.1080/1364557032000119616
  5. Arle JE, Morriss C, Wang ZJ, Zimmerman RA, Phillips PG, Sutton LN : Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks. J Neurosurg 86 : 755-761, 1997 https://doi.org/10.3171/jns.1997.86.5.0755
  6. Banzato T, Causin F, Della Puppa A, Cester G, Mazzai L, Zotti A : Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study. J Magn Reson Imaging 50 : 1152-1159, 2019 https://doi.org/10.1002/jmri.26723
  7. Bernardo A : Virtual reality and simulation in neurosurgical training. World Neurosurg 106 : 1015-1029, 2017 https://doi.org/10.1016/j.wneu.2017.06.140
  8. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL : Surgeon volume and operative mortality in the united states. N Engl J Med 349 : 2117-2127, 2003 https://doi.org/10.1056/NEJMsa035205
  9. Carlson ML, Link MJ : Vestibular schwannomas. N Engl J Med 384 : 1335-1348, 2021 https://doi.org/10.1056/NEJMra2020394
  10. Christopher AS, Caruso D : Promoting health as a human right in the post-ACA united states. AMA J Ethics 17 : 958-965, 2015 https://doi.org/10.1001/journalofethics.2015.17.10.msoc1-1510
  11. Dasgupta A, Gupta T, Pungavkar S, Shirsat N, Epari S, Chinnaswamy G, et al. : Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients. Neuro Oncol 21 : 115-124, 2019
  12. Dewan MC, Rattani A, Fieggen G, Arraez MA, Servadei F, Boop FA, et al. : Global neurosurgery: the current capacity and deficit in the provision of essential neurosurgical care. Executive summary of the global neurosurgery initiative at the program in global surgery and social change. J Neurosurg 130 : 1055-1064, 2018
  13. Dietrich J : Clinical presentation, diagnosis, and initial surgical management of high-grade gliomas. Available at : https://www.uptodate.com/contents/clinical-presentation-diagnosis-and-initialsurgical-management-of-high-grade-gliomas
  14. Dorsey JF, Salinas RD, Dang M : Chapter 63: cancer of the central nervous system in Niederhuber JE, Armitage JO, Doroshow JH, Kastan MB, Tepper JE (eds) : Abeloff's Clinical Oncology, ed 6. Philadelphia : Elsevier, 2020, pp906-967
  15. Emblem KE, Pinho MC, Zollner FG, Due-Tonnessen P, Hald JK, Schad LR, et al. : A generic support vector machine model for preoperative glioma survival associations. Radiology 275 : 228-234, 2015 https://doi.org/10.1148/radiol.14140770
  16. Fabelo H, Ortega S, Ravi D, Kiran BR, Sosa C, Bulters D, et al. : Spatiospectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS One 13 : e0193721, 2018
  17. Frisken S, Luo M, Machado I, Unadkat P, Juvekar P, Bunevicius A, et al. : Preliminary results comparing thin plate splines with finite element methods for modeling brain deformation during neurosurgery using intraoperative ultrasound. Proc SPIE Int Soc Opt Eng 10951 : 1095120, 2019
  18. Hashimoto DA, Rosman G, Rus D, Meireles OR : Artificial intelligence in surgery: promises and perils. Ann Surg 268 : 70-76, 2018 https://doi.org/10.1097/SLA.0000000000002693
  19. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K : The practical implementation of artificial intelligence technologies in medicine. Nat Med 25 : 30-36, 2019 https://doi.org/10.1038/s41591-018-0307-0
  20. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS : Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med 26 : 52-58, 2020 https://doi.org/10.1038/s41591-019-0715-9
  21. Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, et al. : Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One 10 : e0141506, 2015
  22. Jakola AS, Sagberg LM, Gulati S, Solheim O : Advancements in predicting outcomes in patients with glioma: a surgical perspective. Expert Rev Anticancer Ther 20 : 167-177, 2020 https://doi.org/10.1080/14737140.2020.1735367
  23. Kamen A, Sun S, Wan S, Kluckner S, Chen T, Gigler AM, et al. : Automatic tissue differentiation based on confocal endomicroscopic images for intraoperative guidance in neurosurgery. Biomed Res Int 2016 : 6183218, 2016
  24. Karhade AV, Ahmed AK, Pennington Z, Chara A, Schilling A, Thio QCBS, et al. : External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease. Spine J 20 : 14-21, 2020 https://doi.org/10.1016/j.spinee.2019.09.003
  25. Karhade AV, Thio QCBS, Ogink PT, Shah AA, Bono CM, Oh KS, et al. : Development of machine learning algorithms for prediction of 30-day mortality after surgery for spinal metastasis. Neurosurgery 85 : E83-E91, 2019 https://doi.org/10.1093/neuros/nyy469
  26. Ker J, Bai Y, Lee HY, Rao J, Wang L : Automated brain histology classification using machine learning. J Clin Neurosci 66 : 239-245, 2019 https://doi.org/10.1016/j.jocn.2019.05.019
  27. Khalsa SSS, Hollon TC, Adapa A, Urias E, Srinivasan S, Jairath N, et al. : Automated histologic diagnosis of CNS tumors with machine learning. CNS Oncol 9 : CNS56, 2020
  28. Krivoshapkin AL, Sergeev GS, Kalneus LE, Gaytan AS, Murtazin VI, Kurbatov VP, et al. : New software for preoperative diagnostics of meningeal tumor histologic types. World Neurosurg 90 : 123-132, 2016 https://doi.org/10.1016/j.wneu.2016.02.084
  29. Kwoh YS, Hou J, Jonckheere EA, Hayati S : A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng 35 : 153-160, 1988 https://doi.org/10.1109/10.1354
  30. Lee JH, Han IH, Kim DH, Yu S, Lee IS, Song YS, et al. : Spine computed tomography to magnetic resonance image synthesis using generative adversarial networks : a preliminary study. J Korean Neurosurg Soc 63 : 386-396, 2020 https://doi.org/10.3340/jkns.2019.0084
  31. Li L, Wang K, Ma X, Liu Z, Wang S, Du J, et al. : Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma. Eur J Radiol 118 : 81-87, 2019 https://doi.org/10.1016/j.ejrad.2019.07.006
  32. Li Z, Wang Y, Yu J, Shi Z, Guo Y, Chen L, et al. : Low-grade glioma segmentation based on CNN with fully connected CRF. J Healthc Eng 2017 : 9283480, 2017
  33. Manni F, Van der Sommen F, Fabelo H, Zinger S, Shan C, Edstrom E, et al. : Hyperspectral imaging for glioblastoma surgery: improving tumor identification using a deep spectral-spatial approach. Sensors (Basel) 20 : 6955, 2020
  34. Marcus AP, Marcus HJ, Camp SJ, Nandi D, Kitchen N, Thorne L : Improved prediction of surgical resectability in patients with glioblastoma using an artificial neural network. Sci Rep 10 : 5143, 2020
  35. Mathur A, Jain N, Kesavadas C, Thomas B, Kapilamoorthy TR : Imaging of skull base pathologies: role of advanced magnetic resonance imaging techniques. Neuroradiol J 28 : 426-437, 2015 https://doi.org/10.1177/1971400915609341
  36. Mattei TA, Rodriguez AH, Sambhara D, Mendel E : Current state-of-the-art and future perspectives of robotic technology in neurosurgery. Neurosurg Rev 37 : 357-366, 2014 https://doi.org/10.1007/s10143-014-0540-z
  37. McGrath H, Li P, Dorent R, Bradford R, Saeed S, Bisdas S, et al. : Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI. Int J Comput Assist Radiol Surg 15 : 1445-1455, 2020 https://doi.org/10.1007/s11548-020-02222-y
  38. Nam KH, Seo I, Kim DH, Lee JI, Choi BK, Han IH : Machine learning model to predict osteoporotic spine with hounsfield units on lumbar computed tomography. J Korean Neurosurg Soc 62 : 442-449, 2019 https://doi.org/10.3340/jkns.2018.0178
  39. National Cancer Institute : Adult Central Nervous System Tumors Treatment - Health Professional Version. National Cancer Institute. Available at : https://www.cancer.gov/types/brain/hp/adult-brain-treatment-pdq
  40. Nematollahi M, Jajroudi M, Arbabi F, Azarhomayoun A, Azimifar Z : The benefits of decision tree to predict survival in patients with glioblastoma multiforme with the use of clinical and imaging features. Asian J Neurosurg 13 : 697-702, 2018 https://doi.org/10.4103/ajns.AJNS_336_16
  41. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. : The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 : n71, 2021
  42. Palmisciano P, Jamjoom AAB, Taylor D, Stoyanov D, Marcus HJ : Attitudes of patients and their relatives toward artificial intelligence in neurosurgery. World Neurosurg 138 : e627-e633, 2020 https://doi.org/10.1016/j.wneu.2020.03.029
  43. Peng L, Parekh V, Huang P, Lin DD, Sheikh K, Baker B, et al. : Distinguishing true progression from radionecrosis after stereotactic radiation therapy for brain metastases with machine learning and radiomics. Int J Radiat Oncol Biol Phys 102 : 1236-1243, 2018 https://doi.org/10.1016/j.ijrobp.2018.05.041
  44. Pope WB : Brain metastases: neuroimaging. Handb Clin Neurol 149 : 89-112, 2018 https://doi.org/10.1016/B978-0-12-811161-1.00007-4
  45. Racine E, Boehlen W, Sample M : Healthcare uses of artificial intelligence: challenges and opportunities for growth. Healthc Manage Forum 32 : 272-275, 2019 https://doi.org/10.1177/0840470419843831
  46. Scaringi C, Agolli L, Minniti G : Technical advances in radiation therapy for brain tumors. Anticancer Res 38 : 6041-6045, 2018 https://doi.org/10.21873/anticanres.12954
  47. Schlich T : The art and science of surgery: innovation and concepts of medical practice in operative fracture care, 1960s-1970s. Sci Technol Human Values 32 : 65-87, 2007 https://doi.org/10.1177/0162243906293886
  48. Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, et al. : Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery 83 : 181-192, 2018 https://doi.org/10.1093/neuros/nyx384
  49. Senders JT, Zaki MM, Karhade AV, Chang B, Gormley WB, Broekman ML, et al. : An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien) 160 : 29-38, 2018 https://doi.org/10.1007/s00701-017-3385-8
  50. Shaikhouni A, Elder JB : Computers and neurosurgery. World Neurosurg 78 : 392-398, 2012 https://doi.org/10.1016/j.wneu.2012.08.020
  51. Shen Z, Xie Y, Shang X, Xiong G, Chen S, Yao Y, et al. : The manufacturing procedure of 3D printed models for endoscopic endonasal trans-sphenoidal pituitary surgery. Technol Health Care 28 : 131-150, 2020 https://doi.org/10.3233/THC-209014
  52. Shu C, Wang Q, Yan X, Wang J : Whole-genome expression microarray combined with machine learning to identify prognostic biomarkers for high-grade glioma. J Mol Neurosci 64 : 491-500, 2018 https://doi.org/10.1007/s12031-018-1049-7
  53. Siyar S, Azarnoush H, Rashidi S, Del Maestro RF : Tremor assessment during virtual reality brain tumor resection. J Surg Educ 77 : 643-651, 2020 https://doi.org/10.1016/j.jsurg.2019.11.011
  54. Slosarek K, Bekman B, Wendykier J, Grzadziel A, Fogliata A, Cozzi L : In silico assessment of the dosimetric quality of a novel, automated radiation treatment planning strategy for linac-based radiosurgery of multiple brain metastases and a comparison with robotic methods. Radiat Oncol 13 : 41, 2018
  55. Stupp R, Taillibert S, Kanner A, Read W, Steinberg D, Lhermitte B, et al. : Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: a randomized clinical trial. JAMA 318 : 2306-2316, 2017 https://doi.org/10.1001/jama.2017.18718
  56. Stupp R, Wong ET, Kanner AA, Steinberg D, Engelhard H, Heidecke V, et al. : NovoTTF-100A versus physician's choice chemotherapy in recurrent glioblastoma: a randomised phase III trial of a novel treatment modality. Eur J Cancer 48 : 2192-2202, 2012 https://doi.org/10.1016/j.ejca.2012.04.011
  57. Van Niftrik CHB, Van der Wouden F, Staartjes VE, Fierstra J, Stienen MN, Akeret K, et al. : Machine learning algorithm identifies patients at high risk for early complications after intracranial tumor surgery: registry-based cohort study. Neurosurgery 85 : E756-E764, 2019 https://doi.org/10.1093/neuros/nyz145
  58. Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, et al. : Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation. JAMA Netw Open 2 : e198363, 2019
  59. Yan J, Liu L, Wang W, Zhao Y, Li KK, Li K, et al. : Radiomic features from multi-parameter MRI combined with clinical parameters predict molecular subgroups in patients with medulloblastoma. Front Oncol 10 : 558162, 2020
  60. Yock AD, Kim GY : Technical note: using K-means clustering to determine the number and position of isocenters in MLC-based multiple target intracranial radiosurgery. J Appl Clin Med Phys 18 : 351-357, 2017 https://doi.org/10.1002/acm2.12139
  61. Zini G : Artificial intelligence in hematology. Hematology 10 : 393-400, 2005 https://doi.org/10.1080/10245330410001727055