EVOLUTION OF ROLE OF PRE-OPERATIVE MAGNETIC RESONANCE IMAGING IN PLANNING BRAIN TUMOUR SURGERIES: A SYSTEMATIC REVIEW
Main Article Content
Keywords
Pre-operative MRI, functional MRI, Glioma, Menigioma
Abstract
Background: "The pre-operative MRI serves as a valuable diagnostic tool in surgical planning, helping to pinpoint and gain a precise understanding of the extent of lesions, especially in brain tumor surgeries. Inadequate tumor removal increases the likelihood of incomplete detection and recurrence. Therefore, we focus on the utility of "pre-operative MRI" for assessing the status of the posterior surface margin due to its visibility and flexibility in guiding surgical resection.
Methodology: This systematic review adheres to the PRISMA guidelines and includes a comprehensive search across prominent electronic databases. The current review included various types of studies, such as Analytical studies, and full-text literature. In our study, we included the studies provide information about the preoperative MRI for planning of brain tumor surgeries. In the current study, the assessment of bias risk was carried out using the recommended method.
Result: In this review, we incorporated a total of 12 studies on MRI findings. The total number of cases included in 12 studies was 3544, with an average age of 48.46 years. Out of the total studies, the majority show that the preoperative MRI helped to improve the accuracy of brain tumor diagnosis and guided surgical procedures to enhance patient outcomes.
Conclusion: We describe the MRI role in pre-surgical brain tumor; the data showed that it reduced the postsurgical morbidity, especially when combined with other advanced imaging methods like diffusion-tensor imaging, intra-operative MRI, or cortical stimulation.
References
1. Villanueva-Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Neurosurgery. 2017 Sep;81(3):397.
2. Uday PA, Digvijay N, Jeeva JB. Pre-operative brain tumor segmentation using SLICER-3D. In2014 international Conference on green computing communication and electrical engineering (ICGCCEE) 2014 Mar 6 (pp. 1-4). IEEE.
3. Zhang B, MacFadden D, Damyanovich AZ, Rieker M, Stainsby J, Bernstein M, Jaffray DA, Mikulis D, Ménard C. Development of a geometrically accurate imaging protocol at 3 Tesla MRI for stereotactic radiosurgery treatment planning. Physics in Medicine & Biology. 2010 Oct 20; 55(22):6601.
4. Mohsen H, El-Dahshan ES, El-Horbaty ES, Salem AB. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal. 2018 Jun 1; 3(1):68-71.
5. https://www.mrmed.in/health-library/cancer-care/world-brain-tumor-day-2023
6. Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one. 2015 Oct 8; 10(10):e0140381.
7. Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems. 2021 Nov 8:1-23.
8. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR. Brain tumor classification using convolutional neural network. InWorld Congress on Medical Physics and Biomedical Engineering 2018: June 3-8, 2018, Prague, Czech Republic (Vol. 1) 2019 (pp. 183-189). Springer Singapore.\
9. Cancer Treatments Centers of America—Brain Cancer Types. Available online: https://www.cancercenter. com/cancer-types/brain-cancer/types (accessed on 30 November 2019
10. Abir TA, Siraji JA, Ahmed E, Khulna B. Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol. 2018; 4(8):65-79.
11. Satou M, Wang J, Nakano-Tateno T, Teramachi M, Suzuki T, Hayashi K, Lamothe S, Hao Y, Kurata H, Sugimoto H, Chik C. L-type amino acid transporter 1, LAT1, in growth hormone-producing pituitary tumor cells. Molecular and Cellular Endocrinology. 2020 Sep 15; 515:110868. [Cross reference]
12. Ismael MR, Abdel-Qader I. Brain tumor classification via statistical features and back-propagation neural network. In2018 IEEE international conference on electro/information technology (EIT) 2018 May 3 (pp. 0252-0257). IEEE.
13. Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications. 2020 Feb; 32:839-54.
14. Byrne DM, Dwivedi R, Minks D. Recommendations for cross-sectional imaging in cancer management. The Royal College of Radiologists: London, UK. 2014.
15. Núñez-Martín R, Cervera RC, Pulla MP. Gastrointestinal stromal tumour and second tumours: A literature review. Medicina Clínica (English Edition). 2017 Oct 23;149(8):345-50.
16. Schlemmer HP, Bachert P, Henze M, Buslei R, Herfarth K, Debus J, Van Kaick G. Differentiation of radiation necrosis from tumor progression using proton magnetic resonance spectroscopy. Neuroradiology. 2002 Mar;44:216-22.
17. Håberg A, Kvistad KA, Unsgård G, Haraldseth O. Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery. 2004 Apr 1;54(4):902-15.
18. Ulmer JL, Krouwer HG, Mueller WM, Ugurel MS, Kocak M, Mark LP. Pseudo-reorganization of language cortical function at fMR imaging: a consequence of tumor-induced neurovascular uncoupling. American journal of neuroradiology. 2003 Feb 1;24(2):213-7.
19. Trinh VT, Fahim DK, Maldaun MV, Shah K, McCutcheon IE, Rao G, Lang F, Weinberg J, Sawaya R, Suki D, Prabhu SS. Impact of preoperative functional magnetic resonance imaging during awake craniotomy procedures for intraoperative guidance and complication avoidance. Stereotactic and functional neurosurgery. 2014 Oct 1;92(5):315-22.
20. Nadkarni TN, Andreoli MJ, Nair VA, Yin P, Young BM, Kundu B, Pankratz J, Radtke A, Holdsworth R, Kuo JS, Field AS. Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: task and statistical threshold effects on language lateralization. NeuroImage: Clinical. 2015 Jan 1;7:415-23.
21. Morrison MA, Churchill NW, Cusimano MD, Schweizer TA, Das S, Graham SJ. Reliability of task-based fMRI for preoperative planning: a test-retest study in brain tumor patients and healthy controls. PLoS One. 2016 Feb 19;11(2):e0149547.
22. Yan PF, Yan L, Zhang Z, Salim A, Wang L, Hu TT, Zhao HY. Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases. International Journal of Surgery. 2016 Dec 1;36:109-17.
23. D’Andrea G, Trillo’ G, Picotti V, Raco A. Functional magnetic resonance imaging (fMRI), pre-intraoperative tractography in neurosurgery: the experience of Sant’Andrea Rome University Hospital. Trends in Reconstructive Neurosurgery: Neurorehabilitation, Restoration and Reconstruction. 2017:241-50.
24. Kosteniuk SE, Gui C, Gariscsak PJ, Lau JC, Megyesi JF. Impact of functional magnetic resonance imaging on clinical outcomes in a propensity-matched low grade glioma cohort. World Neurosurgery. 2018 Dec 1;120:e1143-8.
25. Yan PF, Yan L, Hu TT, Xiao DD, Zhang Z, Zhao HY, Feng J. The potential value of preoperative MRI texture and shape analysis in grading meningiomas: a preliminary investigation. Translational oncology. 2017 Aug 1;10(4):570-7.
26. Gunal V, Savardekar AR, Devi BI, Bharath RD. Preoperative functional magnetic resonance imaging in patients undergoing surgery for tumors around left (dominant) inferior frontal gyrus region. Surgical Neurology International. 2018;9.
27. Vysotski S, Madura C, Swan BE, Holdsworth RY, Lin YU, Del Rio AM, Wood JO, Kundu B, Penwarden A, Voss J, Gallagher T. Preoperative FMRI associated with decreased mortality and morbidity in brain tumor patients. Interdisciplinary Neurosurgery. 2018 Sep 1;13:40-5.
28. Arita K, Miwa M, Bohara M, Moinuddin FM, Kamimura K, Yoshimoto K. Precision of preoperative diagnosis in patients with brain tumor–A prospective study based on “top three list” of differential diagnosis for 1061 patients. Surgical Neurology International. 2020;11.
29. Rijnen S, Butterbrod E, Rutten G, Sitskoorn M, Gehring K. Presurgical Identification of Patients With Glioblastoma at Risk for Cognitive Impairment at 3-Month Follow-up 2020 Dec; 87(6): 1119–1129.
30. Jiang J, Yu J, Liu X, Deng K, Zhuang K, Lin F, Luo L. The efficacy of preoperative MRI features in the diagnosis of meningioma WHO grade and brain invasion. Frontiers in Oncology. 2023 Jan 18;12:1100350.
31. Bertani G, Fava E, Casaceli G, Carrabba G, Casarotti A, Papagno C, Castellano A, Falini A, Gaini SM, Bello L. Intraoperative mapping and monitoring of brain functions for the resection of low-grade gliomas: technical considerations. Neurosurgical focus. 2009 Oct 1;27(4):E4.
32. Weller P, Wittsack HJ, Siebler M, Hömberg V, Seitz RJ. Motor recovery as assessed with isometric finger movements and perfusion magnetic resonance imaging after acute ischemic stroke. Neurorehabilitation and Neural Repair. 2006 Sep;20(3):390-7.[cross ref]
33. Haberg A, Kvistad KA, Unsgard G, Haraldseth O. Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery. 2004 Apr 1;54(4):902-15.
34. Giussani C, Roux FE, Ojemann J, Sganzerla EP, Pirillo D, Papagno C. Is preoperative functional magnetic resonance imaging reliable for language areas mapping in brain tumor surgery? Review of language functional magnetic resonance imaging and direct cortical stimulation correlation studies. Neurosurgery. 2010 Jan 1;66(1):113-20.
35. Kapsalakis IZ, Kapsalaki EZ, Gotsis ED, Verganelakis D, Toulas P, Hadjigeorgiou G, Chung I, Fezoulidis I, Papadimitriou A, Robinson JS, Lee GP. Preoperative evaluation with FMRI of patients with intracranial gliomas. Radiology research and practice. 2012 Oct;2012.
36. Stippich C, Blatow M, Alzamora MG. Task-based presurgical functional MRI in patients with brain tumors. InClinical Functional MRI: Presurgical Functional Neuroimaging 2021 Dec 4 (pp. 121-195). Cham: Springer International Publishing.
37. Watanabe Y, Yamasaki F, Kajiwara Y, Takayasu T, Nosaka R, Akiyama Y, Sugiyama K, Kurisu K. Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. European journal of radiology. 2013 Apr 1; 82(4):658-63.
38. Prada F, Del Bene M, Mattei L, Lodigiani L, DeBeni S, Kolev V, Vetrano I, Solbiati L, Sakas G, DiMeco F. Preoperative magnetic resonance and intraoperative ultrasound fusion imaging for real-time neuronavigation in brain tumor surgery. Ultraschall in der Medizin-European Journal of Ultrasound. 2014 Nov 27:174-86.
39. Amiez C, Kostopoulos P, Champod AS, Collins DL, Doyon J, Del Maestro R, Petrides M. Preoperative functional magnetic resonance imaging assessment of higher-order cognitive function in patients undergoing surgery for brain tumors. Journal of neurosurgery. 2008 Feb 1; 108(2):258-68.
2. Uday PA, Digvijay N, Jeeva JB. Pre-operative brain tumor segmentation using SLICER-3D. In2014 international Conference on green computing communication and electrical engineering (ICGCCEE) 2014 Mar 6 (pp. 1-4). IEEE.
3. Zhang B, MacFadden D, Damyanovich AZ, Rieker M, Stainsby J, Bernstein M, Jaffray DA, Mikulis D, Ménard C. Development of a geometrically accurate imaging protocol at 3 Tesla MRI for stereotactic radiosurgery treatment planning. Physics in Medicine & Biology. 2010 Oct 20; 55(22):6601.
4. Mohsen H, El-Dahshan ES, El-Horbaty ES, Salem AB. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal. 2018 Jun 1; 3(1):68-71.
5. https://www.mrmed.in/health-library/cancer-care/world-brain-tumor-day-2023
6. Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one. 2015 Oct 8; 10(10):e0140381.
7. Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems. 2021 Nov 8:1-23.
8. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR. Brain tumor classification using convolutional neural network. InWorld Congress on Medical Physics and Biomedical Engineering 2018: June 3-8, 2018, Prague, Czech Republic (Vol. 1) 2019 (pp. 183-189). Springer Singapore.\
9. Cancer Treatments Centers of America—Brain Cancer Types. Available online: https://www.cancercenter. com/cancer-types/brain-cancer/types (accessed on 30 November 2019
10. Abir TA, Siraji JA, Ahmed E, Khulna B. Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol. 2018; 4(8):65-79.
11. Satou M, Wang J, Nakano-Tateno T, Teramachi M, Suzuki T, Hayashi K, Lamothe S, Hao Y, Kurata H, Sugimoto H, Chik C. L-type amino acid transporter 1, LAT1, in growth hormone-producing pituitary tumor cells. Molecular and Cellular Endocrinology. 2020 Sep 15; 515:110868. [Cross reference]
12. Ismael MR, Abdel-Qader I. Brain tumor classification via statistical features and back-propagation neural network. In2018 IEEE international conference on electro/information technology (EIT) 2018 May 3 (pp. 0252-0257). IEEE.
13. Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications. 2020 Feb; 32:839-54.
14. Byrne DM, Dwivedi R, Minks D. Recommendations for cross-sectional imaging in cancer management. The Royal College of Radiologists: London, UK. 2014.
15. Núñez-Martín R, Cervera RC, Pulla MP. Gastrointestinal stromal tumour and second tumours: A literature review. Medicina Clínica (English Edition). 2017 Oct 23;149(8):345-50.
16. Schlemmer HP, Bachert P, Henze M, Buslei R, Herfarth K, Debus J, Van Kaick G. Differentiation of radiation necrosis from tumor progression using proton magnetic resonance spectroscopy. Neuroradiology. 2002 Mar;44:216-22.
17. Håberg A, Kvistad KA, Unsgård G, Haraldseth O. Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery. 2004 Apr 1;54(4):902-15.
18. Ulmer JL, Krouwer HG, Mueller WM, Ugurel MS, Kocak M, Mark LP. Pseudo-reorganization of language cortical function at fMR imaging: a consequence of tumor-induced neurovascular uncoupling. American journal of neuroradiology. 2003 Feb 1;24(2):213-7.
19. Trinh VT, Fahim DK, Maldaun MV, Shah K, McCutcheon IE, Rao G, Lang F, Weinberg J, Sawaya R, Suki D, Prabhu SS. Impact of preoperative functional magnetic resonance imaging during awake craniotomy procedures for intraoperative guidance and complication avoidance. Stereotactic and functional neurosurgery. 2014 Oct 1;92(5):315-22.
20. Nadkarni TN, Andreoli MJ, Nair VA, Yin P, Young BM, Kundu B, Pankratz J, Radtke A, Holdsworth R, Kuo JS, Field AS. Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: task and statistical threshold effects on language lateralization. NeuroImage: Clinical. 2015 Jan 1;7:415-23.
21. Morrison MA, Churchill NW, Cusimano MD, Schweizer TA, Das S, Graham SJ. Reliability of task-based fMRI for preoperative planning: a test-retest study in brain tumor patients and healthy controls. PLoS One. 2016 Feb 19;11(2):e0149547.
22. Yan PF, Yan L, Zhang Z, Salim A, Wang L, Hu TT, Zhao HY. Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases. International Journal of Surgery. 2016 Dec 1;36:109-17.
23. D’Andrea G, Trillo’ G, Picotti V, Raco A. Functional magnetic resonance imaging (fMRI), pre-intraoperative tractography in neurosurgery: the experience of Sant’Andrea Rome University Hospital. Trends in Reconstructive Neurosurgery: Neurorehabilitation, Restoration and Reconstruction. 2017:241-50.
24. Kosteniuk SE, Gui C, Gariscsak PJ, Lau JC, Megyesi JF. Impact of functional magnetic resonance imaging on clinical outcomes in a propensity-matched low grade glioma cohort. World Neurosurgery. 2018 Dec 1;120:e1143-8.
25. Yan PF, Yan L, Hu TT, Xiao DD, Zhang Z, Zhao HY, Feng J. The potential value of preoperative MRI texture and shape analysis in grading meningiomas: a preliminary investigation. Translational oncology. 2017 Aug 1;10(4):570-7.
26. Gunal V, Savardekar AR, Devi BI, Bharath RD. Preoperative functional magnetic resonance imaging in patients undergoing surgery for tumors around left (dominant) inferior frontal gyrus region. Surgical Neurology International. 2018;9.
27. Vysotski S, Madura C, Swan BE, Holdsworth RY, Lin YU, Del Rio AM, Wood JO, Kundu B, Penwarden A, Voss J, Gallagher T. Preoperative FMRI associated with decreased mortality and morbidity in brain tumor patients. Interdisciplinary Neurosurgery. 2018 Sep 1;13:40-5.
28. Arita K, Miwa M, Bohara M, Moinuddin FM, Kamimura K, Yoshimoto K. Precision of preoperative diagnosis in patients with brain tumor–A prospective study based on “top three list” of differential diagnosis for 1061 patients. Surgical Neurology International. 2020;11.
29. Rijnen S, Butterbrod E, Rutten G, Sitskoorn M, Gehring K. Presurgical Identification of Patients With Glioblastoma at Risk for Cognitive Impairment at 3-Month Follow-up 2020 Dec; 87(6): 1119–1129.
30. Jiang J, Yu J, Liu X, Deng K, Zhuang K, Lin F, Luo L. The efficacy of preoperative MRI features in the diagnosis of meningioma WHO grade and brain invasion. Frontiers in Oncology. 2023 Jan 18;12:1100350.
31. Bertani G, Fava E, Casaceli G, Carrabba G, Casarotti A, Papagno C, Castellano A, Falini A, Gaini SM, Bello L. Intraoperative mapping and monitoring of brain functions for the resection of low-grade gliomas: technical considerations. Neurosurgical focus. 2009 Oct 1;27(4):E4.
32. Weller P, Wittsack HJ, Siebler M, Hömberg V, Seitz RJ. Motor recovery as assessed with isometric finger movements and perfusion magnetic resonance imaging after acute ischemic stroke. Neurorehabilitation and Neural Repair. 2006 Sep;20(3):390-7.[cross ref]
33. Haberg A, Kvistad KA, Unsgard G, Haraldseth O. Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery. 2004 Apr 1;54(4):902-15.
34. Giussani C, Roux FE, Ojemann J, Sganzerla EP, Pirillo D, Papagno C. Is preoperative functional magnetic resonance imaging reliable for language areas mapping in brain tumor surgery? Review of language functional magnetic resonance imaging and direct cortical stimulation correlation studies. Neurosurgery. 2010 Jan 1;66(1):113-20.
35. Kapsalakis IZ, Kapsalaki EZ, Gotsis ED, Verganelakis D, Toulas P, Hadjigeorgiou G, Chung I, Fezoulidis I, Papadimitriou A, Robinson JS, Lee GP. Preoperative evaluation with FMRI of patients with intracranial gliomas. Radiology research and practice. 2012 Oct;2012.
36. Stippich C, Blatow M, Alzamora MG. Task-based presurgical functional MRI in patients with brain tumors. InClinical Functional MRI: Presurgical Functional Neuroimaging 2021 Dec 4 (pp. 121-195). Cham: Springer International Publishing.
37. Watanabe Y, Yamasaki F, Kajiwara Y, Takayasu T, Nosaka R, Akiyama Y, Sugiyama K, Kurisu K. Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. European journal of radiology. 2013 Apr 1; 82(4):658-63.
38. Prada F, Del Bene M, Mattei L, Lodigiani L, DeBeni S, Kolev V, Vetrano I, Solbiati L, Sakas G, DiMeco F. Preoperative magnetic resonance and intraoperative ultrasound fusion imaging for real-time neuronavigation in brain tumor surgery. Ultraschall in der Medizin-European Journal of Ultrasound. 2014 Nov 27:174-86.
39. Amiez C, Kostopoulos P, Champod AS, Collins DL, Doyon J, Del Maestro R, Petrides M. Preoperative functional magnetic resonance imaging assessment of higher-order cognitive function in patients undergoing surgery for brain tumors. Journal of neurosurgery. 2008 Feb 1; 108(2):258-68.
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Nov 21, 2023
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Dr. Prashant Khade, Dr. Sandhya Kothari, & Dr. Akshay Chauhan. (2023). EVOLUTION OF ROLE OF PRE-OPERATIVE MAGNETIC RESONANCE IMAGING IN PLANNING BRAIN TUMOUR SURGERIES: A SYSTEMATIC REVIEW. Journal of Population Therapeutics and Clinical Pharmacology, 30(18), 3149-3158. https://doi.org/10.53555/d555wh08
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Author Biographies
Dr. Prashant Khade
Associate Professor at Dept. Of Radiology, Dr. Panjabrao Deshmukh Memorial Medical College, Amravati
Dr. Sandhya Kothari
Professor& HOD at Dept. Of Radiology, Dr. Panjabrao Deshmukh Memorial Medical College, Amravati
Dr. Akshay Chauhan
Resident at Dept. Of Radiology, Dr. Panjabrao Deshmukh Memorial Medical College, Amravati
How to Cite
Dr. Prashant Khade, Dr. Sandhya Kothari, & Dr. Akshay Chauhan. (2023). EVOLUTION OF ROLE OF PRE-OPERATIVE MAGNETIC RESONANCE IMAGING IN PLANNING BRAIN TUMOUR SURGERIES: A SYSTEMATIC REVIEW. Journal of Population Therapeutics and Clinical Pharmacology, 30(18), 3149-3158. https://doi.org/10.53555/d555wh08