Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art

Moamin Jameel; Muslim Jameel; Soner Şahin

Review

Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art

Year 2020, Volume: 2 Issue: 2, 143 - 147, 31.12.2020

Moamin Jameel , Muslim Jameel , Soner Şahin

Abstract

Radiomics is nascent field that involves extracting quantitative features - radiomic features from medical images that corrolate with the properties of the concerned lesion, such as the heterogeneity, shape, volume, proteomic, demographic and histology that have personalizied clinical value in diagnosis, prognosis and treatment. Our article aims to explore and review the state-of-art Radiomics and texture analysing in neuro-oncology, address the challenges a provide a prospect for future studies.

Keywords

radiomics, neurosurgery, oncology

References

1. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G. Introduction to Radiomics. J Nucl Med. 2020 Apr;61(4):488-495. doi: 10.2967/jnumed.118.222893. Epub 2020 Feb 14. PMID: 32060219.
2. Liao X, Cai B, Tian B, Luo Y, Song W, Li Y. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time. J Cell Mol Med. 2019 Jun;23(6):4375-4385. doi: 10.1111/jcmm.14328. Epub 2019 Apr 18. PMID: 31001929; PMCID: PMC6533509
3. Hidetaka Arimura, Mazen Soufi, Hidemi Kamezawa, Kenta Ninomiya, Masahiro Yamada, Radiomics with artificial intelligence for precision medicine in radiation therapy, Journal of Radiation Research, Volume 60, Issue 1, January 2019, Pages 150–157
4. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017 Sep;10(3):257-273. doi: 10.1007/s12194-017-0406-5. Epub 2017 Jul 8. PMID: 28689314 5. Tselikas L, Sun R, Ammari S, Dercle L, Yevich S, Hollebecque A, Ngo-Camus M, Nicotra C, Deutsch E, Deschamps F, de Baere T. Role of image-guided biopsy and radiomics in the age of precision medicine. Chin Clin Oncol. 2019 Dec;8(6):57. doi: 10.21037/cco.2019.12.02. PMID: 31968981
6. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017 Dec;14(12):749-762. doi: 10.1038/nrclinonc.2017.141. Epub 2017 Oct 4. PMID: 28975929.
7. Mirestean CC, Pagute O, Buzea C, Iancu RI, Iancu DT. Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019 Jun;14(2):126-130. doi: 10.26574/maedica.2019.14.2.126. PMID: 31523292; PMCID: PMC6709390.
8. Park JE, Kickingereder P, Kim HS. Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging. Korean J Radiol. 2020;21(10):1126-1137. doi:10.3348/kjr.2019.0847
9. Leng Y, Wang X, Liao W, Cao Y. Radiomics in gliomas: A promising assistance  for glioma clinical research. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2018 Apr 28;43(4):354-359. doi: 10.11817/j.issn.1672-7347.2018.04.004. PMID: 29774870.
10. Mouthuy N, Cosnard G, Abarca-Quinones J, Michoux N (2012) Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases. J Neuroradiol 39: 301–307
11. Tian Q, Yan LF, Zhang X, Zhang X, Hu YC, Han Y, Liu ZC, Nan HY, Sun Q, Sun YZ, Yang Y, Yu Y, Zhang J, Hu B, Xiao G, Chen P, Tian S, Xu J, Wang W, Cui GB. Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging. 2018 Dec;48(6):1518-1528. doi: 10.1002/jmri.26010. Epub 2018 Mar 23. PMID: 29573085.
12. Skogen K., Schulz A., Dormagen J.B., Ganeshan B., Helseth E., Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur. J. Radiol. 2016;85:824–829. doi: 10.1016/j.ejrad.2016.01.013. - DOI - PubMed
13. Ditmer A, Zhang B, Shujaat T, et al. Diagnostic accuracy of MRI texture analysis for grading gliomas. Journal of Neuro-oncology. 2018 Dec;140(3):583-589. DOI: 10.1007/s11060-018-2984-4
14. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016 Jun;131(6):803-20. doi: 10.1007/s00401-016-1545-1. Epub 2016 May 9. PMID: 27157931.
15. VIAL, A., STIRLING, D., FIELD, M., ROS, M., RITZ, C., CAROLAN, M., HOLLOWAY, L., MILLER, A.. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research, North America, 7, jun. 2018. Available at: <http://tcr.amegroups.com/article/view/21823>. Date accessed: 26 Dec. 2020
16. Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology. 2019 Mar;290(3):607-618. doi: 10.1148/radiol.2018181928. Epub 2019 Jan 22. PMID: 30667332; PMCID: PMC6389268.
17. Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol. 2017 Jan;19(1):128-137. doi: 10.1093/neuonc/now135. Epub 2016 Aug 8. PMID: 27502248; PMCID: PMC5193022
18. Hong EK, Choi SH, Shin DJ, Jo SW, Yoo RE, Kang KM, Yun TJ, Kim JH, Sohn CH, Park SH, Won JK, Kim TM, Park CK, Kim IH, Lee ST. Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma. Eur Radiol. 2018 Oct;28(10):4350-4361. doi: 10.1007/s00330-018-5400-8. Epub 2018 May 2. PMID: 29721688.
19. Mazurowski, M.A., Clark, K., Czarnek, N.M. et al. Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data. J Neurooncol 133, 27–35 (2017).

Nöro-Onkolojide Radyomik Kullanımı: Son Teknolojinin Kısa İncelemesi

Year 2020, Volume: 2 Issue: 2, 143 - 147, 31.12.2020

Moamin Jameel , Muslim Jameel , Soner Şahin

Abstract

Radyomik, tanı, prognoz ve tedavide kişiselleştirilmiş klinik değere sahip heterojenlik, şekil, hacim, proteomik, demografik ve histoloji gibi ilgili lezyonun özellikleriyle uyumlu tıbbi görüntülerden radyomik özellikler olan nicel özelliklerin çıkarılmasını içeren yeni bir alandır. Makalemiz nöro-onkolojide son teknoloji Radyomikleri ve doku analizini keşfetmeyi ve gözden geçirmeyi, zorlukları ele almayı ve gelecekteki çalışmalar için bir umut sunmayı amaçlamaktadır.

Keywords

radyomik, beyin cerrahisi, onkoloji

References

1. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G. Introduction to Radiomics. J Nucl Med. 2020 Apr;61(4):488-495. doi: 10.2967/jnumed.118.222893. Epub 2020 Feb 14. PMID: 32060219.
2. Liao X, Cai B, Tian B, Luo Y, Song W, Li Y. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time. J Cell Mol Med. 2019 Jun;23(6):4375-4385. doi: 10.1111/jcmm.14328. Epub 2019 Apr 18. PMID: 31001929; PMCID: PMC6533509
3. Hidetaka Arimura, Mazen Soufi, Hidemi Kamezawa, Kenta Ninomiya, Masahiro Yamada, Radiomics with artificial intelligence for precision medicine in radiation therapy, Journal of Radiation Research, Volume 60, Issue 1, January 2019, Pages 150–157
4. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017 Sep;10(3):257-273. doi: 10.1007/s12194-017-0406-5. Epub 2017 Jul 8. PMID: 28689314 5. Tselikas L, Sun R, Ammari S, Dercle L, Yevich S, Hollebecque A, Ngo-Camus M, Nicotra C, Deutsch E, Deschamps F, de Baere T. Role of image-guided biopsy and radiomics in the age of precision medicine. Chin Clin Oncol. 2019 Dec;8(6):57. doi: 10.21037/cco.2019.12.02. PMID: 31968981
6. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017 Dec;14(12):749-762. doi: 10.1038/nrclinonc.2017.141. Epub 2017 Oct 4. PMID: 28975929.
7. Mirestean CC, Pagute O, Buzea C, Iancu RI, Iancu DT. Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019 Jun;14(2):126-130. doi: 10.26574/maedica.2019.14.2.126. PMID: 31523292; PMCID: PMC6709390.
8. Park JE, Kickingereder P, Kim HS. Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging. Korean J Radiol. 2020;21(10):1126-1137. doi:10.3348/kjr.2019.0847
9. Leng Y, Wang X, Liao W, Cao Y. Radiomics in gliomas: A promising assistance  for glioma clinical research. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2018 Apr 28;43(4):354-359. doi: 10.11817/j.issn.1672-7347.2018.04.004. PMID: 29774870.
10. Mouthuy N, Cosnard G, Abarca-Quinones J, Michoux N (2012) Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases. J Neuroradiol 39: 301–307
11. Tian Q, Yan LF, Zhang X, Zhang X, Hu YC, Han Y, Liu ZC, Nan HY, Sun Q, Sun YZ, Yang Y, Yu Y, Zhang J, Hu B, Xiao G, Chen P, Tian S, Xu J, Wang W, Cui GB. Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging. 2018 Dec;48(6):1518-1528. doi: 10.1002/jmri.26010. Epub 2018 Mar 23. PMID: 29573085.
12. Skogen K., Schulz A., Dormagen J.B., Ganeshan B., Helseth E., Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur. J. Radiol. 2016;85:824–829. doi: 10.1016/j.ejrad.2016.01.013. - DOI - PubMed
13. Ditmer A, Zhang B, Shujaat T, et al. Diagnostic accuracy of MRI texture analysis for grading gliomas. Journal of Neuro-oncology. 2018 Dec;140(3):583-589. DOI: 10.1007/s11060-018-2984-4
14. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016 Jun;131(6):803-20. doi: 10.1007/s00401-016-1545-1. Epub 2016 May 9. PMID: 27157931.
15. VIAL, A., STIRLING, D., FIELD, M., ROS, M., RITZ, C., CAROLAN, M., HOLLOWAY, L., MILLER, A.. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research, North America, 7, jun. 2018. Available at: <http://tcr.amegroups.com/article/view/21823>. Date accessed: 26 Dec. 2020
16. Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology. 2019 Mar;290(3):607-618. doi: 10.1148/radiol.2018181928. Epub 2019 Jan 22. PMID: 30667332; PMCID: PMC6389268.
17. Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol. 2017 Jan;19(1):128-137. doi: 10.1093/neuonc/now135. Epub 2016 Aug 8. PMID: 27502248; PMCID: PMC5193022
18. Hong EK, Choi SH, Shin DJ, Jo SW, Yoo RE, Kang KM, Yun TJ, Kim JH, Sohn CH, Park SH, Won JK, Kim TM, Park CK, Kim IH, Lee ST. Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma. Eur Radiol. 2018 Oct;28(10):4350-4361. doi: 10.1007/s00330-018-5400-8. Epub 2018 May 2. PMID: 29721688.
19. Mazurowski, M.A., Clark, K., Czarnek, N.M. et al. Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data. J Neurooncol 133, 27–35 (2017).

There are 18 citations in total.

Details

Primary Language	English
Subjects	Biomedical Engineering
Journal Section	Review
Authors	Moamin Jameel 0000-0001-8934-5803 Muslim Jameel 0000-0001-8397-1280 Soner Şahin 0000-0001-9391-8088
Publication Date	December 31, 2020
Published in Issue	Year 2020 Volume: 2 Issue: 2

Cite

APA	Jameel, M., Jameel, M., & Şahin, S. (2020). Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art. Journal of Medical Innovation and Technology, 2(2), 143-147.
AMA	Jameel M, Jameel M, Şahin S. Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art. Journal of Medical Innovation and Technology. December 2020;2(2):143-147.
Chicago	Jameel, Moamin, Muslim Jameel, and Soner Şahin. “Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-Art”. Journal of Medical Innovation and Technology 2, no. 2 (December 2020): 143-47.
EndNote	Jameel M, Jameel M, Şahin S (December 1, 2020) Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art. Journal of Medical Innovation and Technology 2 2 143–147.
IEEE	M. Jameel, M. Jameel, and S. Şahin, “Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art”, Journal of Medical Innovation and Technology, vol. 2, no. 2, pp. 143–147, 2020.
ISNAD	Jameel, Moamin et al. “Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-Art”. Journal of Medical Innovation and Technology 2/2 (December 2020), 143-147.
JAMA	Jameel M, Jameel M, Şahin S. Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art. Journal of Medical Innovation and Technology. 2020;2:143–147.
MLA	Jameel, Moamin et al. “Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-Art”. Journal of Medical Innovation and Technology, vol. 2, no. 2, 2020, pp. 143-7.
Vancouver	Jameel M, Jameel M, Şahin S. Radiomics Utilization in Neuro-Oncology: Brief Review of the State-of-art. Journal of Medical Innovation and Technology. 2020;2(2):143-7.

Article Files

Full Text