Artificial Intelligence in Neuro-Oncology: predicting molecular markers and response to therapy.
Main Article Content
Abstract
Artificial intelligence’s capability to analyze and interpret complex data is transforming neuro-oncology by enhancing the precision of diagnosis and enabling personalized treatment plans. Particularly, applications in radiogenomics are instrumental in identifying molecular markers from imaging data, potentially reducing the need for invasive procedures and accelerating molecular diagnostics. This review discusses various artificial intelligence methodologies, from machine learning to deep learning, mentioning a number of their current use cases and the challenges faced in clinical integration. In addition, future directions, such as multimodal data integration and the need to address technical and ethical implications, are highlighted.
Keywords:
Radiogenomic, Neuro-oncology, Artificial Intelligence, Treatment
Article Details
How to Cite
MOASSEFI, Mana; FAGHANI, Shahriar; ERICKSON, Bradley J..
Artificial Intelligence in Neuro-Oncology: predicting molecular markers and response to therapy..
Medical Research Archives, [S.l.], v. 12, n. 6, june 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5523>. Date accessed: 23 nov. 2024.
doi: https://doi.org/10.18103/mra.v12i6.5523.
Section
Review Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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[30] Miller DD, Brown EW. How Cognitive Machines Can Augment Medical Imaging. AJR Am J Roentgenol. 2019;212(1):9-14.
[31] de Godoy LL, Chawla S, Brem S, Mohan S. Taming Glioblastoma in “Real Time”: Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology. Clin Cancer Res. 2023;29(14):2588-2592.
[32] Taylor C, Ekert JO, Sefcikova V, Fersht N, Samandouras G. Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis. Sci Rep. 2022;12(1):13258.
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[38] Prezelski K, Hsu DG, Del Balzo L, Heller E, Ma J, Pike LRG, et al. Artificial-intelligence-driven measurements of brain metastases’ response to SRS compare favorably with current manual standards of assessment. Neurooncol Adv. 2024;6(1):vdae015.
[39] Faghani S, Khosravi B, Zhang K, Moassefi M, Jagtap JM, Nugen F, et al. Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics. Radiology: Artificial Intelligence. Published online August 24, 2022. doi:10.1148/ryai.220061
[40] Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV, Singh Y, et al. Mitigating Bias in Radiology Machine Learning: 1. Data Handling. Radiology: Artificial Intelligence. Published online August 24, 2022. doi:10.1148/ryai.210290
[41] Moassefi M, Rouzrokh P, Conte GM, Vahdati S, Fu T, Tahmasebi A, et al. Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review. J Digit Imaging. 2023;36 (5):2306-2312.
[42] Stupple A, Singerman D, Celi LA. The reproducibility crisis in the age of digital medicine. npj Digital Medicine. 2019;2(1):1-3.
[43] Baker M. 1,500 scientists lift the lid on reproducibility. Nature Publishing Group UK. doi:10.1038/533452a
[44] Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology Artificial intelligence. 2020;2(2). doi:10.1148/ryai.2020200029
[45] Moassefi M, Singh Y, Conte GM, Khosravi B, Rouzrokh P, Vahdati S, et al. Checklist for Reproducibility of Deep Learning in Medical Imaging. Journal of Imaging Informatics in Medicine. Published online March 14, 2024:1-10.
[46] Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26(9):1364-1374.
[47] Tejani AS, Ng YS, Xi Y, Rayan JC. Understanding and Mitigating Bias in Imaging Artificial Intelligence. Radiographics. 2024;44( 5). doi:10.1148/rg.230067
[48] Samartha MVS, Dubey NK, Jena B, Maheswar G, Lo WC, Saxena S. AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis. J Cancer Res Clin Oncol. 2024;150(2):1-22.
[49] Moassefi M, Faghani S, Khanipour Roshan S, Conte GM, Rassoulinejad Mousavi SM, Kaufmann TJ, et al. Exploring the Impact of 3D Fast Spin Echo and Inversion Recovery Gradient Echo Sequences Magnetic Resonance Imaging Acquisition on Automated Brain Tumor Segmentation. Mayo Clinic Proceedings: Digital Health. 2024;2(2):231-240.
[50] Ellingson BM, Brown MS, Boxerman JL, Gerstner ER, Kaufmann TJ, Cole PE, et al. Radiographic read paradigms and the roles of the central imaging laboratory in neuro-oncology clinical trials. Neuro Oncol. 2021;23(2):189-198.
[2] Philip AK, Samuel BA, Bhatia S, Khalifa SAM, El-Seedi HR. Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors. Life. 2022;13(1). doi:10.3390/life13010024
[3] He A, Wang P, Zhu A, Liu Y, Chen J, Liu L. Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging. Diagnostics (Basel). 2022 ;12(12). doi:10.3390/diagnostics12122995
[4] Omuro A, Brandes AA, Carpentier AF, Idbaih A, Reardon DA, Cloughesy T, et al. Radiotherapy combined with nivolumab or temozolomide for newly diagnosed glioblastoma with unmethylated MGMT promoter: An international randomized phase III trial. Neuro Oncol. 2023;25(1):123-134.
[5] Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10): 997-1003.
[6] Moassefi M, Faghani S, Conte GM, Kowalchuk RO, Vahdati S, Crompton DJ, et al. A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients. J Neurooncol. 2022; 159(2):447-455.
[7] Staedtke V, Dzaye OD a., Holdhoff M. Actionable Molecular Biomarkers in Primary Brain Tumors. Trends Cancer Res. 2016;2(7): 338-349.
[8] Mahajan A, Sahu A, Ashtekar R, Kulkarni T, Shukla S, Agarwal U, et al. Glioma radiogenomics and artificial intelligence: road to precision cancer medicine. Clin Radiol. 2023;78(2):137-149.
[9] Moassefi M, Faghani S, Khosravi B, Rouzrokh P, Erickson BJ. Artificial Intelligence in Radiology: Overview of Application Types, Design, and Challenges. Semin Roentgenol. 2023;58(2):170-177.
[10] Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510.
[11] Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology. 2019;290(3):607-618.
[12] Weller M, van den Bent M, Preusser M, Le Rhun E, Tonn JC, Minniti G, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2021;18(3):170-186.
[13] Mirchia K, Richardson TE. Beyond IDH-Mutation: Emerging Molecular Diagnostic and Prognostic Features in Adult Diffuse Gliomas. Cancers . 2020;12(7). doi:10.3390/ca ncers12071817
[14] Capper D, Weissert S, Balss J, Habel A, Meyer J, Jäger D, et al. Characterization of R132H mutation-specific IDH1 antibody binding in brain tumors. Brain Pathol. 2010;20(1):245-254.
[15] Balss J, Meyer J, Mueller W, Korshunov A, Hartmann C, von Deimling A. Analysis of the IDH1 codon 132 mutation in brain tumors. Acta Neuropathol. 2008;116(6):597-602.
[16] Urbanovska I, Megova MH, Dwight Z, Kalita O, Uvirova M, Simova J, et al. IDH Mutation Analysis in Glioma Patients by CADMA Compared with SNaPshot Assay and two Immunohistochemical Methods. Pathol Oncol Res. 2019;25(3):971-978.
[17] Polivka J, Polivka J Jr, Rohan V, Pesta M, Repik T, Pitule P, et al. Isocitrate dehydrogenase-1 mutations as prognostic biomarker in glioblastoma multiforme patients in West Bohemia. Biomed Res Int. 2014;2014:735659.
[18] Santinha J, Katsaros V, Stranjalis G, Liouta E, Boskos C, Matos C, et al. Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. J Imaging Inform Med. 2024;37(1):31-44.
[19] Di Salle G, Tumminello L, Laino ME, Shalaby S, Aghakhanyan G, Fanni SC, et al. Accuracy of Radiomics in Predicting IDH Mutation Status in Diffuse Gliomas: A Bivariate Meta-Analysis. Radiol Artif Intell. 2024;6(1):e220257.
[20] Rathore S, Mohan S, Bakas S, Sako C, Badve C, Pati S, et al. Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk). Neurooncol Adv. 2020;2(Suppl 4):iv2 2-iv34.
[21] Cluceru J, Interian Y, Phillips JJ, Molinaro AM, Luks TL, Alcaide-Leon P, et al. Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging. Neuro Oncol. 2022;24(4). doi:10.1093/neuonc/noab238
[22] Dasgupta A, Gupta T. Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning? AJNR Am J Neuroradiol. 2019;40(5):E24-E25.
[23] Cavalli FMG, Remke M, Rampasek L, Peacock J, Shih DJH, Luu B, et al. Intertumoral Heterogeneity within Medulloblastoma Subgroups. Cancer Cell. 2017;31(6):737-754.e6.
[24] Zhang M, Wong SW, Wright JN, Wagner MW, Toescu S, Han M, et al. MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study. Radiology. 2022;304 (2):406-416.
[25] Brain Tumor Segmentation (BraTS) Challenge. https://www.med.upenn.edu/cbica/brats/. Accessed April 22, 2024
[26] Maraka S, Janku F. BRAF Alterations in Primary Brain Tumors. Discov Med. 2018;26(141):51-60.
[27] Han Y, Xie Z, Zang Y, Zhang S, Gu D, Zhou M, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol. 2018;140(2). doi:10.1 007/s11060-018-2953-y
[28] Tandel G S, Biswas M, Kakde O G, Tiwari A, Suri H S, Turk M, et al. A Review on a Deep Learning Perspective in Brain Cancer Classification. Cancers . 2019;11(1). doi:10.33 90/cancers11010111
[29] Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci. 2021;14(1):86-93.
[30] Miller DD, Brown EW. How Cognitive Machines Can Augment Medical Imaging. AJR Am J Roentgenol. 2019;212(1):9-14.
[31] de Godoy LL, Chawla S, Brem S, Mohan S. Taming Glioblastoma in “Real Time”: Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology. Clin Cancer Res. 2023;29(14):2588-2592.
[32] Taylor C, Ekert JO, Sefcikova V, Fersht N, Samandouras G. Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis. Sci Rep. 2022;12(1):13258.
[33] Liu X, Chan MD, Zhou X, Qian X. Transparency guided ensemble convolutional neural networks for stratification of pseudoprogression and true progression of glioblastoma multiform. arXiv [q-bioTO]. Published online February 26, 2019. http://arxiv.org/abs/1902.09921
[34] Li M, Tang H, Chan MD, Zhou X, Qian X. DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet. Med Phys. 2020;47(3):1139-1150.
[35] Kebir S, Schmidt T, Weber M, Lazaridis L, Galldiks N, Langen KJ, et al. A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma. Cancers . 2020;12(11). doi:10.3390/cancers12113080
[36] Lee J, Wang N, Turk S, Mohammed S, Lobo R, Kim J, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning. Sci Rep. 2020;10(1):20331.
[37] Du P, Liu X, Shen L, Wu X, Chen J, Chen L, et al. Prediction of treatment response in patients with brain metastasis receiving stereotactic radiosurgery based on pre-treatment multimodal MRI radiomics and clinical risk factors: A machine learning model. Front Oncol. 2023;13:1114194.
[38] Prezelski K, Hsu DG, Del Balzo L, Heller E, Ma J, Pike LRG, et al. Artificial-intelligence-driven measurements of brain metastases’ response to SRS compare favorably with current manual standards of assessment. Neurooncol Adv. 2024;6(1):vdae015.
[39] Faghani S, Khosravi B, Zhang K, Moassefi M, Jagtap JM, Nugen F, et al. Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics. Radiology: Artificial Intelligence. Published online August 24, 2022. doi:10.1148/ryai.220061
[40] Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV, Singh Y, et al. Mitigating Bias in Radiology Machine Learning: 1. Data Handling. Radiology: Artificial Intelligence. Published online August 24, 2022. doi:10.1148/ryai.210290
[41] Moassefi M, Rouzrokh P, Conte GM, Vahdati S, Fu T, Tahmasebi A, et al. Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review. J Digit Imaging. 2023;36 (5):2306-2312.
[42] Stupple A, Singerman D, Celi LA. The reproducibility crisis in the age of digital medicine. npj Digital Medicine. 2019;2(1):1-3.
[43] Baker M. 1,500 scientists lift the lid on reproducibility. Nature Publishing Group UK. doi:10.1038/533452a
[44] Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology Artificial intelligence. 2020;2(2). doi:10.1148/ryai.2020200029
[45] Moassefi M, Singh Y, Conte GM, Khosravi B, Rouzrokh P, Vahdati S, et al. Checklist for Reproducibility of Deep Learning in Medical Imaging. Journal of Imaging Informatics in Medicine. Published online March 14, 2024:1-10.
[46] Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26(9):1364-1374.
[47] Tejani AS, Ng YS, Xi Y, Rayan JC. Understanding and Mitigating Bias in Imaging Artificial Intelligence. Radiographics. 2024;44( 5). doi:10.1148/rg.230067
[48] Samartha MVS, Dubey NK, Jena B, Maheswar G, Lo WC, Saxena S. AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis. J Cancer Res Clin Oncol. 2024;150(2):1-22.
[49] Moassefi M, Faghani S, Khanipour Roshan S, Conte GM, Rassoulinejad Mousavi SM, Kaufmann TJ, et al. Exploring the Impact of 3D Fast Spin Echo and Inversion Recovery Gradient Echo Sequences Magnetic Resonance Imaging Acquisition on Automated Brain Tumor Segmentation. Mayo Clinic Proceedings: Digital Health. 2024;2(2):231-240.
[50] Ellingson BM, Brown MS, Boxerman JL, Gerstner ER, Kaufmann TJ, Cole PE, et al. Radiographic read paradigms and the roles of the central imaging laboratory in neuro-oncology clinical trials. Neuro Oncol. 2021;23(2):189-198.