Using Artificial Intelligence and Magnetic Resonance Imaging to Address Limitations in Response Assessment in Glioma


Artificial Intelligence

How to Cite

Krauze, A. (2022). Using Artificial Intelligence and Magnetic Resonance Imaging to Address Limitations in Response Assessment in Glioma. Oncology Insights, 2022. Retrieved from


Gliomas are rapidly progressive, neurologically devastating, nearly uniformly fatal brain tumors. In WHO grade IV tumors like glioblastoma, the standard of care involves maximal surgical resection followed by concurrent radiation therapy (RT) and temozolomide (TMZ) chemotherapy followed by adjuvant TMZ. This results in overall survival (OS) of less than 30% at two years. Currently, tumor progression assessment is based on clinician assessment and MRI interpretation using Response Assessment in Neuro-Oncology (RANO) criteria. These criteria classify response as complete, partial, stable, or progression. This approach, however, suffers from significant limitations due to the difficulty in interpreting MRI findings on T1 gad and T2 FLAIR sequences, lack of concurrent correlation with radiation therapy fields, inconsistent follow-up imaging, concurrent administration of steroids, and systemic management, including immunotherapy. The neuro-oncology field struggles with classifying true progression vs. pseudoprogression vs. pseudoresponse with progression guidelines actively evolving. The lack of consensus on the definition of progression impairs the ability to initiate earlier management upon progression, judge the impact of therapies, and optimize and personalize management. Due to the pivotal role of imaging, radiology is at the center of the question of optimizing and advancing response criteria [1-5]. The hypothesis is that MRI images of patients with glioma, when subjected to change over time analysis (at diagnosis, prior to and post-radiation therapy), can identify features predictive of treatment failure helping guide patient management in the clinic. Likely a combination of imaging and biospecimen-driven biomarkers is needed. Given the large amount of data generated by both approaches, success in this space hinges on leveraging computational approaches and artificial intelligence algorithms validated using large-scale publicly available data sets to disentangle the complexity and heterogeneity inherent in glioma progression.



Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2021;19:132–46.

Abdalla G, Hammam A, Anjari M, D’Arco DrF, Bisdas DrS. Glioma surveillance imaging: current strategies, shortcomings, challenges and outlook. BJR Open. 2020;2(1):20200009.

3Leao DJ, Craig PG, Godoy LF, Leite CC, Policeni B. Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques. Am J Neuroradiol. 2019;41(1):10–20.

Ellingson BM, Sampson J, Achrol AS, Aghi MK, Bankiewicz K, Wang C, et al. Modified RANO, Immunotherapy RANO, and Standard RANO Response to Convection-Enhanced Delivery of IL4R-Targeted Immunotoxin MDNA55 in Recurrent Glioblastoma. Clin Cancer Res. 2021;27(14):3916–25.

Ellingson BM, Wen PY, Cloughesy TF. Modified Criteria for Radiographic Response Assessment in Glioblastoma Clinical Trials. Neurotherapeutics. 2017;14(2):307–20.

6Brennan Cameron W, Verhaak Roel GW, McKenna A, Campos B, Noushmehr H, Salama Sofie R, et al. The Somatic Genomic Landscape of Glioblastoma. Cell. 2013;155(2):462–77.

Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer. 2020;124(4):686–96.

Klughammer J, Kiesel B, Roetzer T, Fortelny N, Nemc A, Nenning K-H, et al. The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nat Med. 2018;24(10):1611–24.

Lombardi G, Barresi V, Castellano A, Tabouret E, Pasqualetti F, Salvalaggio A, et al. Clinical Management of Diffuse Low-Grade Gliomas. Cancers. 2020;12(10):3008.

Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328–38.

Booth CM, Goodman AM. Practicing on the edge of oncology: when standard of care feels uncomfortable. Nat Rev Clin Oncol. 2021;18(11):673–4.

Booth TC, Williams M, Luis A, Cardoso J, Ashkan K, Shuaib H. Machine learning and glioma imaging biomarkers. Clin Radiol. 2020;75(1):20–32.

Johnson DR, Guerin JB, Ruff MW, Fang S, Hunt CH, Morris JM, et al. Glioma response assessment: Classic pitfalls, novel confounders, and emerging imaging tools. Br J Radiol. 2019;92(1094):20180730.

Harrison RA, Ou A, Naqvi SMAA, Naqvi SM, Weathers SS, O’Brien BJ, et al. Aggressiveness of care at end of life in patients with high‐grade glioma. Cancer Med. 2021;10(23):8387–94.

Ko C-C, Yeh L-R, Kuo Y-T, Chen J-H. Imaging biomarkers for evaluating tumor response: RECIST and beyond. Biomark Res. 2021;9(1).

Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47.

Macdonald DR, Cascino TL, Schold SC, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8(7):1277–80.

Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. J Clin Oncol. 2010;28(11):1963–72.

Eisele SC, Wen PY, Lee EQ. Assessment of Brain Tumor Response: RANO and Its Offspring. Curr Treat Options Oncol. 2016;17(7).

Galanis E, Wu W, Cloughesy T, Lamborn K, Mann B, Wen PY, et al. Phase 2 trial design in neuro-oncology revisited: a report from the RANO group. Lancet Oncol. 2012;13(5):e196–204.

Huang RY, Rahman R, Ballman KV, Felten SJ, Anderson SK, Ellingson BM, et al. The Impact of T2/FLAIR Evaluation per RANO Criteria on Response Assessment of Recurrent Glioblastoma Patients Treated with Bevacizumab. Clin Cancer Res. 2015;22(3):575–81.

Chukwueke UN, Wen PY. Use of the Response Assessment in Neuro-Oncology (RANO) criteria in clinical trials and clinical practice. CNS Oncol. 2019;8(1):CNS28.

Bolcaen J, Kleynhans J, Nair S, Verhoeven J, Goethals I, Sathekge M, et al. A perspective on the radiopharmaceutical requirements for imaging and therapy of glioblastoma. Theranostics. 2021;11(16):7911–47.

Le Fèvre C, Lhermitte B, Ahle G, Chambrelant I, Cebula H, Antoni D, et al. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Crit Rev Oncol Hematol. 2021;157:103188.

Patrizz A, Dono A, Zhu P, Tandon N, Ballester LY, Esquenazi Y. Tumor recurrence or treatment-related changes following chemoradiation in patients with glioblastoma: does pathology predict outcomes? J Neurooncol. 2021;152(1):163–72.

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. 2020;126(11):2625–36.

Bronk JK, Guha-Thakurta N, Allen PK, Mahajan A, Grosshans DR, McGovern SL. Analysis of pseudoprogression after proton or photon therapy of 99 patients with low grade and anaplastic glioma. Clin Transl Radiat Oncol. 2018;9:30–4.

Nabors LB, Portnow J, Ahluwalia M, Baehring J, Brem H, Brem S, et al. Central Nervous System Cancers, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2020;18(11):1537–70.

Mellinghoff IK, Penas-Prado M, Peters KB, Burris HA, Maher EA, Janku F, et al. Vorasidenib, a dual inhibitor of mutant IDH1/2, in recurrent or progressive glioma; Results of a first-in-human Phase I trial. Clin Cancer Res. 2021;27(16):4491–9.

van den Bent M, Wefel J, Schiff D, Taphoorn M, Jaeckle K, Junck L, et al. Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol. 2011;12(6):583–93.

Okada H, Weller M, Huang R, Finocchiaro G, Gilbert MR, Wick W, et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol. 2015;16(15):e534–42.

Arvold ND, Armstrong TS, Warren KE, Chang SM, DeAngelis LM, Blakeley J, et al. Corticosteroid use endpoints in neuro-oncology: Response Assessment in Neuro-Oncology Working Group. Neuro Oncol. 2018;20(7):897–906.

Nayak L, DeAngelis LM, Brandes AA, Peereboom DM, Galanis E, Lin NU, et al. The Neurologic Assessment in Neuro-Oncology (NANO) scale: a tool to assess neurologic function for integration into the Response Assessment in Neuro-Oncology (RANO) criteria. Neuro Oncol. 2017;19(5):625–35.

Brothwell MRS, West CM, Dunning AM, Burnet NG, Barnett GC. Radiogenomics in the Era of Advanced Radiotherapy. Clin Oncol. 2019;31(5):319–25.

El Naqa I, Kerns SL, Coates J, Luo Y, Speers C, West CML, et al. Radiogenomics and radiotherapy response modeling. Phys Med Biol. 2017;62(16):R179–206.

El Naqa I, Pandey G, Aerts H, Chien J-T, Andreassen CN, Niemierko A, et al. Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation Int J Radiat Oncol Biol Phys. 2018;102(4):1070–3.

Kerns SL, Chuang K-H, Hall W, Werner Z, Chen Y, Ostrer H, et al. Radiation biology and oncology in the genomic era. Br J Radiol. 2018;91(1091):20170949.

Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol. 2020;196(10):856–67.

Winter SF, Vaios EJ, Muzikansky A, Martinez-Lage M, Bussière MR, Shih HA, et al. Defining Treatment-Related Adverse Effects in Patients with Glioma: Distinctive Features of Pseudoprogression and Treatment-Induced Necrosis. Oncologist. 2020;25(8):e1221–32.

Ritterbusch R, Halasz LM, Graber JJ. Distinct imaging patterns of pseudoprogression in glioma patients following proton versus photon radiation therapy. J Neurooncol. 2021;152(3):583–90.

Kebir S, Khurshid Z, Gaertner FC, Essler M, Hattingen E, Fimmers R, et al. Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma. Oncotarget. 2016;8(5):8294–304.

Galldiks N, Lohmann P, Albert NL, Tonn JC, Langen K-J. Current status of PET imaging in neuro-oncology. Neurooncol Adv. 2019;1(1).

van Dijken BRJ, van Laar PJ, Holtman GA, van der Hoorn A. Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis. Eur Radiol. 2017;27(10):4129–44.

Huang RY, Young RJ, Ellingson BM, Veeraraghavan H, Wang W, Tixier F, et al. Volumetric analysis of IDH-mutant lower-grade glioma: a natural history study of tumor growth rates before and after treatment. Neuro Oncol. 2020;22(12):1822–30.

Hoff BA, Lemasson B, Chenevert TL, Luker GD, Tsien CI, Amouzandeh G, et al. Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma. Acad Radiol. 2021;28(12):1711–20.

Gatson NTN, Bross SP, Odia Y, Mongelluzzo GJ, Hu Y, Lockard L, et al. Early imaging marker of progressing glioblastoma: a window of opportunity. J Neurooncol. 2020;148(3):629–40.

Liu Z-C, Yan L-F, Hu Y-C, Sun Y-Z, Tian Q, Nan H-Y, et al. Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial. BMC Med Imaging. 2017;17(1).

Yu Y, Ma Y, Sun M, Jiang W, Yuan T, Tong D. Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression. Medicine. 2020;99(23):e20270.

Jabehdar Maralani P, Myrehaug S, Mehrabian H, Chan AKM, Wintermark M, Heyn C, et al. Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma. Radiother Oncol. 2021;156:258–65.

Tsakiris C, Siempis T, Alexiou GA, Zikou A, Sioka C, Voulgaris S, et al. Differentiation Between True Tumor Progression of Glioblastoma and Pseudoprogression Using Diffusion-Weighted Imaging and Perfusion-Weighted Imaging: Systematic Review and Meta-analysis. World Neurosurg. 2020;144:e100–9.

Hughes KL, O’Neal CM, Andrews BJ, Westrup AM, Battiste JD, Glenn CA. A systematic review of the utility of amino acid PET in assessing treatment response to bevacizumab in recurrent high-grade glioma. Neurooncol Adv. 2021;3(1).

Ceccon G, Lohmann P, Werner J-M, Tscherpel C, Dunkl V, Stoffels G, et al. Early treatment response assessment using 18F-FET PET compared to contrast-enhanced MRI in glioma patients following adjuvant temozolomide chemotherapy. J Nucl Med. 2020;62(7):918–25.

Kan LK, Drummond K, Hunn M, Williams D, O’Brien TJ, Monif M. Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis. BMJ Neurol Open. 2020;2(2):e000069.

Raza IJ, Tingate CA, Gkolia P, Romero L, Tee JW, Hunn MK. Blood Biomarkers of Glioma in Response Assessment Including Pseudoprogression and Other Treatment Effects: A Systematic Review. Front Oncol. 2020;10:1191.

Oltra-Sastre M, Fuster-Garcia E, Juan-Albarracin J, Sáez C, Perez-Girbes A, Sanz-Requena R, et al. Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Curr Med Imaging Rev. 2019;15(10):933–47.

Dianat-Moghadam H, Azizi M, Eslami-S Z, Cortés-Hernández LE, Heidarifard M, Nouri M, et al. The Role of Circulating Tumor Cells in the Metastatic Cascade: Biology, Technical Challenges, and Clinical Relevance. Cancers. 2020;12(4):867.

Almutairi MMA, Gong C, Xu YG, Chang Y, Shi H. Factors controlling permeability of the blood–brain barrier. Cell Mol Life Sci. 2015;73(1):57–77.

Olioso D, Caccese M, Santangelo A, Lippi G, Zagonel V, Cabrini G, et al. Serum Exosomal microRNA-21, 222 and 124-3p as Noninvasive Predictive Biomarkers in Newly Diagnosed High-Grade Gliomas: A Prospective Study. Cancers. 2021;13(12):3006.

Tankov S, Walker PR. Glioma-Derived Extracellular Vesicles – Far More Than Local Mediators. Front Immunol. 2021;12:679954.

Jelski W, Mroczko B. Molecular and Circulating Biomarkers of Brain Tumors. Int J Mol Sci. 2021;22(13):7039.

Mouliere F, Smith CG, Heider K, Su J, Pol Y, Thompson M, et al. Fragmentation patterns and personalized sequencing of cell‐free DNA in urine and plasma of glioma patients. EMBO Mol Med. 2021;13(8):e12881.

Yu J, Sheng Z, Wu S, Gao Y, Yan Z, Bu C, et al. Tumor DNA From Tumor In Situ Fluid Reveals Mutation Landscape of Minimal Residual Disease After Glioma Surgery and Risk of Early Recurrence. Front Oncol. 2021 Oct 11;11:742037.

Maros ME, Capper D, Jones DTW, Hovestadt V, von Deimling A, Pfister SM, et al. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. Nat Protoc. 2020;15(2):479–512.

Seidlitz A, Beuthien-Baumann B, Löck S, Jentsch C, Platzek I, Zöphel K, et al. Final Results of the Prospective Biomarker Trial PETra: [11C]-MET-Accumulation in Postoperative PET/MRI Predicts Outcome after Radiochemotherapy in Glioblastoma. Clin Cancer Res. 2020;27(5):1351–60.

McGee KP, Hwang K, Sullivan DC, Kurhanewicz J, Hu Y, Wang J, et al. Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294. Med Phys. 2021;48(7):e697–732.

McGee KP, Tyagi N, Bayouth JE, Cao M, Fallone BG, Glide‐Hurst CK, et al. Findings of the AAPM Ad Hoc committee on magnetic resonance imaging in radiation therapy: Unmet needs, opportunities, and recommendations. Med Phys. 2021;48(8):4523–31.

Mathios D, Phallen J. Circulating Biomarkers in Glioblastoma. Cancer J. 2021;27(5):404–9.

Eibl RH, Schneemann M. Liquid Biopsy and Primary Brain Tumors. Cancers. 2021;13(21):5429.

Sobhani F, Robinson R, Hamidinekoo A, Roxanis I, Somaiah N, Yuan Y. Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology. Biochim Biophys Acta Rev Cancer. 2021;1875(2):188520.

Willems SM, Abeln S, Feenstra KA, de Bree R, van der Poel EF, Baatenburg de Jong RJ, et al. The potential use of big data in oncology. Oral Oncol. 2019;98:8–12.

Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neurooncol Adv. 2020;2(Supplement_4):iv3–14.

Shui L, Ren H, Yang X, Li J, Chen Z, Yi C, et al. The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front Oncol. 2021;10:570465.

Buchlak QD, Esmaili N, Leveque J-C, Bennett C, Farrokhi F, Piccardi M. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. J Clin Neurosci. 2021;89:177–98.

TCGA-GBM - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki. [Accessed 2021 Nov 23]. Available from:

TCGA-LGG - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki. [Accessed 2021 Nov 23]. Available from:

Gusev Y, Bhuvaneshwar K, Song L, Zenklusen J-C, Fine H, Madhavan S. The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci Data. 2018;5:180158.

Yang Y, Sui Y, Xie B, Qu H, Fang X. GliomaDB: A Web Server for Integrating Glioma Omics Data and Interactive Analysis. Genomics Proteomics Bioinformatics. 2019;17(4):465–71.

Ismail M, Hill V, Statsevych V, Huang R, Prasanna P, Correa R, et al. Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study. Am J Neuroradiol. 2018;39(12):2187–93.

Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol. 2018;21(3):404–14.

Lao J, Chen Y, Li Z-C, Li Q, Zhang J, Liu J, et al. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. Scientific Reports. 2017;7(1):10353.

Loureiro H, Becker T, Bauer-Mehren A, Ahmidi N, Weberpals J. Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study. Front Artif Intell. 2021;4:625573.

Jiang H, Yu K, Li M, Cui Y, Ren X, Yang C, et al. Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications. Front Oncol. 2020;10:590648.

Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep. 2020;10(1):12598.

Davatzikos C, Barnholtz-Sloan JS, Bakas S, Colen R, Mahajan A, Quintero CB, et al. AI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium. Neuro Oncol. 2020;22(6):886–8.

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:iv22–34.

Kalinin M. Glioma of the left parietal lobe. CT scan with contrast enhancement. 2009 [Accessed 2021 Nov 29]. Available from:

Neurochirurgica A. MRI Image of a Glioblastoma before and after surgery. [Accessed 2021 Nov 29]. Available from:

Dilmen N. Low Grade brain glioma. [Accessed 2021 Nov 29]. Available from:

RadsWiki. Oligodendroglioma, MRI T1 (left), MRI T2 (right) with gadolinium contrast. [Accessed 2021 Nov 29]. Available from:

Dilmen N. Diffusion MRI images. Colored by direction of diffusion.. [Accessed 2021 Nov 29]. Available from:

TattwamasiB. Brain PET CT with various agents – Tumor. [Accessed 2021 Nov 29]. Available from:

Mschocke. MRS Brainstem Glioma. [Accessed 2021 Nov 29]. Available from:

Gigandet X , H.P., Kurant M,Cammoun L, Meuli R, Thiran JP. Estimating the Confidence Level of White Matter Connections Obtained with MRI Tractography. [Accessed 2021 Nov 29]. Available from:

Tanter TD Charlie Demene, Mathieu Pernot, Mickael. Main brain functional imaging techniques on a three-axis chart (temporal resolution, spatial resolution, portability). [Accessed 2021 Nov 29]. Available from:

Tdvorak. Brain stem glioma. MRI axial, with contrast. [Accessed 2021 Nov 29]. Available from:

D’Agnano I, B.A.C. Extracellular Vesicles, A Possible Theranostic Platform Strategy for Hepatocellular Carcinoma—An Overview. [Accessed 2021 Nov 29]. Available from:

Kalamedits. Eexosomes are 30-150 nm extracellular vesicle containing various cargoes like RNA and proteins. [Accessed 2021 Nov 29]. Available from:

Anmery. Clinical applications of ctDNA. [Accessed 2021 Nov 29]. Available from:

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2022 Krauze A