Share this content in WeChat
Advances in clinical and radiomics of distinguishing pseudoprogression and true progression in brain gliomas
JIANG Jian  ZHOU Junlin 

Cite this article as: JIANG J, ZHOU J L. Advances in clinical and radiomics of distinguishing pseudoprogression and true progression in brain gliomas[J]. Chin J Magn Reson Imaging, 2023, 14(4): 142-146, 153. DOI:10.12015/issn.1674-8034.2023.04.025.

[Abstract] Glioma is the most common primary intracranial tumor, which is aggressive and highly heterogeneous. Pseudoprogression (PsP) is one of the post-treatment related effects of glioma. The clinical and imaging performance are similar to that of true progression (TP). Therefore, early differentiation of PsP from TP is a difficult problem in clinical diagnosis, which affects clinical decision-making. Conventional MRI signs, advanced MRI technology and parameters are still difficult to distinguish PsP from TP. Artificial intelligence methods such as radiomics can be used for individualized treatment of tumor patients, and change the limitation of image analysis only relying on visual judgment, and have potential in distinguishing PsP from TP. This article reviews the research progress of radiomics in differentiating pseudoprogression from true progression in glioma.
[Keywords] glioma;glioblastoma;magnetic resonance imaging;artificial intelligence;radiomics;machine learning;deep learning;pseudoprogression;true progression

JIANG Jian1, 2, 3   ZHOU Junlin1*  

1 Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Second Clinical School of Lanzhou University, Lanzhou 730030, China

3 Gansu Key Laboratory of Medical Imaging, Lanzhou 730030, China

Corresponding author: Zhou JL, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82071872); Natural Science Foundation of Gansu Province (No. 21JR11RA105); Gansu Key Laboratory of Medical Imaging Open Fund (No. GSYX202007).
Received  2022-10-22
Accepted  2023-04-06
DOI: 10.12015/issn.1674-8034.2023.04.025
Cite this article as: JIANG J, ZHOU J L. Advances in clinical and radiomics of distinguishing pseudoprogression and true progression in brain gliomas[J]. Chin J Magn Reson Imaging, 2023, 14(4): 142-146, 153. DOI:10.12015/issn.1674-8034.2023.04.025.

OSTROM Q T, GITTLEMAN H, TRUITT G, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011-2015[J/OL]. Neuro Oncol, 2018, 20(suppl_4): iv1-iv86 [2022-10-21]. DOI: 10.1093/neuonc/noy131.
BAINE M, BURR J, DU Q, et al. The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients[J]. J Imaging, 2021, 7(2): 17. DOI: 10.3390/jimaging7020017.
LAMBIN P, LEIJENAAR R T, DEIST T M, et al. Radiomics: The bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14: 749-762. DOI: 10.1038/nrclinonc.2017.141.
FENG Y, LAN X L. Introduction to Radiomics[J]. Chin J Nucl Med Mol Imaging, 2023, 43(1): 55-60. DOI: 10.3760/cma.j.cn321828-20211130-00427.
CHAWLA S, BUKHARI S, AFRIDI O M, et al. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma[J/OL]. NMR Biomed, 2022, 35(7): e4719 [2022-10-21]. DOI: 10.1002/nbm.4719.
PLATTEN M, BUNSE L, WICK A, et al. A vaccine targeting mutant IDH1 in newly diagnosed glioma[J]. Nature, 2021, 592(7854): 463-468. DOI: 10.1038/s41586-021-03363-z.
XU Q, LI M S, DONG L N, et al. New imaging technology to evaluate the response of glioma posttreatment[J]. Chin J Med Imaging Technol, 2021, 37(5): 785-788. DOI: 10.13929/j.issn.1003-3289.2021.05.040.
VOSS M, FRANZ K, STEINBACH J P, et al. Contrast enhancing spots as a new pattern of late onset pseudoprogression in glioma patients[J]. J Neurooncol, 2019, 142(1): 161-169. DOI: 10.1007/s11060-018-03076-w.
MÜLLER BARK J, KULASINGHE A, CHUA B, et al. Circulating biomarkers in patients with glioblastoma[J]. Br J Cancer, 2020, 122(3): 295-305. DOI: 10.1038/s41416-019-0603-6.
TSANG D S, MURPHY E S, LUCAS J T JR, et al. Pseudoprogression in pediatric low-grade glioma after irradiation[J]. J Neurooncol, 2017 11, 135(2): 371-379. DOI: 10.1007/s11060-017-2583-9.
ELLINGSON B M, WEN P Y, CLOUGHESY T F. Modified Criteria for Radiographic Response Assessment in Glioblastoma Clinical Trials[J]. Neurotherapeutics, 2017, 14(2): 307-320. DOI: 10.1007/s13311-016-0507-6.
LIN A L, WHITE M, MILLER-THOMAS M M, et al. Molecular and histologic characteristics of pseudoprogression in diffuse gliomas[J]. J Neurooncol, 2016, 130(3): 529-533. DOI: 10.1007/s11060-016-2247-1.
SLATER J M, SHIH H A. Pseudoprogression in low-grade glioma[J]. Transl Cancer Res, 2019, 8(Suppl 6): S580-S584. DOI: 10.21037/tcr.2019.11.16.
ABBASI A W, WESTERLAAN H E, HOLTMAN G A, et al. Incidence of tumour progression and pseudoprogression in high-grade gliomas: a systematic review and meta-analysis[J]. Clin Neuroradiol, 2018, 28(3): 401-411. DOI: 10.1007/s00062-017-0584-x.
ROWE L S, BUTMAN J A, MACKEY M, et al. Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM[J]. J Neurooncol, 2018, 139(1): 145-152. DOI: 10.1007/s11060-018-2855-z.
WEN P Y, CHANG S M, VAN DEN BENT M J, et al. Response Assessment in Neuro-Oncology Clinical Trials[J]. J Clin Oncol, 2017, 35(21): 2439-2449. DOI: 10.1200/JCO.2017.72.7511.
HYGINO DA CRUZ L C JR, RODRIGUEZ I, DOMINGUES R C, et al. Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma[J]. Am J Neuroradiol, 2011, 32: 1978-1985. DOI: 10.3174/ajnr.A2397.
WANG Y, ZHANG Y, JI N. Clinical imaging evaluation of glioma in the evaluation criteria of neurooncological response[J]. Chin J Neurosurg, 2018, 34(1): 89-93. DOI: 10.3760/cma.j.issn.1001-2346.2018.01.021.
SOLINAS C, PORCU M, HLAVATA Z, et al. Critical features and challenges associated with imaging in patients undergoing cancer immunotherapy[J]. Crit Rev Oncol Hematol, 2017, 120: 13-21. DOI: 10.1016/j.critrevonc.2017.09.017.
PANG X L, WU S X, DENG M L. The difference of conventional MRI signs between true and false progression of high-grade gliomas in the brain[J]. Chinese Journal of Neuro-Oncology, 2012, 10(1): 24-29.
HENRIKSEN O M, DEL MAR ÁLVAREZ-TORRES M, FIGUEIREDO P, et al. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques[J]. Front Oncol, 2022, 12: 810263. DOI: 10.3389/fonc.2022.810263.
LI C, GAN Y, CHEN H, et al. Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes. Int Rev Neurobiol[J], 2020, 151: 281-297. DOI: 10.1016/bs.irn.2020.03.009.
ABDULLA S, SAADA J, JOHNSON G, et al. Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma[J]. Clin Radiol, 2015, 70: 1299-1312. DOI: 10.1016/j.crad.2015.06.096.
DANG P, WANG L D, HUANG X Y, et al. Study on the application value of DKI in differentiating recurrence and pseudoprogression of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 28-33. DOI: 10.12015/issn.1674-8034.2022.05.006.
PRAGER A J, MARTINEZ N, BEAL K, et al. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence[J]. AJNR Am J Neuroradiol, 2015, 36(5): 877-885. DOI: 10.3174/ajnr.A4218.
TSAKIRIS C, SIEMPIS T, ALEXIOU G A, et al. Differentiation Between True Tumor Progression of Glioblastoma and Pseudoprogression Using Diffusion-Weighted Imaging and Perfusion-Weighted Imaging: Systematic Review and Meta-analysis[J/OL]. World Neurosurg, 2020, 144: e100-e109 [2023-03-10]. DOI: 10.1016/j.wneu.2020.07.218.
CORDOVA J S, SHU H K, LIANG Z, et al. Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients[J]. Neuro Oncol, 2016, 18(8): 1180-1189. DOI: 10.1093/neuonc/now036.
VERMA G, CHAWLA S, MOHAN S, et al. Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma[J/OL]. NMR Biomed, 2019, 32(2): e4042 [2023-03-10]. DOI: 10.1002/nbm.4042.
MA B, BLAKELEY J O, HONG X, et al. Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas[J]. J Magn Reson Imaging, 2016, 44: 456-462. DOI: 10.1002/jmri.25159.
RUDIE J D, RAUSCHECKER A M, BRYAN R N, et al. Emerging Applications of Artificial Intelligence in Neuro-Oncology[J]. Radiology, 2019, 290(3): 607-618. DOI: 10.1148/radiol.2018181928.
MAMMADOV O, AKKURT B H, MUSIGMANN M, et al. Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent[J/OL]. Heliyon, 2022, 8(8): e10023 [2023-03-10]. DOI: 10.1016/j.heliyon.2022.e10023.
PENG L, PAREKH V, HUANG P, et al. Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics[J]. Int J Radiat Oncol Biol Phys, 2018, 102(4): 1236-1243. DOI: 10.1016/j.ijrobp.2018.05.041.
ISMAIL M, HILL V, STATSEVYCH V, et al. Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study[J]. AJNR Am J Neuroradiol, 2018, 39(12): 2187-2193. DOI: 10.3174/ajnr.A5858.
BANI-SADR A, EKER O F, BERNER L P, et al. Conventional MRI radiomics in patients with suspected early- or pseudo-progression[J/OL]. Neurooncol Adv, 2019, 1(1): vdz019 [2023-03-10]. DOI: 10.1093/noajnl/vdz019.
KIM J Y, PARK J E, JO Y, et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients[J]. Neuro Oncol, 2019, 21(3): 404-414. DOI: 10.1093/neuonc/noy133.
WANG S, MARTINEZ-LAGE M, SAKAI Y, et al. Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI[J]. AJNR Am J Neuroradiol, 2016, 37(1): 28-36. DOI: 10.3174/ajnr.A4474.
PATEL M, ZHAN J, NATARAJAN K, et al. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma[J/OL]. Clin Radiol, 2021, 76(8): 628.e17-628.e27 [2023-03-10]. DOI: 10.1016/j.crad.2021.03.019.
JANG B S, JEON S H, KIM I H, et al. Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma[J/OL]. Sci Rep, 2018, 8(1): 12516 [2023-03-10]. DOI: 10.1038/s41598-018-31007-2.
AKBARI H, RATHORE S, BAKAS S, et al. Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma[J]. Cancer, 2020, 126(11): 2625-2636. DOI: 10.1002/cncr.32790.
LI M, TANG H, CHAN M D, et al. DC-AL GAN: pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and Alexnet[J]. Med Phys, 2020, 47: 1139-1150. DOI: 10.1002/mp.14003.
BAO S S, LIU Y F, LUO Y Y, et al. Progress in imaging differentiation of pseudoprogression and recurrence of glioma after treatment[J]. Chin J Magn Reson Imaging, 2021, 12(3): 85-88. DOI: 10.12015/issn.1674-8034.2021.03.020.
ANTUNES J T, ISMAIL M, HOSSAIN I, et al. RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment[J]. IEEE J Biomed Health Inform, 2022, 26(6): 2627-2636. DOI: 10.1109/JBHI.2022.3146778.
YIP S S, AERTS H J. Applications and limitations of radiomics[J/OL]. Phys Med Biol, 2016, 61(13): R150-R166 [2023-03-10]. DOI: 10.1088/0031-9155/61/13/R150.
VAN TIMMEREN J E, CESTER D, TANADINI-LANG S, et al. Radiomics in medical imaging-"how-to" guide and critical reflection[J]. Insights Imaging, 2020, 11(1): 91. DOI: 10.1186/s13244-020-00887-2.
YI Z, LONG L, ZENG Y, et al. Current Advances and Challenges in Radiomics of Brain Tumors[J/OL]. Front Oncol, 2021, 11: 732196 [2023-03-10]. DOI: 10.3389/fonc.2021.732196.
WU Y, GUO Y, MA J, et al. Research Progress of Gliomas in Machine Learning[J/OL]. Cells, 2021, 10(11): 3169 [2023-03-10]. DOI: 10.3390/cells10113169.
SIDIBE I, TENSAOUTI F, ROQUES M, et al. Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review[J/OL]. Biomedicines, 2022, 10(2): 285 [2023-03-10]. DOI: 10.3390/biomedicines10020285.
SHI Z W, LIU Z Y. The dilemma and way out of radiomics research[J]. Chin J Radiol, 2022, 56(1): 9-11. DOI: 10.3760/cma.j.cn112149-20211111-00998.
GUIOT J, VAIDYANATHAN A, DEPREZ L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging[J]. Med Res Rev, 2022, 42(1): 426-440. DOI: 10.1002/med.21846.

PREV Research progress of MRI in diagnosis and treatment of lower grade glioma based on IDH and 1p/19q classification
NEXT Progress of MRI in differentiating treatment-related changes and recurrence of glioblastoma

Tel & Fax: +8610-67113815    E-mail: