Share:
Share this content in WeChat
X
Review
Implications of habitat imaging-based multisequence MRI in adult-type diffuse glioma
LIU Yanhao  GAO Yang 

LIU Y H, GAO Y. Implications of habitat imaging-based multisequence MRI in adult-type diffuse glioma[J]. Chin J Magn Reson Imaging, 2023, 14(9): 119-124. DOI:10.12015/issn.1674-8034.2023.09.022.


[Abstract] Adult-type diffuse glioma is the most common malignancy among central nervous system tumors, and although surgical resection combined with radiotherapy and chemotherapy prolongs patient survival, its 5-year survival rate remains low. The genetic heterogeneity, epigenetic heterogeneity and environmental heterogeneity encompassed by gliomas greatly affect the effectiveness of patient treatment options. Habitat imaging based on Darwinian evolutionary dynamics can be combined with quantitative MRI to reflect the spatial and temporal heterogeneity of tumors more clearly. The subregions generated by the habitat analysis are expressions of different environmental selective forces and cellular adaptation differences within the tumor. In this review, we first describe the importance of tumor microenvironment heterogeneity studies and the advantages of habitat imaging. Different clustering methods for building habitat maps and their advantages and disadvantages were explored. The application of this technology in the areas of predicting patient survival cycles, assessing tumor genetic subtypes, and monitoring post-treatment response is then summarized. Finally, the technical difficulties faced in this research direction and the future development trend are given in-depth consideration and forward-looking outlook. This will help in genotyping, prognosis prediction, targeted puncture and individualized treatment of adult-type diffuse glioma patients.
[Keywords] glioma;habitat imaging;magnetic resonance imaging;perfusion imaging;diffusion imaging

LIU Yanhao   GAO Yang*  

Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China

Corresponding author: Gao Y, E-mail: 1390903990@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Inner Mongolia Autonomous Region Science and Technology Plan Project (No. 2019GG047).
Received  2023-02-12
Accepted  2023-06-28
DOI: 10.12015/issn.1674-8034.2023.09.022
LIU Y H, GAO Y. Implications of habitat imaging-based multisequence MRI in adult-type diffuse glioma[J]. Chin J Magn Reson Imaging, 2023, 14(9): 119-124. DOI:10.12015/issn.1674-8034.2023.09.022.

[1]
OSTROM Q T, PRICE M, NEFF C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019[J/OL]. Neuro-Oncology, 2022, 24(Suppl 5): v1-v95 [2023-02-10]. https://academic.oup.com/neuro-oncology/article/24/Supplement_5/v1/6742201?login=false. DOI: 10.1093/neuonc/noac202.
[2]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J/OL]. Neuro-Oncology, 2021, 23(8): 1231-1251 [2023-02-10]. https://academic.oup.com/neuro-oncology/article/23/8/1231/6311214?login=false. DOI: 10.1093/neuonc/noab106.
[3]
NICHOLSON J G, FINE H A. Diffuse Glioma Heterogeneity and Its Therapeutic Implications[J]. Cancer Discov, 2021, 11(3): 575-590. DOI: 10.1158/2159-8290.CD-20-1474.
[4]
GATENBY R A, GROVE O, GILLIES R J. Quantitative imaging in cancer evolution and ecology[J]. Radiology, 2013, 269(1): 8-15. DOI: 10.1148/radiol.13122697.
[5]
CUI Y, THA K K, TERASAKA S, et al. Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images[J]. Radiology, 2016, 278(2): 546-553. DOI: 10.1148/radiol.2015150358.
[6]
JUAN-ALBARRACÍN J, FUSTER-GARCIA E, PÉREZ-GIRBÉS A, et al. Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival[J]. Radiology, 2018, 287(3): 944-954. DOI: 10.1148/radiol.2017170845.
[7]
PARK J E, KIM H S, KIM N, et al. Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma[J]. Clin Cancer Res, 2021, 27(1): 237-245. DOI: 10.1158/1078-0432.CCR-20-2156.
[8]
BEIG N, BERA K, PRASANNA P, et al. Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma[J]. Clin Cancer Res, 2020, 26(8): 1866-1876. DOI: 10.1158/1078-0432.CCR-19-2556.
[9]
CUI Y, REN S, THA K K, et al. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma[J]. Eur Radiol, 2017, 27(9): 3583-3592. DOI: 10.1007/s00330-017-4751-x.
[10]
LEE J, NARANG S, MARTINEZ J, et al. Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme[J/OL]. PloS One, 2015, 10(9): e0136557 [2023-02-10]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136557. DOI: 10.1371/journal.pone.0136557.
[11]
ZHANG L, PAN H, LIU Z, et al. Multicenter clinical radiomics-integrated model based on [18F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas[J]. Eur Radiol, 2023, 33(2): 872-883. DOI: 10.1007/s00330-022-09043-4.
[12]
BAILO M, PECCO N, CALLEA M, et al. Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study[J/OL]. Front Neurosci-Switz, 2022, 16: 885291 [2023-02-10]. https://www.frontiersin.org/articles/10.3389/fnins.2022.885291/full. DOI: 10.3389/fnins.2022.885291.
[13]
VARN F S, JOHNSON K C, MARTINEK J, et al. Glioma progression is shaped by genetic evolution and microenvironment interactions[J/OL]. Cell, 2022, 185(12): 2184-2199.e16 [2023-02-10]. https://linkinghub.elsevier.com/retrieve/pii/S0092-8674(22)00536-0. DOI: 10.1016/j.cell.2022.04.038.
[14]
TOMASZEWSKI W, SANCHEZ-PEREZ L, GAJEWSKI T F, et al. Brain Tumor Microenvironment and Host State: Implications for Immunotherapy[J/OL]. Clin Cancer Res, 2019, 25(14): 4202-4210 [2023-02-10]. https://aacrjournals.org/clincancerres/article/25/14/4202/81960/Brain-Tumor-Microenvironment-and-Host-State. DOI: 10.1158/1078-0432.CCR-18-1627.
[15]
ERBANI J, BOON M, AKKARI L. Therapy-induced shaping of the glioblastoma microenvironment: Macrophages at play[J/OL]. Semin Cancer Biol, 2022, 86(Pt 3): 41-56 [2023-02-10]. https://linkinghub.elsevier.com/retrieve/pii/S1044-579X(22)00112-2. DOI: 10.1016/j.semcancer.2022.05.003.
[16]
FENDERSON L E, KOVACH A I, LLAMAS B. Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time[J]. Mol Ecol, 2020, 29(2): 218-246. DOI: 10.1111/mec.15315.
[17]
TORQUETTI C G, DE CARVALHO T P, DE FREITAS R M P, et al. Influence of landscape ecology and physiological implications in bats from different trophic guilds[J/OL]. Sci Total Environ, 2023, 857(Pt 3): 159631 [2023-02-10]. https://www.sciencedirect.com/science/article/abs/pii/S0048969722067316?via%3Dihub. DOI: 10.1016/j.scitotenv.2022.159631.
[18]
O'CONNOR J P B, ROSE C J, WATERTON J C, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome[J]. Clin Cancer Res, 2015, 21(2): 249-257. DOI: 10.1158/1078-0432.CCR-14-0990.
[19]
ZHAO H, BAI Y, WANG M Y. Progress of multimodality magnetic resonance imaging in genotyping and prognostic evaluation of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(9): 98-102. DOI: 10.12015/issn.1674-8034.2021.09.025
[20]
AMINU M, YADAV D, HONG L, et al. Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19[J]. Cancers, 2022, 15(1): 275. DOI: 10.3390/cancers15010275.
[21]
CHEN L, LIU K, ZHAO X, et al. Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases[J/OL]. Front Oncol, 2021, 11: 759897 [2023-02-10]. https://www.frontiersin.org/articles/10.3389/fonc.2021.759897/full. DOI: 10.3389/fonc.2021.759897.
[22]
WU J, CAO G, SUN X, et al. Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy[J]. Radiology, 2018, 288(1): 26-35. DOI: 10.1148/radiol.2018172462.
[23]
RANJBARZADEH R, CAPUTO A, TIRKOLAEE E B, et al. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools[J/OL]. Comput Biol Med, 2023, 152: 106405 [2023-02-10]. https://linkinghub.elsevier.com/retrieve/pii/S0010-4825(22)01113-1. DOI: 10.1016/j.compbiomed.2022.106405.
[24]
FAGHANI S, KHOSRAVI B, MOASSEFI M, et al. A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI[J]. J Digit Imaging, 2023, 36(3): 837-846. DOI: 10.1007/s10278-022-00757-x.
[25]
REHMAN M U, RYU J, NIZAMI I F, et al. RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames[J/OL]. Comput Biol Med, 2023, 152: 106426 [2023-02-10]. https://linkinghub.elsevier.com/retrieve/pii/S0010-4825(22)01134-9. DOI: 10.1016/j.compbiomed.2022.106426.
[26]
STRINGFIELD O, ARRINGTON J A, JOHNSTON S K, et al. Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme[J]. Tomography, 2019, 5(1): 135-144. DOI: 10.18383/j.tom.2018.00052.
[27]
PARK J E, KIM H S, GOH M J, et al. Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set[J]. Radiology, 2015, 275(3): 792-802. DOI: 10.1148/radiol.14141414.
[28]
YANG Y, HAN Y, ZHAO S, et al. Spatial heterogeneity of edema region uncovers survival-relevant habitat of Glioblastoma[J/OL]. Eur J Radiol, 2022, 154: 110423 [2023-02-10]. https://linkinghub.elsevier.com/retrieve/pii/S0720-048X(22)00273-X. DOI: 10.1016/j.ejrad.2022.110423.
[29]
KIM M, PARK J E, KIM H S, et al. Spatiotemporal habitats from multiparametric physiologic MRI distinguish tumor progression from treatment-related change in post-treatment glioblastoma[J/OL]. Eur Radiol, 2021, 31(8): 6374-6383 [2023-02-10]. https://link.springer.com/article/10.1007/s00330-021-07718-y. DOI: 10.1007/s00330-021-07718-y.
[30]
SONG J, YUAN L. Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field[J]. Math Biosci Eng, 2022, 19(2): 1891-1908. DOI: 10.3934/mbe.2022089.
[31]
JUAN-ALBARRACÍN J, FUSTER-GARCIA E, GARCÍA-FERRANDO G A, et al. ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI[J]. Int J Med Inform, 2019, 128: 53-61. DOI: 10.1016/j.ijmedinf.2019.05.002.
[32]
ÁLVAREZ-TORRES M D M, FUSTER-GARCÍA E, REYNÉS G, et al. Differential effect of vascularity between long- and short-term survivors with IDH1/2 glioblastomawild-type.[J/OL]. Nmr Biomed, 2021, 34(4): e4462 [2023-02-10]. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4462. DOI: 10.1002/nbm.4462.
[33]
FUSTER-GARCIA E, JUAN-ALBARRACÍN J, GARCÍA-FERRANDO G A, et al. Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures.[J/OL]. Nmr Biomed, 2018, 31(12): e4006 [2023-02-10]. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4006. DOI: 10.1002/nbm.4006.
[34]
WU H, TONG H, DU X, et al. Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas[J]. Eur Radiol, 2020, 30(6): 3254-3265. DOI: 10.1007/s00330-020-06702-2.
[35]
SLAVKOVA K P, PATEL S H, CACINI Z, et al. Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma[J/OL]. Sci Rep, 2023, 13(1): 2916 [2023-02-10]. https://www.nature.com/articles/s41598-023-30010-6. DOI: 10.1038/s41598-023-30010-6.
[36]
AHIR B K, ENGELHARD H H, LAKKA S S. Tumor Development and Angiogenesis in Adult Brain Tumor: Glioblastoma[J]. Mol Neurobiol, 2020, 57(5): 2461-2478. DOI: 10.1007/s12035-020-01892-8.
[37]
MONTEIRO A R, HILL R, PILKINGTON G J, et al. The Role of Hypoxia in Glioblastoma Invasion[J]. Cells, 2017, 6(4): 45. DOI: 10.3390/cells6040045.
[38]
DOMÈNECH M, HERNÁNDEZ A, PLAJA A, et al. Hypoxia: The Cornerstone of Glioblastoma[J/OL]. Int J Mol Sci, 2021, 22(22): 12608 [2023-02-10]. https://www.mdpi.com/1422-0067/22/22/12608. DOI: 10.3390/ijms222212608.
[39]
DEL MAR ÁLVAREZ-TORRES M, JUAN-ALBARRACÍN J, FUSTER-GARCIA E, et al. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study[J]. J Magn Reson Imaging, 2020, 51(5): 1478-1486. DOI: 10.1002/jmri.26958.
[40]
FERRER V P, MOURA NETO V, MENTLEIN R. Glioma infiltration and extracellular matrix: key players and modulators[J]. Glia, 2018, 66(8): 1542-1565. DOI: 10.1002/glia.23309.
[41]
BERGER T R, WEN P Y, LANG-ORSINI M, et al. World Health Organization 2021 Classification of Central Nervous System Tumors and Implications for Therapy for Adult-Type Gliomas: A Review[J]. JAMA Oncol, 2022, 8(10): 1493-1501. DOI: 10.1001/jamaoncol.2022.2844.
[42]
JIANG J, ZHANG X L, ZHOU J L. Research progress on isocitrate dehydrogenase genotype and imaging of glioma[J]. Chin J Magn Reson Imaging, 2021, 12(5): 103-106. DOI: 10.12015/issn.1674-8034.2021.05.025.
[43]
HAN S, LIU Y, CAI S J, et al. IDH mutation in glioma: molecular mechanisms and potential therapeutic targets[J]. Brit J Cancer2020, 122(11): 1580-1589. DOI: 10.1038/s41416-020-0814-x.
[44]
BUTLER M, PONGOR L, SU Y T, et al. MGMT Status as a Clinical Biomarker in Glioblastoma[J/OL]. Trends Cancer, 2020, 6(5): 380-391. DOI: 10.1016/j.trecan.2020.02.010.
[45]
CHEN S X, XU Y, YE M P, et al. Prediction of MGMT promoter methylation in gliomas with different radiomics models based on MRI[J]. Chin J Magn Reson Imaging, 2022, 13(3): 1-5, 36. DOI: 10.12015/issn.1674-8034.2022.03.001.
[46]
MUR P, RODRÍGUEZ DE LOPE Á, DÍAZ-CRESPO F J, et al. Impact on prognosis of the regional distribution of MGMT methylation with respect to the CpG island methylator phenotype and age in glioma patients[J]. J Neuro-Oncol, 2015, 122(3): 441-450. DOI: 10.1007/s11060-015-1738-9.
[47]
TANCREDI A, GUSYATINER O, BADY P, et al. BET protein inhibition sensitizes glioblastoma cells to temozolomide treatment by attenuating MGMT expression[J]. Cell Death Dis, 2022, 13(12): 1037. DOI: 10.1038/s41419-022-05497-y.
[48]
ZOMER A, CROCI D, KOWAL J, et al. Multimodal imaging of the dynamic brain tumor microenvironment during glioblastoma progression and in response to treatment[J/OL]. iScience, 2022, 25(7): 104570 [2023-02-10]. https://linkinghub.elsevier.com/retrieve/pii/S2589-0042(22)00842-2. DOI: 10.1016/j.isci.2022.104570.
[49]
PARK J E, KIM H S, KIM N, et al. Low conductivity on electrical properties tomography demonstrates unique tumor habitats indicating progression in glioblastoma[J]. Eur Radiol, 2021, 31(9): 6655-6665. DOI: 10.1007/s00330-021-07976-w.
[50]
KATSCHER U, VAN DEN BERG C A T. Electric properties tomography: Biochemical, physical and technical background, evaluation and clinical applications[J/OL]. NMR Biomed, 2017, 30(8) [2023-02-10]. https://pubmed.ncbi.nlm.nih.gov/28543640/. DOI: 10.1002/nbm.3729.
[51]
GIRARD A, LE RESTE P J, METAIS A, et al. Combining (18)F-DOPA PET and MRI with perfusion-weighted imaging improves delineation of high-grade subregions in enhancing and non-enhancing gliomas prior treatment: a biopsy-controlled study.[J]. J Neuro-Oncol, 2021, 155(3): 287-295. DOI: 10.1007/s11060-021-03873-w.
[52]
JOHN F, BOSNYÁK E, ROBINETTE N L, et al. Multimodal imaging-defined subregions in newly diagnosed glioblastoma: impact on overall survival.[J]. Neuro-Oncology, 2019, 21(2): 264-273. DOI: 10.1093/neuonc/noy169.
[53]
BRENDLE C, HEMPEL J M, SCHITTENHELM J, et al. Glioma Grading and Determination of IDH Mutation Status and ATRX loss by DCE and ASL Perfusion[J]. Clinical Neuroradiol, 2018, 28(3): 421-428. DOI: 10.1007/s00062-017-0590-z.
[54]
VAN SANTWIJK L, KOUWENBERG V, MEIJER F, et al. A systematic review and meta-analysis on the differentiation of glioma grade and mutational status by use of perfusion-based magnetic resonance imaging[J]. Insights Imaging, 2022, 13(1): 102. DOI: 10.1186/s13244-022-01230-7.
[55]
BRANCATO V, NUZZO S, TRAMONTANO L, et al. Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review[J/OL]. Cancers, 2020, 12(10): E2858 [2023-02-10]. https://www.mdpi.com/2072-6694/12/10/2858. DOI: 10.3390/cancers12102858.
[56]
SMITS M. MRI biomarkers in neuro-oncology[J]. Nat Rev Neurol, 2021, 17(8): 486-500. DOI: 10.1038/s41582-021-00510-y.
[57]
OVERCAST W B, DAVIS K M, HO C Y, et al. Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors[J]. Curr Oncol Rep, 2021, 23(3): 34. DOI: 10.1007/s11912-021-01020-2.

PREV Research progress of quantitative susceptibility mapping in cognitive impairment
NEXT Research progress of magnetic resonance imaging in the assessment of TAO activity
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn