Biomarkers Neuro-Tumors
The following list has been prepared in collaboration with the EU-COST Action GLiMR2.0 (for further reference please see the main papers of the working group, which can be found here and here).
| Biomarker | Units of Measurement | Acquisition Modality | Data Acquisition Requirements | Patient Prep | Extracting biomarker (Reading/Algorithm) | Pathophysiological Process | Target: MONITORING BIOMARKER |
Evidence Level (Ref) | Evidence (type of studies: single/multi/meta-analysis) | Issues/Limitations |
| DCE (Ktrans) 1-6! | min-1 (Ktrans) Ktrans-ratio (normalised to unaffected tissue) | MR (T1w DCE) | Temporal resolution <10 sec (ideal ≤5 sec) | i.v. placement for contrast agent administration | Phamarkokinetic analysis (Tofts' modles or Tofts' extended model) | Neoangiogenesis | Recurrence, evaluation treatment reponse | 3a | single center, meta-analyses | no established criteria of interpretation/cut-offs |
|
DCE (Vp) 4-7! |
ml/100 ml (Vp), Vp-ratio (normalised to unaffected tissue) |
MR (T1w DCE) |
Temporal resolution <10 sec (ideal ≤5 sec) |
i.v. placement for contrast agent administration |
Phamarkokinetic analysis (Tofts' extended model) |
Neoangiogenesis |
Recurrence, evaluation treatment reponse |
3a |
single center, meta-analyses |
no established criteria of interpretation/cut-offs |
|
DSC (CBV) 2-3!, 8-9! |
standardized units, ml/100g or rCBV-ratio (normalized to unaffected tissue) |
MR (GRE-EPI) |
Temporal resolution 1-1.5 sec, 120 time points, with 30-50 baseline points collected before contrast bolus passage |
i.v. placement for contrast agent administration |
Pharmokinetic models that include conversion to ∆R2* and correction for contrast agent extravastion |
Neoangiogenesis |
Recurrence, evaluation treatment reponse |
2a |
numerous single center, and several multi-center studies, meta-analyses |
Emerging evidence of consistent thresholds, to distinguish glioblastoma tumor from treatment effect, in some single and multi-institutional studies. |
|
ASL (CBF) 10-12! |
ml/min/100ml (CBF units), or ratio (normalised to unaffected tissue) |
MRI (ASL) |
Comparable sequence available from all major vendors (~4 min). Additional calibration scan is required for calibration (<30sec) |
n.a. |
Quantification equation (based on kinetic model) based on the ASL and calibration data |
Neoangiogenesis |
Recurrence, evaluation treatment reponse |
3a |
single center, meta-analyses |
no established criteria of interpretation/cut-offs |
|
1H-MRS (tCho/tNAA) |
ratio |
MRS |
expert method, 5-20 min acquisition |
n.a. |
Spectral fitting |
Metabolism |
Recurrence, evaluation treatment reponse |
3a |
single center, few multicenter, meta-analyses |
no established criteria of interpretation/cut-offs |
|
DWI (ADC) 13-14! |
mm2/s (ADC) or ratio (rADC, normalised to unaffected tissue) |
MR (DWI) |
Comparable sequence available from all major vendors, rapid acquisition (1 min), 3 diffusion directions, minimum b0 and b1000 mm2/s |
n.a. |
ROI or VOI |
Restriction and environment of water molecules |
Recurrence, evaluation treatment reponse |
3a |
single center, few multicenter, one meta-analysis |
no widely established criteria of interpretation/cut-offs |
|
DWI (DTI) 15! |
mm2/s (MD), value range 0-1 (FA) |
MR (DTI) |
Comparable sequence available from all major vendors, 5-15 mins, minimum 6 directions |
n.a. |
ROI or VOI; can undergo post-processing for tractography |
Restriction directionality and environment of water molecules |
Recurrence, evaluation treatment response |
3b |
single center, few multicenter, one meta-analysis |
no consensus regarding direction numbers or b-value shells, no established criteria of interpretation/cut-offs |
|
CEST (APT) 16! |
% of the water signal |
MR (CEST) |
Non-standardised across vendors, at 3T preferred pulse‐train method (B1rms = 2 μT, T sat = 2 s) |
n.a. |
model fitting or MTR analysis in ROI or VOI |
levels of mobile proteins and peptides in tissue |
Recurrence, evaluation treatment response |
3b |
single center |
difficulty to separate chemical and NOE effects at clinical MRI field strength, no established criteria of interpretation/cut-offs |
|
Amino acid PET (tumour-to-background ratio) 17-24! |
a.u. |
PET (11C-MET, 18F-FET, 18F-FDOPA) |
FET: 20 min static 20 min p.i. MET 20 min static 10 min p.i.,FDOPA 10-20 min static 10-30 min p.i. |
i.v. placement for tracer administration & 4 h fasting |
ROI and/or VOI-based analysis |
Expression of L-type amino acid transporter |
Recurrence, evaluation treatment reponse |
3a |
single center, meta-analyses |
availability |
|
FDG PET (tumour-to-background, visual or ratio) 22-24! |
a.u. |
PET (18F-FDG) |
10-20 min static imaging min 45 min p.i. |
i.v. placement for tracer administration & 4 h fasting & 30 min rest p.i. |
ROI/VOI: Tumor-to-background uptake ratios |
Glucose metabolism |
Recurrence, evaluation treatment response |
3a |
single center, meta-analyses |
high physiological uptake in brain |
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- Patel, P., Baradaran, H., Delgado, D., Askin, G., Christos, P., John Tsiouris, A., & Gupta, A. (2017). MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro-oncology, 19(1), 118–127. https://doi.org/10.1093/neuonc/now148
- Wang, L., Wei, L., Wang, J., Li, N., Gao, Y., Ma, H., Qu, X., & Zhang, M. (2020). Evaluation of perfusion MRI value for tumor progression assessment after glioma radiotherapy: A systematic review and meta-analysis. Medicine, 99(52), e23766. https://doi.org/10.1097/MD.0000000000023766
- Quantitative imaging biomarkers alliance (QIBA). (2020). QIBA Profile 4: DCE-MRI Quantification (DCEMRI-Q). Stage 1: Public Comment. https://qibawiki.rsna.org/images/1/1f/QIBA_DCE-MRI_Profile-Stage_1-Public_Comment.pdf. Accessed: 27.02.2023.
- ACR, ASNR, SPR. (Revised 2022; Resolution 24). ACR–ASNR–SPR Practice Parameter For The Performance Of Intracranial Magnetic Resonance Perfusion Imaging. https://www.acr.org/-/media/ACR/Files/Practice-Parameters/MR-Perfusion.pdf. Accessed: 27.02.2023.
- Okuchi, S., Rojas-Garcia, A., Ulyte, A., Lopez, I., Ušinskienė, J., Lewis, M., Hassanein, S. M., Sanverdi, E., Golay, X., Thust, S., Panovska-Griffiths, J., & Bisdas, S. (2019). Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis. Cancer medicine, 8(12), 5564–5573. https://doi.org/10.1002/cam4.2369
- Hatzoglou, V., Yang, T. J., Omuro, A., Gavrilovic, I., Ulaner, G., Rubel, J., Schneider, T., Woo, K. M., Zhang, Z., Peck, K. K., Beal, K., & Young, R. J. (2016). A prospective trial of dynamic contrast-enhanced MRI perfusion and fluorine-18 FDG PET-CT in differentiating brain tumor progression from radiation injury after cranial irradiation. Neuro Oncol, 18(6), 873-880. https://doi.org/10.1093/neuonc/nov301
- Boxerman, J. L., Quarles, C. C., Hu, L. S., Erickson, B. J., Gerstner, E. R., Smits, M., Kaufmann, T. J., Barboriak, D. P., Huang, R. H., Wick, W., Weller, M., Galanis, E., Kalpathy-Cramer, J., Shankar, L., Jacobs, P., Chung, C., van den Bent, M. J., Chang, S., Al Yung, W. K., Cloughesy, T. F., Wen, P. Y., Gilbert, M. R., Rosen, B. R., Ellingson, B. M., & Schmainda, K. M.; Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee. (2020). Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol, 22(9), 1262-1275. https://doi.org/10.1093/neuonc/noaa141
- Hoxworth, J. M., Eschbacher, J. M., Gonzales, A. C., Singleton, K. W., Leon, G. D., Smith, K. A., Stokes, A. M., Zhou, Y., Mazza, G. L., Porter, A. B., Mrugala, M. M., Zimmerman, R. S., Bendok, B. R., Patra, D. P., Krishna, C., Boxerman, J. L., Baxter, L. C., Swanson, K. R., Quarles, C. C., Schmainda, K. M., … Hu, L. S. (2020). Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies. AJNR. American journal of neuroradiology, 41(3), 408–415. https://doi.org/10.3174/ajnr.A6486
- Lindner, T., Bolar, D. S., Achten, E., Barkhof, F., Bastos-Leite, A. J., Detre, J. A., Golay, X., Günther, M., Wang, D. J. J., Haller, S., Ingala, S., Jäger, H. R., Jahng, G. H., Juttukonda, M. R., Keil, V. C., Kimura, H., Ho, M. L., Lequin, M., Lou, X., Petr, J., Pinter, N., Pizzini, F. B., Smits, M., Sokolska, M., Zaharchuk, G., & Mutsaerts, H. J. M. M.; on behalf of the ISMRM Perfusion Study Group. (2023). Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging. Magn Reson Med, 89(5), 2024-2047. https://doi.org/10.1002/mrm.29572
- Kong, L., Chen, H., Yang, Y., & Chen, L. (2017). A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clinical radiology, 72(3), 255–261. https://doi.org/10.1016/j.crad.2016.10.016
- Alsaedi, A., Doniselli, F., Jäger, H. R., Panovska-Griffiths, J., Rojas-Garcia, A., Golay, X., & Bisdas, S. (2019). The value of arterial spin labelling in adults glioma grading: systematic review and meta-analysis. Oncotarget, 10(16), 1589–1601. https://doi.org/10.18632/oncotarget.26674
- Yu, Y., Ma, Y., Sun, M., Jiang, W., Yuan, T., & Tong, D. (2020). Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression. Medicine, 99(23), e20270. https://doi.org/10.1097/MD.0000000000020270
- Zhang, H., Ma, L., Shu, C., Wang, Y. B., & Dong, L. Q. (2015). Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necrosis. Journal of the neurological sciences, 351(1-2), 65–71. https://doi.org/10.1016/j.jns.2015.02.038
- Suh, C. H., Kim, H. S., Jung, S. C., & Kim, S. J. (2018). Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis. AJNR. American journal of neuroradiology, 39(7), 1208–1214. https://doi.org/10.3174/ajnr.A5650
- Zhou, J., Zaiss, M., Knutsson, L., Sun, P. Z., Ahn, S. S., Aime, S., Bachert, P., Blakeley, J. O., Cai, K., Chappell, M. A., Chen, M., Gochberg, D. F., Goerke, S., Heo, H. Y., Jiang, S., Jin, T., Kim, S. G., Laterra, J., Paech, D., Pagel, M. D., … van Zijl, P. C. M. (2022). Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors. Magnetic resonance in medicine, 88(2), 546–574. https://doi.org/10.1002/mrm.29241
- Mehrkens, J. H., Pöpperl, G., Rachinger, W., Herms, J., Seelos, K., Tatsch, K., Tonn, J. C., & Kreth, F. W. (2008). The positive predictive value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET in the diagnosis of a glioma recurrence after multimodal treatment. Journal of neuro-oncology, 88(1), 27–35. https://doi.org/10.1007/s11060-008-9526-4
- Galldiks, N., Lohmann, P., Albert, N. L., Tonn, J. C., & Langen, K. J. (2019). Current status of PET imaging in neuro-oncology. Neuro-oncology advances, 1(1), vdz010. https://doi.org/10.1093/noajnl/vdz010
- Werner, J. M., Lohmann, P., Fink, G. R., Langen, K. J., & Galldiks, N. (2020). Current Landscape and Emerging Fields of PET Imaging in Patients with Brain Tumors. Molecules (Basel, Switzerland), 25(6), 1471. https://doi.org/10.3390/molecules25061471
- Jain, S., & Dhingra, V. K. (2023). An overview of radiolabeled amino acid tracers in oncologic imaging. Frontiers in oncology, 13, 983023. https://doi.org/10.3389/fonc.2023.983023
- Singnurkar, A., Poon, R., & Detsky, J. (2023). 18F-FET-PET imaging in high-grade gliomas and brain metastases: A systematic review and meta-analysis. Journal of Neurooncology, 161(1), 1-12. https://doi.org/10.1007/s11060-022-04201-6
- Cui, M., Zorrilla-Veloz, R. I., Hu, J., Guan, B., & Ma, X. (2021). Diagnostic Accuracy of PET for Differentiating True Glioma Progression From Post Treatment-Related Changes: A Systematic Review and Meta-Analysis. Frontiers in neurology, 12, 671867. https://doi.org/10.3389/fneur.2021.671867
- de Zwart, P. L., van Dijken, B. R. J., Holtman, G. A., Stormezand, G. N., Dierckx, R. A. J. O., Jan van Laar, P., & van der Hoorn, A. (2020). Diagnostic Accuracy of PET Tracers for the Differentiation of Tumor Progression from Treatment-Related Changes in High-Grade Glioma: A Systematic Review and Metaanalysis. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 61(4), 498–504. https://doi.org/10.2967/jnumed.119.233809
- Law, I., Albert, N. L., Arbizu, J., Boellaard, R., Drzezga, A., Galldiks, N., la Fougère, C., Langen, K. J., Lopci, E., Lowe, V., McConathy, J., Quick, H. H., Sattler, B., Schuster, D. M., Tonn, J. C., & Weller, M. (2019). Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0. European journal of nuclear medicine and molecular imaging, 46(3), 540–557. https://doi.org/10.1007/s00259-018-4207-9