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Membership

Biomarkers Head and Neck

The following list has been endorsed by the European Society of Head and Neck Radiology

Biomarker Units of Measurement Data
Acquisition Modality		 Data Acquisition Requirements/
Patient Preparation		 Biomarker extraction (Reading/
Algorithm)		 Pathophysiological Process Target (diagnostic: characterisation, predictive: treatment response, prognostic: recurrence/survival) References Evidence Level (31!) Evidence (studies: single-center (SC), multi-center (MC), systematic Review (SR), meta-analysis (MA) Issues/
Limitations
DCE (TIC analysis) Shape of the tine-intensity curve MR (T1w DCE) Temporal resolution <10 sec (ideal ≤5 sec) ROI
Curve analysis		 Vascularisation Characterization parotid tumours (1-3)
Characterization SCC (4)		 [1-4!] 3A SR, MA
DCE (Ktrans)  min-1 (Ktrans) Ktrans-ratio (normalised to unaffected tissue) MR (T1w DCE) Temporal resolution <10 sec (ideal ≤5 sec) ROI or VOI
Phamarkokinetic analysis (Tofts' extended model)		 Neoangiogenesis. Ktrans is the volume transfer constant for gadolinium between blood plasma and the tissue extravascular extracellular space (EES). Characterization (HPV) (11)
Characterization (Ki67) (11)
Treatment response evaluation (7)
Patient outcome (Locoregional recurrence) (5,6,8)
Patient outcome (Overall survival) (4,6,8)		 [4-8!] 3A SR, SC no established criteria of interpretation/cut-offs.
Inter-vendor variability.
DCE (Ve) Fractional volume of EESe 0 < ve < 1 MR (T1w DCE) Temporal resolution <10 sec (ideal ≤5 sec) ROI or VOI
Phamarkokinetic analysis (Tofts' extended model)		 Neoangiogenesis.
Ve is the volume of the EES per unit volume of tissue.		 Characterization (HPV) (11)
Characterization (Ki67) (11)
Treatment response evaluation (7)
Patient outcome (Locoregional recurrence) (4,8)
Patient outcome (Overall survival) (4,8)		 [4, 7, 8!] 3A SR, SC no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Inter-vendor variability.
DCE (Vp) ml/100 ml (Vp), Vp-ratio (normalised to unaffected tissue) 0<Vp<1 MR (T1w DCE) Temporal resolution <10 sec (ideal ≤5 sec) ROI or VOI
Phamarkokinetic analysis (Tofts' extended model)		 Neoangiogenesis. Vp is the fractional plasma volume. In many lesions this variable is small and inconsequential. Characterization (VEGF) (11)
Treatment response evaluation (7)
Patient outcome (Locoregional recurrence) (8)
Patient outcome (Overall survival) (8)		 [7, 8!] 3A SR, SC no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Inter-vendor variability.
DCE (kep) min-1 (Kep) Rate constant MR (T1w DCE) Temporal resolution <10 sec (ideal ≤5 sec) ROI or VOI
Phamarkokinetic analysis (Tofts' extended model)		 Neoangiogenesis. Kep is the time constant for gadolinium reflux from the EES back into the vascular system. Characterization (HPV) (11)
Characterization (Ki67) (11)
Treatment response evaluation (7)
Patient outcome (Locoregional recurrence) (8)
Patient outcome (Overall survival) (8)		 [7, 8!] 3A SR, SC no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Inter-vendor variability.
DWI (ADC) mm2/s (ADC) or ratio (rADC, normalised to unaffected tissue) MR (DWI) Comparable sequence available from all major vendors. Mono-exponential fitting of the decay between 0 and high b-value (e.g. b1000 mm2/s) ROI or VOI Restriction and environment of water molecules Characterization parotid tumours (2,3,14)
Characterization SCC (9,10,20)
Characterization SCC-LN (16,18,20)
Characterization (HPV) (11,17,28)
Characterization (Ki67) (11)
Treatment response prediction (5,10,13,15,19)
Treatment response evaluation (7,19,20)
Patient outcome (Locoregional recurrence) (5,7,8,10,13,15,20)
Patient outcome (Distant metatasis) (13)
Patient outcome (Overall survival) (8,10,13)		 [2, 3, 5, 7-20!] 3A SR, MA, SC no widely established criteria of interpretation/cut-offs.
Geometrical distorsion major disadvantage of EPI-acquistion.
Inter-vendor variability.
IVIM (D) mm2/s MR (DWI) Comparable sequence available from all major vendors
Minimum acquisitions of 3 b-values (including b0, a low b-value (i.e. 100 mm2/s) and a high b-value (e.g. b1000 mm2/s)		 ROI or VOI Restriction and environment of water molecules. D is the classical diffusion coefficient. Characterization (lesion) (21)
Characterization (HPV) (11)
Treatment response prediction (21)
Patient outcome (Locoregional recurrence) (5,8,21)
Patient outcome (Overall survival) (8)		 [5, 8, 11, 21!] 3A  SR no widely established criteria of interpretation/cut-offs.
Geometrical distorsion major disadvantage of EPI-acquistion.
Inter-vendor variability.
IVIM (f) mm2/s MR (DWI) Comparable sequence available from all major vendors
Minimum acquisitions of 3 b-values (including b0, a low b-value (i.e. 100 mm2/s) and a high b-value (e.g. b1000 mm2/s).		 ROI or VOI Restriction and environment of water molecules. f is the diffusion of small tissue capillaries. Treatment response evaluation (21)
Patient outcome (Locoregional recurrence) (8,21)		 [8, 21!] 3B SR no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Geometrical distorsion major disadvantage of EPI-acquistion.
Inter-vendor variability.
IVIM (D*) mm2/s MR (DWI) Comparable sequence available from all major vendors
Minimum acquisitions of 3 b-values (including b0, a low b-value (i.e. 100 mm2/s) and a high b-value (e.g. b1000 mm2/s)		 ROI or VOI Restriction and environment of water molecules. Pseudo-diffusion motions related to blood flow. Patient outcome (Overall survival) (8) [8!] 3B SR no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Geometrical distorsion major disadvantage of EPI-acquistion.
Inter-vendor variability.
18F-FDG PET (SUV) standardized-uptake-value SUV (g/mL). The SUV is the ratio of the image-derived radioactivity concentration cimg and the whole body concentration of the injected radioactivity Cinj PET (18F-FDG) 18F-Fluorodeoxyglucose EANM protocol: 10-20 min static imaging min 45-60 min post-injection ROI/VOI
SUV (standardized-uptake-value)
TLG (Total lesion glycolysis)		 Glucose metabolism Treatment response evaluation (22,23)
Patient outcome (Locoregional recurrence) (5)
Patient outcome (Distant metatasis) (5)
Patient outcome (Overall survival) (5)

[5, 22, 23!] 3A SR, MA no widely established criteria of interpretation/cut-offs.
FMISO PET (SUV)  standardized-uptake-value SUV (g/mL). The SUV is the ratio of the image-derived radioactivity concentration cimg and the whole body concentration of the injected radioactivity Cinj PET (18F-FMISO) 18F-fluoromisonidazole 10 min static acquisition planned 160 min post-injection ROI/VOI
SUV (standardized-uptake-value)		 Hypoxia. Reflection of cell reoxygenation. Patient outcome (Locoregional recurrence) (5) [5!] 3B SR no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
FAZA PET (SUV, HV) standardized-uptake-value SUV (g/mL). The SUV is the ratio of the image-derived radioactivity concentration cimg and the whole body concentration of the injected radioactivity Cinj PET (18F-FAZA) 18F-fluoroazomycin-arabinoside 10 min static PET imaging was started 120 min post-injection ROI/VOI
SUV (standardized-uptake-value)
HV (hypoxic volume)		 Hypoxia Patient outcome (Locoregional recurrence) (24) [24!] 3B MC no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
MRI Radiomics and Radiogenomics signatures, Deep learning First order features, texture, shape, intensity, heterogeneity, filters MRI Lesion segmentation by using available software (3D Slicer, MITK, ITK-SNAP, MeVisLab, LifEx, ImageJ, i.e.).
Radiomics feature extraction software (PyRadiomics, LifEx)		 Quantitative features from radiological images. Tumour characteristics. Characterization (SCC/thyroid/parotid/sinonasal) (26)
Characterization (HPV) (11,25)
Patient outcome (Locoregional recurrence) (11)		 [11, 25-27!] 3A SR, MA no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Inter-vendor variability.
PET Radiomics and Radiogenomics signatures, Deep learning First order features, texture, shape, intensity, heterogeneity, filters PET Lesion segmentation by using available software Quantitative features from radiological images.
PET Radiomics:(Cancer Med 2023) 		 Tumour characteristics. Patient outcome (Locoregional recurrence) (29,30)
Patient outcome (Distant metatasis) (29)
Patient outcome (Overall survival) (29,30)		 [29, 30!] 3A SR, MA no established criteria of interpretation/cut-offs. Limited evidence of significant prognostic value.
Inter-vendor variability.
  1. Li ZQ, Gao JN, Xu S, Shi Y, Liu X, Li X, Wan J. Multimodal magnetic resonance imaging for the diagnosis of parotid gland malignancies: systematic review and meta-analysis. Transl Cancer Res. 2022 Jul;11(7):2275-2282.doi: 10.21037/tcr-22-695.
  2. Coudert H, Mirafzal S, Dissard A, Boyer L, Montoriol PF. Multiparametric magnetic resonance imaging of parotid tumors: A systematic review. Diagn Interv Imaging. 2021 Mar;102(3):121-130. doi: 10.1016/j.diii.2020.08.002.
  3. Liang YY, Xu F, Guo Y, Wang J. Diagnostic accuracy of magnetic resonance imaging techniques for parotid tumors, a systematic review and meta-analysis. Clin Imaging. 2018 Nov-Dec;52:36-43. doi: 10.1016/j.clinimag.2018.05.026.
  4. Noij DP, de Jong MC, Mulders LG, Marcus JT, de Bree R, Lavini C, de Graaf P, Castelijns JA. Contrast-enhanced perfusion magnetic resonance imaging for head and neck squamous cell carcinoma: a systematic review. Oral Oncol. 2015 Feb;51(2):124-38. doi: 10.1016/j.oraloncology.2014.10.016.
  5. Martens RM, Noij DP, Ali M, Koopman T, Marcus JT, Vergeer MR, de Vet H, de Jong MC, Leemans CR, Hoekstra OS, de Bree R, de Graaf P, Boellaard R, Castelijns JA. Functional imaging early during (chemo)radiotherapy for response prediction in head and neck squamous cell carcinoma; a systematic review. Oral Oncol. 2019 Jan;88:75-83. doi: 10.1016/j.oraloncology.2018.11.005.
  6. Bernstein JM, Homer JJ, West CM. Dynamic contrast-enhanced magnetic resonance imaging biomarkers in head and neck cancer: potential to guide treatment? A systematic review. Oral Oncol. 2014 Oct;50(10):963-70. doi: 10.1016/j.oraloncology.2014.07.011.
  7. Baba A, Kurokawa R, Rawie E, Kurokawa M, Ota Y, Srinivasan A. Normalized Parameters of Dynamic Contrast-Enhanced Perfusion MRI and DWI-ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer. AJNR Am J Neuroradiol. 2022 Aug;43(8):1184-1189. doi: 10.3174/ajnr.A7567.
  8. Bos P, van der Hulst HJ, van den Brekel MWM, Schats W, Jasperse B, Beets-Tan RGH, Castelijns JA. Prognostic functional MR imaging parameters in head and neck squamous cell carcinoma: A systematic review. Eur J Radiol. 2021 Nov;144:109952. doi: 10.1016/j.ejrad.2021.109952.
  9. Baba A, Kurokawa R, Kurokawa M, Rivera-de Choudens R, Srinivasan A. Apparent diffusion coefficient for differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck: a systematic review and meta-analysis. Acta Radiol. 2024 May;65(5):449-454. doi: 10.1177/02841851241228487.
  10. Parsaei M, Sanjari Moghaddam H, Mazaheri P. The clinical utility of diffusion-weighted imaging in diagnosing and predicting treatment response of laryngeal and hypopharyngeal carcinoma: A systematic review and meta-analysis. Eur J Radiol. 2024 Aug;177:111550. doi: 10.1016/j.ejrad.2024.111550.
  11. van der Hulst HJ, Jansen RW, Vens C, Bos P, Schats W, de Jong MC, Martens RM, Bodalal Z, Beets-Tan RGH, van den Brekel MWM, de Graaf P, Castelijns JA. The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis. Cancers (Basel). 2023 Oct 20;15(20):5077. doi: 10.3390/cancers15205077.
  12. Belfiore MP, Nardone V, D’Onofrio I, Salvia AAH, D’Ippolito E, Gallo L, Caliendo V, Gatta G, Fasano M, Grassi R, Angrisani A, Guida C, Reginelli A, Cappabianca S. Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review. Explor Target Antitumor Ther. 2022;3(6):734-745. doi: 10.37349/etat.2022.00110.
  13. Lee MK, Choi Y, Jung SL. Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis. Sci Rep. 2021 Sep 23;11(1):18986. doi: 10.1038/s41598-021-98508-5.
  14. Munhoz L, Ramos EADA, Im DC, Hisatomi M, Yanagi Y, Asaumi J, Arita ES. Application of diffusion-weighted magnetic resonance imaging in the diagnosis of salivary gland diseases: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 Sep;128(3):280-310. doi: 10.1016/j.oooo.2019.02.020.
  15. Chung SR, Choi YJ, Suh CH, Lee JH, Baek JH. Diffusion-weighted Magnetic Resonance Imaging for Predicting Response to Chemoradiation Therapy for Head and Neck Squamous Cell Carcinoma: A Systematic Review. Korean J Radiol. 2019 Apr;20(4):649-661. doi: 10.3348/kjr.2018.0446.
  16. Payabvash S, Brackett A, Forghani R, Malhotra A. Differentiation of lymphomatous, metastatic, and non-malignant lymphadenopathy in the neck with quantitative diffusion-weighted imaging: systematic review and meta-analysis. Neuroradiology. 2019 Aug;61(8):897-910. doi: 10.1007/s00234-019-02236-7.
  17. Payabvash S, Chan A, Jabehdar Maralani P, Malhotra A. Quantitative diffusion magnetic resonance imaging for prediction of human papillomavirus status in head and neck squamous-cell carcinoma: A systematic review and meta-analysis. Neuroradiol J. 2019 Aug;32(4):232-240. doi: 10.1177/1971400919849808.
  18. Suh CH, Choi YJ, Baek JH, Lee JH. The Diagnostic Value of Diffusion-Weighted Imaging in Differentiating Metastatic Lymph Nodes of Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis. AJNR Am J Neuroradiol. 2018 Oct;39(10):1889-1895. doi: 10.3174/ajnr.A5813.
  19. van der Hoorn A, van Laar PJ, Holtman GA, Westerlaan HE. Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with head and neck tumors, a systematic review and meta-analysis. PLoS One. 2017 May 24;12(5):e0177986. doi: 10.1371/journal.pone.0177986. eCollection 2017.
  20. Driessen JP, Caldas-Magalhaes J, Janssen LM, Pameijer FA, Kooij N, Terhaard CH, Grolman W, Philippens ME. Diffusion-weighted MR imaging in laryngeal and hypopharyngeal carcinoma: association between apparent diffusion coefficient and histologic findings. Radiology. 2014 Aug;272(2):456-63. doi: 10.1148/radiol.14131173.
  21. Noij DP, Martens RM, Marcus JT, de Bree R, Leemans CR, Castelijns JA, de Jong MC, de Graaf P. Intravoxel incoherent motion magnetic resonance imaging in head and neck cancer: A systematic review of the diagnostic and prognostic value. Oral Oncol. 2017 May;68:81-91. doi: 10.1016/j.oraloncology.2017.03.016.
  22. Sheikhbahaei S, Taghipour M, Ahmad R, Fakhry C, Kiess AP, Chung CH, Subramaniam RM. Diagnostic Accuracy of Follow-Up FDG PET or PET/CT in Patients With Head and Neck Cancer After Definitive Treatment: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol. 2015 Sep;205(3):629-39. doi: 10.2214/AJR.14.14166.
  23. Isles MG, McConkey C, Mehanna HM. A systematic review and meta-analysis of the role of positron emission tomography in the follow up of head and neck squamous cell carcinoma following radiotherapy or chemoradiotherapy. Clin Otolaryngol. 2008 Jun;33(3):210-22. doi: 10.1111/j.1749-4486.2008.01688.x.
  24. Mortensen LS, Johansen J, Kallehauge J, Primdahl H, Busk M, Lassen P, Alsner J, Sørensen BS, Toustrup K, Jakobsen S, Petersen J, Petersen H, Theil J, Nordsmark M, Overgaard J. FAZA PET/CT hypoxia imaging in patients with squamous cell carcinoma of the head and neck treated with radiotherapy: results from the DAHANCA 24 trial. Radiother Oncol. 2012 Oct;105(1):14-20. doi: 10.1016/j.radonc.2012.09.015.
  25. Ansari G, Mirza-Aghazadeh-Attari M, Mosier KM, Fakhry C, Yousem DM. Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis. Diagnostics (Basel). 2024 Mar 29;14(7):737. doi: 10.3390/diagnostics14070737.
  26. Jethanandani A, Lin TA, Volpe S, Elhalawani H, Mohamed ASR, Yang P, Fuller CD. Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review. Front Oncol. 2018 May 14;8:131. doi: 10.3389/fonc.2018.00131. eCollection 2018.
  27. Spadarella G, Calareso G, Garanzini E, Ugga L, Cuocolo A, Cuocolo R. MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol. 2021 Jul;140:109744. doi: 10.1016/j.ejrad.2021.109744.
  28. Chen LL, Lauwers I, Verduijn G, Philippens M, Gahrmann R, Capala ME, Petit S. MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review. Cancers (Basel). 2024 May 31;16(11):2105. doi: 10.3390/cancers16112105.
  29. Philip MM, Welch A, McKiddie F, Nath M. A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients. Cancer Med. 2023 Aug;12(15):16181-16194. doi: 10.1002/cam4.6278.
  30. Wang B, Liu J, Xie J, Zhang X, Wang Z, Cao Z, Wen D, Wan Hasan WZ, Harun Ramli HR, Dong X. Systematic review and meta-analysis of the prognostic value of (18)F-Fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET) and/or computed tomography (CT)-based radiomics in head and neck cancer. Clin Radiol. 2024 Oct;79(10):757-772. doi: 10.1016/j.crad.2024.05.016.
  31. https://www.cebm.ox.ac.uk/resources/levels-of-evidence/oxford-centre-for-evidence-based-medicine-levels-of-evidence-march-2009

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Footnotes:

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Reduced registration fees for ECR 2026:
Provided that ESR 2025 membership is activated and approved by August 31, 2025.

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