Response Assessment

  • Ines Joye
  • Piet Dirix


It is well recognized that substantial heterogeneity of radiation response in normal tissues and tumors exists between individual patients as well as between individual tumors of the same histology. Even within a single tumor, different regions can have different radiosensitivities, dependent on, for example, tumor microenvironment, inhomogeneous distribution of cancer stem cells, or possibly specific genetic or molecular alterations. To really implement biology-driven precision radiation oncology, which tailors treatment to individual patients based on the biological features of the tumor or normal tissues beyond anatomical information, noninvasive biomarkers are essential. Clearly, repeated imaging that enables the presence and magnitude of specific mechanisms of radioresistance to be assessed in individual tumors would be extremely valuable. This chapter reviews the current clinical evidence on magnetic resonance imaging for response prediction and assessment.


Precision medicine Diffusion-weighted MRI Dynamic contrast-enhanced MRI 


  1. Ahmed HU, Bosaily AE, Brown LC, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389:815–22.PubMedCrossRefGoogle Scholar
  2. Aoyagi T, Shuto K, Okazumi S, Shimada H, Kazama T, Matsubara H. Apparent diffusion coefficient values measured by diffusion-weighted imaging predict chemoradiotherapeutic effect for advanced esophageal cancer. Dig Surg. 2011;28:252–7.PubMedCrossRefGoogle Scholar
  3. Arrayeh E, et al. Does local recurrence of prostate cancer after radiation therapy occur at the site of primary tumor? Results of a longitudinal MRI and MRSI study. Int J Radiat Oncol Biol Phys. 2012;82:e787–93.PubMedPubMedCentralCrossRefGoogle Scholar
  4. Asao C, Korogi Y, Kitajima M, et al. Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol. 2005;26(6):1455–60.PubMedGoogle Scholar
  5. Bahig H, Simard D, Létourneau L, Wong P, Roberge D, Filion E, et al. A study of pseudoprogression after spine stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2016;96(4):848–56.PubMedCrossRefGoogle Scholar
  6. Barajas RF Jr, Chang JS, Segal MR, et al. Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2009;253(2):486–96.PubMedPubMedCentralCrossRefGoogle Scholar
  7. Barbaro B, Vitale R, Valentini V, et al. Diffusion-weighted magnetic resonance imaging in monitoring rectal cancer response to neoadjuvant chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2012;83:594–9.PubMedCrossRefGoogle Scholar
  8. Bauman G, Haider M, Van der Heide U, Ménard C. Boosting imaging defined dominant prostatic tumors: a systematic review. Radiother Oncol. 2013;10:274–81.CrossRefGoogle Scholar
  9. Baumann M, Krause M, Overgaard J, et al. Radiation oncology in the era of precision medicine. Nat Rev Cancer. 2016;16:234–49.PubMedCrossRefGoogle Scholar
  10. Bongers A, Hau E, Shen H. Short diffusion time diffusion-weighted imaging with oscillating gradient preparation as an early magnetic resonance imaging biomarker for radiation therapy response monitoring in glioblastoma: a preclinical feasibility study. Int J Radiat Oncol Biol Phys. 2018;4:pii:S0360-3016(17)34506-6. Scholar
  11. Boonzaier NR, Larkin TJ, Matys T, van der Hoorn A, Yan J, Price SJ. Multiparametric MR imaging of diffusion and perfusion in contrast-enhancing and nonenhancing components in patients with glioblastoma. Radiology. 2017;284(1):180–90.PubMedCrossRefGoogle Scholar
  12. Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9(5):453–61.PubMedCrossRefGoogle Scholar
  13. Bulens P, Couwenberg A, Haustermans K, et al. Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer. Radiother Oncol. 2018;126(3):437–42.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Cai G, Xu Y, Zhu J, et al. Diffusion-weighted magnetic resonance imaging for predicting the response of rectal cancer to neo-adjuvant concurrent chemoradiation. World J Gastroeneterol. 2013;19:5520–7.CrossRefGoogle Scholar
  15. Chopra S, et al. Pathological predictors for site of local recurrence after radiotherapy for prostate cancer. Int J Radiat Oncol Biol Phys. 2012;82:e441–8.PubMedCrossRefGoogle Scholar
  16. Chu HH, Choi SH, Ryoo I, et al. Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging. Radiology. 2013;269(3):831–40.PubMedCrossRefGoogle Scholar
  17. Cusumano D, Dinapoli N, Boldrini L, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2018;123(4):286–95.PubMedCrossRefGoogle Scholar
  18. De Cobelli F, Giganti F, Orsenigo E, et al. Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumour regression grade at histology. Eur Radiol. 2013;23:2165–74.PubMedCrossRefGoogle Scholar
  19. Dirix P, Nuyts S. Evidence-based organ-sparing radiotherapy in head and neck cancer. Lancet Oncol. 2010;11:85–91.PubMedCrossRefGoogle Scholar
  20. Dirix P, De Keyzer F, Vandecaveye V, et al. Diffusion-weighted magnetic resonance imaging to evaluate major salivary gland function before and after radiotherapy. Int J Radiat Oncol Biol Phys. 2008;71(5):1365–71.PubMedCrossRefPubMedCentralGoogle Scholar
  21. Dirix P, Vandecaveye F, De Keyzer F, et al. Dose painting in radiotherapy for head and neck squamous cell carcinoma: value of repeated functional imaging with (18)F-FDG PET, (18)F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med. 2009;50:1020–7.PubMedCrossRefGoogle Scholar
  22. Driessen JP, Caldas-Magalhaes J, Janssen LM, et al. Diffusion-weighted MRI imaging in laryngeal and hypopharyngeal carcinoma: association between apparent diffusion coefficient and histologic findings. Radiology. 2014;272:456–63.PubMedCrossRefGoogle Scholar
  23. Fatterpekar GM, Galheigo D, Narayana A, Johnson G, Knopp E. Treatment-related change versus tumor recurrence in high-grade gliomas: a diagnostic conundrum—use of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI. AJR Am J Roentgenol. 2012;198(1):19–26.PubMedCrossRefGoogle Scholar
  24. Fuchsjager MH, et al. Predicting post-external beam radiation therapy PSA relapse of prostate cancer using pretreatment MRI. Int J Radiat Oncol Biol Phys. 2010;78:743–50.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Galvin J, De Neve W. Intensity modulating and other radiation therapy devices for dose painting. J Clin Oncol. 2007;25:924–30.PubMedCrossRefGoogle Scholar
  26. Glynne-Jones R, Wyrwicz L, Tiret E, et al. Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017;28(suppl_4):iv22–40.PubMedCrossRefGoogle Scholar
  27. Gollub MJ, Tong T, Weiser M, Zheng J, Gonen M, Zakian KL. Limited accuracy of DCE-MRI in identification of pathological complete responders after chemoradiotherapy treatment for rectal cancer. Eur Radiol. 2017;27(4):1605–12.PubMedCrossRefGoogle Scholar
  28. Habr-Gama A, Perez RO, Nadalin W, et al. Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: long-term results. Ann Surg. 2004;240(4):711–7; discussion 717–8.PubMedPubMedCentralGoogle Scholar
  29. Harrison L, Blackwell K. Hypoxia and anemia: factors in decreased sensitivity to radiation therapy and chemotherapy? Oncologist. 2004;5:31–40.CrossRefGoogle Scholar
  30. Hatakenaka M, et al. Pretreatment apparent diffusion coefficient of the primary lesion correlates with local failure in head-and-neck cancer treated with chemoradiotherapy or radiotherapy. Int J Radiat Oncol Biol Phys. 2011;81:339–45.PubMedCrossRefGoogle Scholar
  31. Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol. 2004;25(2):201–9.PubMedGoogle Scholar
  32. Hu LS, Baxter LC, Smith KA, et al. Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. AJNR Am J Neuroradiol. 2009;30(3):552–8.PubMedCrossRefGoogle Scholar
  33. Hyare H, Thust S, Rees J. Advanced MRI techniques in the monitoring of treatment of gliomas. Curr Treat Options Neurol. 2017;19(3):11.PubMedCrossRefGoogle Scholar
  34. Intven M, Reerink O, Philippens ME. Diffusion-weighted MRI in locally advanced rectal cancer : pathological response prediction after neo-adjuvant radiochemotherapy. Strahlenther Onkol. 2013;189:117–22.PubMedCrossRefGoogle Scholar
  35. Intven M, Reerink O, Philippens ME. Dynamic contrast enhanced MR imaging for rectal cancer response assessment after neo-adjuvant chemoradiation. J Magn Reson Imaging. 2015;41(6):1646–53.PubMedCrossRefGoogle Scholar
  36. Jaffray D, Das S, Jacobs PM, Jeraj R, Lambin P. How advances in imaging will affect precision radiation oncology. Int J Radiat Oncol Biol Phys. 2018;101(2):292–8.PubMedCrossRefGoogle Scholar
  37. Jeraj R, Cao Y, Ten Haken R, Hahn C, Marks L. Imaging for assessment of radiation-induced normal tissue effects. Int J Radiat Oncol Biol Phys. 2010;76:S140–4.PubMedPubMedCentralCrossRefGoogle Scholar
  38. Joseph T, et al. Pretreatment endorectal magnetic resonance imaging and magnetic resonance spectroscopic imaging features of prostate cancer as predictors of response to external beam radiotherapy. Int J Radiat Oncol Biol Phys. 2009;73:665–71.PubMedCrossRefGoogle Scholar
  39. Joye I, Deroose CM, Vandecaveye V, Haustermans K. The role of diffusion-weighted MRI and (18)F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: a systematic review. Radiother Oncol. 2014;113(2):158–65.PubMedCrossRefGoogle Scholar
  40. Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378(19):1835–6.CrossRefGoogle Scholar
  41. Katsoulakis E, Kumae K, Laufer I, Yamada Y. Stereotactic body radiotherapy in the treatment of spinal metastases. Semin Radiat Oncol. 2017;27(3):209–17.PubMedCrossRefGoogle Scholar
  42. Kim S, Loevner L, Quon H, et al. Diffusion-weighted magnetic resonance imaging for predicting and detection of early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res. 2009;15:986–94.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Kim S, Loevner LA, Quon H, et al. Prediction of response to chemoradiation therapy in squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging. AJNR Am J Neuroradiol. 2010;31:262–8.PubMedCrossRefGoogle Scholar
  44. Krishnan AP, Asher IM, Davis D, Okunieff P, O’Dell WG. Evidence that MR diffusion tensor imaging (tractography) predicts the natural history of regional progression in patients irradiated conformally for primary brain tumors. Int J Radiat Oncol Biol Phys. 2008;7:1553–62.CrossRefGoogle Scholar
  45. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical imaging using advanced feature analysis. Eur J Cancer. 2012;48:441–6.PubMedPubMedCentralCrossRefGoogle Scholar
  46. Lambin P, Leijenaar R, Deist T, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.PubMedCrossRefGoogle Scholar
  47. Lambrecht M, Vandecaveye V, De Keyzer F, et al. Value of diffusion-weighted magnetic resonance imaging for prediction and early assessment of response to neoadjuvant radiochemotherapy in rectal cancer: preliminary results. Int J Radiat Oncol Biol Phys. 2012;82:863–70.PubMedCrossRefGoogle Scholar
  48. Lambrecht M, Van Calster B, Vandecaveye V, et al. Integrating pretreatment diffusion weighted MRI into a multivariable prognostic model for head and neck squamous cell carcinoma. Radiother Oncol. 2014;110:429–34.PubMedCrossRefGoogle Scholar
  49. Ling C, Humm J, Larson S, et al. Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys. 2000;47:551–60.PubMedCrossRefGoogle Scholar
  50. Loimu V, Seppälä T, Kapanen M, et al. Diffusion-weighted magnetic resonance imaging for evaluation of salivary gland function in head and neck cancer patients treated with intensity-modulated radiotherapy. Radiother Oncol. 2017;122(2):178–84.PubMedCrossRefGoogle Scholar
  51. Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010;11(9):835–44.PubMedCrossRefGoogle Scholar
  52. Maas M, Beets-Tan RG, Lambregts DM. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer. J Clin Oncol. 2011;29(35):4633–40.PubMedCrossRefGoogle Scholar
  53. Maeda M, Itoh S, Kimura H, et al. Tumor vascularity in the brain: evaluation with dynamic susceptibility-contrast MR imaging. Radiology. 1993;189:233–8.PubMedCrossRefGoogle Scholar
  54. Mardor Y, Pfeffer R, Spiegelmann R, et al. Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. J Clin Oncol. 2003;21(6):1094–100.PubMedCrossRefGoogle Scholar
  55. Monninkhof EM, van Loon JWL, van Vulpen M, et al. Standard whole prostate gland radiotherapy with and without lesion boost in prostate cancer: toxicity in the FLAME randomized controlled trial. Radiother Oncol. 2018;127(1):74–80.PubMedCrossRefGoogle Scholar
  56. Nie K, Shi L, Chen O. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res. 2016;22(21):5256–64.PubMedCrossRefGoogle Scholar
  57. Rafat M, Ali R, Graves E. Imaging radiation response in tumor and normal tissue. Am J Nucl Med Mol Imaging. 2015;5(4):317–32.PubMedPubMedCentralGoogle Scholar
  58. Sahgal A, Atenafu EG, Chao S, et al. Vertebral compression fracture after spine stereotactic body radiotherapy: a multi-institutional analysis with a focus on radiation dose and the spinal instability neoplastic score. J Clin Oncol. 2013;31(27):3426–31.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Shukla-Dave A, Lee NY, Jansen JF, et al. Dynamic contrast-enhanced magnetic resonance imaging as a predictor of outcome in head-and-neck squamous cell carcinoma patients with nodal metastases. Int J Radiat Oncol Biol Phys. 2012;82:1837–44.PubMedCrossRefGoogle Scholar
  60. Sjoquist K, Burmeister BH, Smithers BM, et al. Survival after neoadjuvant chemotherapy or chemoradiotherapy for resectable oesophageal carcinoma: an updated meta-analysis. Lancet Oncol. 2011;12(7):681–92.PubMedCrossRefPubMedCentralGoogle Scholar
  61. Soliman M, Taunk N, Simons R, et al. Anatomic and functional imaging in the diagnosis of spine metastases and response assessment after spine radiosurgery. Neurosurg Focus. 2017;42(1):E5.PubMedCrossRefPubMedCentralGoogle Scholar
  62. Spratt DE, Arevalo-Perez J, Leeman JE, et al. Early magnetic resonance imaging biomarkers to predict local control after high dose stereotactic body radiotherapy for patients with sarcoma spine metastases. Spine J. 2016;16(3):291–8.PubMedPubMedCentralCrossRefGoogle Scholar
  63. Spratt DE, Beeler WH, de Moraes FY, et al. An integrated multidisciplinary algorithm for the management of spinal metastases: an International Spine Oncology Consortium report. Lancet Oncol. 2017;18(12):e720–30.PubMedCrossRefPubMedCentralGoogle Scholar
  64. Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–96.PubMedCrossRefPubMedCentralGoogle Scholar
  65. Stupp R, Hegi ME, Mason WP, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 2009;10(5):459–66.PubMedPubMedCentralCrossRefGoogle Scholar
  66. Sun YS, Zhang XP, Tang L, et al. Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion-weighted MR imaging of early detection of tumor histopathologic downstaging. Radiology. 2010;254:170–8.PubMedCrossRefGoogle Scholar
  67. Swartz JE, Driessen JP, van Kempen PMW, et al. Influence of tumor and microenvironment characteristics on diffusion-weighted imaging in oropharyngeal carcinoma: a pilot study. Oral Oncol. 2018;77:9–15.PubMedCrossRefGoogle Scholar
  68. Thibault I, Chang E, Sheehan J, et al. Response assessment after stereotactic body radiotherapy for spinal metastasis: a report from the SPIne response assessment in Neuro-Oncology (SPINO) group. Lancet Oncol. 2015;16:e595–603.PubMedCrossRefGoogle Scholar
  69. Van Hagen P, Hulshof MC, van Lanschot JJ, et al. Preoperative chemoradiotherapy for esophageal or junctional cancer. N Engl J Med. 2012;366(22):2074–84.PubMedCrossRefGoogle Scholar
  70. Van Rossum PS, van Lier AL, van Vulpen M, et al. Diffusion-weighted magnetic resonance imaging for the prediction of pathologic response to neoadjuvant chemoradiotherapy in esophageal cancer. Radiother Oncol. 2015;115:163–70.PubMedCrossRefGoogle Scholar
  71. Vandecaveye V, Dirix P, De Keyzer F, et al. Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy for head and neck squamous cell carcinoma. Eur Radiol. 2010;20:1703–14.PubMedCrossRefGoogle Scholar
  72. Vandecaveye V, Dirix P, De Keyzer F, et al. Diffusion-weighted magnetic resonance imaging early after chemoradiotherapy to monitor treatment response in head-and-neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys. 2012;82:1098–107.PubMedCrossRefGoogle Scholar
  73. Vecchio FM, Valentini V, Minsky BD. The relationship of pathologic tumor regression grade (TRG) and outcomes after preoperative therapy in rectal cancer. Int J Radiat Oncol Biol Phys. 2005;62(3):752–60.PubMedCrossRefGoogle Scholar
  74. Wang L, Liu L, Han C, et al. The diffusion-weighted magnetic resonance imaging (DWI) predicts the early response of esophageal squamous cell carcinoma to concurrent chemoradiotherapy. Radiother Oncol. 2016a;121(2):246–51.PubMedCrossRefGoogle Scholar
  75. Wang Q, Zhang H, Zhang J, et al. The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: a systematic review and meta-analysis. Eur Radiol. 2016b;26(8):2670–84.PubMedCrossRefGoogle Scholar
  76. Weinreb JC, et al. PI-RADS prostate imaging – reporting and data system: 2015, version 2. Eur Urol. 2016;69:16–40.PubMedCrossRefGoogle Scholar
  77. Wen PY, Macdonald DR, Reardon DA. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–72.PubMedCrossRefGoogle Scholar
  78. Wong KH, Panek R, Dunlop A, et al. Changes in multimodality functional imaging parameters early during chemoradiation predict treatment response in patients with locally advanced head and neck cancer. Eur J Nucl Med Mol Imaging. 2018;45:759–67.PubMedCrossRefGoogle Scholar
  79. Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. J Radiat Res. 2018;59:i25–31.PubMedPubMedCentralCrossRefGoogle Scholar
  80. Yoon RG, Kim HS, Kim DY, Hong GS, Kim SJ. Apparent diffusion coefficient parametric response mapping MRI for follow-up of glioblastoma. Eur Radiol. 2016;26(4):1037–47.PubMedCrossRefGoogle Scholar
  81. Yoon RG, Kim HS, Paik W, Shim WH, Kim SJ, Kim JH. Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promotor methylation. Eur Radiol. 2017;27:255–66.PubMedCrossRefGoogle Scholar
  82. Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu BN. Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol. 2014;83(12):2181–9.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ines Joye
    • 1
    • 2
  • Piet Dirix
    • 1
    • 2
  1. 1.Department of Radiation OncologyIridium Cancer NetworkWilrijkBelgium
  2. 2.Molecular Imaging, Pathology, Radiotherapy & Oncology (MIPRO)University of AntwerpAntwerpenBelgium

Personalised recommendations