Review Article | Published:

The beginning of the end for conventional RECIST — novel therapies require novel imaging approaches

Nature Reviews Clinical Oncology (2019) | Download Citation


Owing to improvements in our understanding of the biological principles of tumour initiation and progression, a wide variety of novel targeted therapies have been developed. Developments in biomedical imaging, however, have not kept pace with these improvements and are still mainly designed to determine lesion size alone, which is reflected in the Response Evaluation Criteria in Solid Tumors (RECIST). Imaging approaches currently used for the evaluation of treatment responses in patients with solid tumours, therefore, often fail to detect successful responses to novel targeted agents and might even falsely suggest disease progression, a scenario known as pseudoprogression. The ability to differentiate between responders and nonresponders early in the course of treatment is essential to allowing the early adjustment of treatment regimens. Various imaging approaches targeting a single dedicated tumour feature, as described in the hallmarks of cancer, have been successful in preclinical investigations, and some have been evaluated in pilot clinical trials. However, these approaches have largely not been implemented in clinical practice. In this Review, we describe current biomedical imaging approaches used to monitor responses to treatment in patients receiving novel targeted therapies, including a summary of the most promising future approaches and how these might improve clinical practice.

Key points

  • Targeted therapies require novel imaging techniques for the assessment of tumour response; the current Response Evaluation Criteria in Solid Tumors (RECIST) are inadequate because tumour diameter does not reflect all types of response.

  • Nuclear medicine-based approaches, such as immuno-PET, enable the detection of specific biomarkers expressed by tumour cells or cells located in the microenvironment, such as tumour-associated immune cells.

  • MRI enables the noninvasive determination of several characteristics of solid tumours including cellularity, stromal composition and fibrosis.

  • Imaging data comprise much more information besides allowing reconstruction of a morphological picture; methods such as magnetic resonance fingerprinting will likely facilitate response evaluation beyond measures of tumour diameter.

  • The integration of imaging biomarkers with other diagnostic tools such as genomics, proteomics and metabolomics is expected to enable more accurate response evaluation than that provided by RECIST in patients with solid tumours.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

  2. 2.

    Schwartz, L. H. et al. RECIST 1.1-update and clarification: from the RECIST committee. Eur. J. Cancer 62, 132–137 (2016).

  3. 3.

    Nishino, M. et al. Radiographic assessment and therapeutic decisions at RECIST progression in EGFR-mutant NSCLC treated with EGFR tyrosine kinase inhibitors. Lung Cancer 79, 283–288 (2013).

  4. 4.

    Patil, V. et al. Is there a limitation of RECIST criteria in prediction of pathological response, in head and neck cancers, to postinduction chemotherapy? ISRN Oncol. 2013, 259154 (2013).

  5. 5.

    Sharma, M. R., Maitland, M. L. & Ratain, M. J. RECIST: no longer the sharpest tool in the oncology clinical trials toolbox — point. Cancer Res. 72, 5145–5149; discussion 5150 (2012).

  6. 6.

    Zhou, T. et al. The effectiveness of RECIST on survival in patients with NSCLC receiving chemotherapy with or without target agents as first-line treatment. Sci. Rep. 5, 7683 (2015).

  7. 7.

    Bronstein, Y., Ng, C. S., Hwu, P. & Hwu, W.-J. Radiologic manifestations of immune-related adverse events in patients with metastatic melanoma undergoing anti-CTLA-4 antibody therapy. AJR Am. J. Roentgenol. 197, W992–W1000 (2011).

  8. 8.

    Nishino, M. et al. Immune-related tumor response dynamics in melanoma patients treated with pembrolizumab: identifying markers for clinical outcome and treatment decisions. Clin. Cancer Res. 23, 4671–4679 (2017).

  9. 9.

    Bieker, R. et al. Infarction of tumor vessels by NGR-peptide-directed targeting of tissue factor: experimental results and first-in-man experience. Blood 113, 5019–5027 (2009).

  10. 10.

    Persigehl, T. et al. Non-invasive monitoring of tumor-vessel infarction by retargeted truncated tissue factor tTF-NGR using multi-modal imaging. Angiogenesis 17, 235–246 (2014).

  11. 11.

    Thompson, E. M., Frenkel, E. P. & Neuwelt, E. A. The paradoxical effect of bevacizumab in the therapy of malignant gliomas. Neurology 76, 87–93 (2011).

  12. 12.

    Kelly-Morland, C. et al. Evaluation of treatment response and resistance in metastatic renal cell cancer (mRCC) using integrated 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography/magnetic resonance imaging (PET/MRI); the REMAP study. BMC Cancer 17, 392 (2017).

  13. 13.

    Kwak, J. J., Tirumani, S. H., van den Abbeele, A. D., Koo, P. J. & Jacene, H. A. Cancer immunotherapy: imaging assessment of novel treatment response patterns and immune-related adverse events. Radiographics 35, 424–437 (2015).

  14. 14.

    Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

  15. 15.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

  16. 16.

    Arteaga, C. L. Epidermal growth factor receptor dependence in human tumors: more than just expression? Oncologist 7 (Suppl. 4), 31–39 (2002).

  17. 17.

    Wee, P. & Wang, Z. Epidermal growth factor receptor cell proliferation signaling pathways. Cancers (Basel) 9, E52 (2017).

  18. 18.

    Red Brewer, M. et al. Mechanism for activation of mutated epidermal growth factor receptors in lung cancer. Proc. Natl Acad. Sci. USA 110, E3595–E3604 (2013).

  19. 19.

    Normanno, N. et al. Epidermal growth factor receptor (EGFR) signaling in cancer. Gene 366, 2–16 (2006).

  20. 20.

    van Dijk, L. K. et al. PET of EGFR with 64Cu-cetuximab-F(ab’)2 in mice with head and neck squamous cell carcinoma xenografts. Contrast Media Mol. Imaging 11, 65–70 (2016).

  21. 21.

    Miao, Z. et al. PET of EGFR expression with an 18F-labeled affibody molecule. J. Nucl. Med. 53, 1110–1118 (2012).

  22. 22.

    Su, X. et al. Comparison of two site-specifically 18F-labeled affibodies for PET imaging of EGFR positive tumors. Mol. Pharm. 11, 3947–3956 (2014).

  23. 23.

    Menke-van der Houven van Oordt, C. W. et al. 89Zr-cetuximab PET imaging in patients with advanced colorectal cancer. Oncotarget 6, 30384–30393 (2015).

  24. 24.

    van Helden, E. J. et al. Pharmacokinetics of cetuximab and tumor uptake of 89Zr-cetuximab as potential predictive biomarkers for benefit of cetuximab in patients with advanced colorectal cancer [abstract]. J. Clin. Oncol. 35 (Suppl. 15), e15117 (2017).

  25. 25.

    Iqbal, R. et al. Validation of [18F]FLT as a perfusion-independent imaging biomarker of tumour response in EGFR-mutated NSCLC patients undergoing treatment with an EGFR tyrosine kinase inhibitor. EJNMMI Res. 8, 22 (2018).

  26. 26.

    Sun, X. et al. A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management. Sci. Transl Med. 10, eaan8840 (2018).

  27. 27.

    Bollineni, V. R., Kramer, G. M., Jansma, E. P., Liu, Y. & Oyen, W. J. G. A systematic review on 18FFLT-PET uptake as a measure of treatment response in cancer patients. Eur. J. Cancer 55, 81–97 (2016).

  28. 28.

    Benz, M. R. et al. 18F-FDG PET/CT for monitoring treatment responses to the epidermal growth factor receptor inhibitor erlotinib. J. Nucl. Med. 52, 1684–1689 (2011).

  29. 29.

    Wahl, R. L., Jacene, H., Kasamon, Y. & Lodge, M. A. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J. Nucl. Med. 50, 122S–150S (2009).

  30. 30.

    O, J. H., Lodge, M. A. & Wahl, R. L. Practical PERCIST: a simplified guide to PET response criteria in solid tumors 1.0. Radiology 280, 576–584 (2016).

  31. 31.

    Turner, N. C., Huang Bartlett, C. & Cristofanilli, M. Palbociclib in hormone-receptor-positive advanced breast cancer. N. Engl. J. Med. 373, 1672–1673 (2015).

  32. 32.

    Degrassi, A. et al. Efficacy of PHA-848125, a cyclin-dependent kinase inhibitor, on the K-Ras(G12D)LA2 lung adenocarcinoma transgenic mouse model: evaluation by multimodality imaging. Mol. Cancer Ther. 9, 673–681 (2010).

  33. 33.

    Wallitt, K. L. et al. Clinical PET imaging in prostate cancer. Radiographics 37, 1512–1536 (2017).

  34. 34.

    Schuster, D. M., Nanni, C. & Fanti, S. PET tracers beyond FDG in prostate cancer. Semin. Nucl. Med. 46, 507–521 (2016).

  35. 35.

    Leonard, J. P. et al. Selective CDK4/6 inhibition with tumor responses by PD0332991 in patients with mantle cell lymphoma. Blood 119, 4597–4607 (2012).

  36. 36.

    Chipuk, J. E., Moldoveanu, T., Llambi, F., Parsons, M. J. & Green, D. R. The BCL-2 family reunion. Mol. Cell 37, 299–310 (2010).

  37. 37.

    Lochmann, T. L. et al. Venetoclax is effective in small-cell lung cancers with high BCL-2 expression. Clin. Cancer Res. 24, 360–369 (2018).

  38. 38.

    Jia, F. et al. Molecular imaging of bcl-2 expression in small lymphocytic lymphoma using 111In-labeled PNA-peptide conjugates. J. Nucl. Med. 49, 430–438 (2008).

  39. 39.

    Hoebers, F. J. P. et al. 99mTc Hynic-rh-Annexin V scintigraphy for in vivo imaging of apoptosis in patients with head and neck cancer treated with chemoradiotherapy. Eur. J. Nuclear Med. Mol. Imaging 35, 509–518 (2008).

  40. 40.

    Zeng, W. et al. Molecular imaging of apoptosis: from micro to macro. Theranostics 5, 559–582 (2015).

  41. 41.

    Mingwei, W., Yujia, Z., Zhang, Y. & Yingjian, Z. Cancer apoptosis detection by 18F-Annexin B1 and 18F-Annexin V PET/CT imaging: a comparative study. J. Nucl. Med. 53 (Suppl. 1), 1700 (2012).

  42. 42.

    Belhocine, T. Z. et al. 99mTc-Annexin A5 quantification of apoptotic tumor response: a systematic review and meta-analysis of clinical imaging trials. Eur. J. Nuclear Med. Mol. Imaging 42, 2083–2097 (2015).

  43. 43.

    Kartachova, M. et al. In vivo imaging of apoptosis by 99mTc-Annexin V scintigraphy: visual analysis in relation to treatment response. Radiother. Oncol. 72, 333–339 (2004).

  44. 44.

    Andrews, L. G. & Tollefsbol, T. O. Methods of telomerase inhibition. Methods Mol. Biol. 405, 1–7 (2007).

  45. 45.

    Middleton, G. et al. Gemcitabine and capecitabine with or without telomerase peptide vaccine GV1001 in patients with locally advanced or metastatic pancreatic cancer (TeloVac): an open-label, randomised, phase 3 trial. Lancet Oncol. 15, 829–840 (2014).

  46. 46.

    Kim, Y.-H. et al. Image-aided suicide gene therapy utilizing multifunctional hTERT-targeting adenovirus for clinical translation in hepatocellular carcinoma. Theranostics 6, 357–368 (2016).

  47. 47.

    Bielenberg, D. R. & Zetter, B. R. The contribution of angiogenesis to the process of metastasis. Cancer J. 21, 267–273 (2015).

  48. 48.

    Murukesh, N., Dive, C. & Jayson, G. C. Biomarkers of angiogenesis and their role in the development of VEGF inhibitors. Br. J. Cancer 102, 8–18 (2010).

  49. 49.

    Sennino, B. & McDonald, D. M. Controlling escape from angiogenesis inhibitors. Nat. Rev. Cancer 12, 699–709 (2012).

  50. 50.

    Ferrara, N., Hillan, K. J., Gerber, H.-P. & Novotny, W. Discovery and development of bevacizumab, an anti-VEGF antibody for treating cancer. Nat. Rev. Drug Discov. 3, 391–400 (2004).

  51. 51.

    Ellis, L. M. & Hicklin, D. J. VEGF-targeted therapy: mechanisms of anti-tumour activity. Nat. Rev. Cancer 8, 579–591 (2008).

  52. 52.

    Ma, X. et al. Integrin-targeted hybrid fluorescence molecular tomography/X-ray computed tomography for imaging tumor progression and early response in non-small cell lung cancer. Neoplasia 19, 8–16 (2017).

  53. 53.

    Chang, S. S. et al. Five different anti-prostate-specific membrane antigen (PSMA) antibodies confirm PSMA expression in tumor-associated neovasculature. Cancer Res. 59, 3192–3198 (1999).

  54. 54.

    Kesler, M. et al. 68Ga-PSMA is a novel PET-CT tracer for imaging of hepatocellular carcinoma: a prospective pilot study. J. Nucl. Med. (2018).

  55. 55.

    Sathekge, M. et al. 68Ga-PSMA imaging of metastatic breast cancer. Eur. J. Nuclear Med. Mol. Imaging 42, 1482–1483 (2015).

  56. 56.

    Kunikowska, J., Bartosz, K. & Leszek, K. Glioblastoma multiforme: another potential application for 68Ga-PSMA PET/CT as a guide for targeted therapy. Eur. J. Nuclear Med. Mol. Imaging 45, 886–887 (2018).

  57. 57.

    van Es, S. C. et al. 89Zr-Bevacizumab PET: potential early indicator of everolimus efficacy in patients with metastatic renal cell carcinoma. J. Nucl. Med. 58, 905–910 (2017).

  58. 58.

    Eo, J. S. & Jeong, J. M. Angiogenesis imaging using 68Ga-RGD PET/CT: therapeutic implications. Semin. Nucl. Med. 46, 419–427 (2016).

  59. 59.

    Zhang, H. et al. Can an ¹8F-ALF-NOTA-PRGD2 PET/CT scan predict treatment sensitivity to concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma? J. Nucl. Med. 57, 524–529 (2016).

  60. 60.

    Zheng, K. et al. 68Ga-NOTA-PRGD2 PET/CT for integrin imaging in patients with lung cancer. J. Nucl. Med. 56, 1823–1827 (2015).

  61. 61.

    Nathan, P. et al. Phase I trial of combretastatin A4 phosphate (CA4P) in combination with bevacizumab in patients with advanced cancer. Clin. Cancer Res. 18, 3428–3439 (2012).

  62. 62.

    Yap, T. A. et al. First-in-human phase I trial of two schedules of OSI-930, a novel multikinase inhibitor, incorporating translational proof-of-mechanism studies. Clin. Cancer Res. 19, 909–919 (2013).

  63. 63.

    Tudorica, A. et al. Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. Transl Oncol. 9, 8–17 (2016).

  64. 64.

    Sabir, A. et al. Perfusion MDCT enables early detection of therapeutic response to antiangiogenic therapy. AJR Am. J. Roentgenol. 191, 133–139 (2008).

  65. 65.

    Ehling, J., Lammers, T. & Kiessling, F. Non-invasive imaging for studying anti-angiogenic therapy effects. Thromb. Haemostasis 109, 375–390 (2013).

  66. 66.

    Jain, R. et al. Imaging response criteria for recurrent gliomas treated with bevacizumab: role of diffusion weighted imaging as an imaging biomarker. J. Neurooncol. 96, 423–431 (2010).

  67. 67.

    Nowosielski, M. et al. ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma. Neuroradiology 53, 291–302 (2011).

  68. 68.

    Mirus, M. et al. Noninvasive assessment and quantification of tumour vascularisation using MRI and CT in a tumour model with modifiable angiogenesis - an animal experimental prospective cohort study. Eur. Radiol. Exp. 1, 15 (2017).

  69. 69.

    Assili, S., Fathi Kazerooni, A., Aghaghazvini, L., Saligheh Rad, H. R. & Pirayesh Islamian, J. Dynamic contrast magnetic resonance imaging (DCE-MRI) and diffusion weighted MR imaging (DWI) for differentiation between benign and malignant salivary gland tumors. J. Biomed. Phys. Engineer. 5, 157–168 (2015).

  70. 70.

    Niccoli Asabella, A., Di Palo, A., Altini, C., Ferrari, C. & Rubini, G. Multimodality imaging in tumor angiogenesis: present status and perspectives. Int. J. Mol. Sci. 18, E1864 (2017).

  71. 71.

    Backer, M. V. & Backer, J. M. Imaging key biomarkers of tumor angiogenesis. Theranostics 2, 502–515 (2012).

  72. 72.

    Iagaru, A. & Gambhir, S. S. Imaging tumor angiogenesis: the road to clinical utility. AJR Am. J. Roentgenol. 201, W183–W191 (2013).

  73. 73.

    Chun, Y. S. et al. Association of computed tomography morphologic criteria with pathologic response and survival in patients treated with bevacizumab for colorectal liver metastases. JAMA 302, 2338–2344 (2009).

  74. 74.

    Saltz, L. B. et al. Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study. J. Clin. Oncol. 26, 2013–2019 (2008).

  75. 75.

    Organ, S. L. & Tsao, M.-S. An overview of the c-MET signaling pathway. Ther. Adv. Med. Oncol. 3, S7–S19 (2011).

  76. 76.

    Bardelli, A. et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov. 3, 658–673 (2013).

  77. 77.

    Hector, A. et al. The Axl receptor tyrosine kinase is an adverse prognostic factor and a therapeutic target in esophageal adenocarcinoma. Cancer Biol. Ther. 10, 1009–1018 (2010).

  78. 78.

    Santoro, M. & Carlomagno, F. Central role of RET in thyroid cancer. Cold Spring Harb. Perspect. Biol. 5, a009233 (2013).

  79. 79.

    Luo, H. et al. PET of c-Met in cancer with 64Cu-labeled hepatocyte growth factor. J. Nucl. Med. 56, 758–763 (2015).

  80. 80.

    Lien, V. T., Klaveness, J. & Olberg, D. E. One-step synthesis of 18F cabozantinib for use in positron emission tomography imaging of c-Met. J. Labelled Comp. Radiopharm. 61, 11–17 (2018).

  81. 81.

    Pool, M. et al. 89Zr-Onartuzumab PET imaging of c-MET receptor dynamics. Eur. J. Nuclear Med. Mol. Imaging 44, 1328–1336 (2017).

  82. 82.

    Laukamp, K. R. et al. Multimodal imaging of patients with gliomas confirms 11C-MET PET as a complementary marker to MRI for noninvasive tumor grading and intraindividual follow-up after therapy. Mol. Imaging 16, 1536012116687651 (2017).

  83. 83.

    Graham, T. J. et al. Preclinical evaluation of imaging biomarkers for prostate cancer bone metastasis and response to cabozantinib. J. Natl Cancer Inst. 106, dju033 (2014).

  84. 84.

    Vaishampayan, U. N. et al. Genomic and imaging biomarkers associated with cabozantinib therapy in metastatic castrate resistant prostate cancer (mCRPC). J. Clin. Oncol. 34, 212 (2016).

  85. 85.

    Oldan, J. D., Hawkins, A. S. & Chin, B. B. 18F sodium fluoride PET/CT in patients with prostate cancer: quantification of normal tissues, benign degenerative lesions, and malignant lesions. World J. Nucl. Med. 15, 102–108 (2016).

  86. 86.

    Chatterjee, S., Behnam Azad, B. & Nimmagadda, S. The intricate role of CXCR4 in cancer. Adv. Cancer Res. 124, 31–82 (2014).

  87. 87.

    Yoon, Y. et al. CXC chemokine receptor-4 antagonist blocks both growth of primary tumor and metastasis of head and neck cancer in xenograft mouse models. Cancer Res. 67, 7518–7524 (2007).

  88. 88.

    Nayak, T. R., Hong, H., Zhang, Y. & Cai, W. Multimodality imaging of CXCR4 in cancer: current status towards clinical translation. Curr. Mol. Med. 13, 1538–1548 (2013).

  89. 89.

    De Silva, R. A. et al. Imaging CXCR4 expression in human cancer xenografts: evaluation of monocyclam 64Cu-AMD3465. J. Nucl. Med. 52, 986–993 (2011).

  90. 90.

    Lapa, C. et al. 68GaPentixafor-PET/CT for imaging of chemokine receptor CXCR4 expression in multiple myeloma - comparison to 18FFDG and laboratory values. Theranostics 7, 205–212 (2017).

  91. 91.

    Vag, T. et al. PET imaging of chemokine receptor CXCR4 in patients with primary and recurrent breast carcinoma. EJNMMI Res. 8, 90 (2018).

  92. 92.

    Vag, T. et al. First experience with chemokine receptor CXCR4-targeted PET imaging of patients with solid cancers. J. Nucl. Med. 57, 741–746 (2016).

  93. 93.

    Eustermann, S. et al. Structural basis of detection and signaling of DNA single-strand breaks by human PARP-1. Mol. Cell 60, 742–754 (2015).

  94. 94.

    Walsh, C. S. Two decades beyond BRCA1/2: homologous recombination, hereditary cancer risk and a target for ovarian cancer therapy. Gynecol. Oncol. 137, 343–350 (2015).

  95. 95.

    Perez-Lopez, R. et al. High frequency of radiological differential responses with poly(ADP-Ribose) polymerase (PARP) inhibitor therapy. Oncotarget 8, 104430–104443 (2017).

  96. 96.

    Reiner, T. et al. Imaging therapeutic PARP inhibition in vivo through bioorthogonally developed companion imaging agents. Neoplasia 14, 169–177 (2012).

  97. 97.

    Tang, J. et al. Targeted PET imaging strategy to differentiate malignant from inflamed lymph nodes in diffuse large B cell lymphoma. Proc. Natl Acad. Sci. USA 114, E7441–E7449 (2017).

  98. 98.

    Anderson, R.-C. et al. Iodinated benzimidazole PARP radiotracer for evaluating PARP1/2 expression in vitro and in vivo. Nuclear Med. Biol. 43, 752–758 (2016).

  99. 99.

    Michel, L. S. et al. PET of poly (ADP-ribose) polymerase activity in cancer: preclinical assessment and first in-human studies. Radiology 282, 453–463 (2017).

  100. 100.

    Makvandi, M. et al. A PET imaging agent for evaluating PARP-1 expression in ovarian cancer. J. Clin. Invest. 128, 2116–2126 (2018).

  101. 101.

    Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nature Med. 19, 1423–1437 (2013).

  102. 102.

    Helfen, A., Roth, J., Ng, T. & Eisenblaetter, M. In vivo imaging of pro- and antitumoral cellular components of the tumor microenvironment. J. Nucl. Med. 59, 183–188 (2018).

  103. 103.

    Yang, R. et al. MRI monitoring of monocytes to detect immune stimulating treatment response in brain tumor. Neuro-oncology 19, 364–371 (2017).

  104. 104.

    Danhier, P. et al. Contribution of macrophages in the contrast loss in iron oxide-based MRI cancer cell tracking studies. Oncotarget 8, 38876–38885 (2017).

  105. 105.

    Becker, A. et al. Optical in vivo imaging of the alarmin S100A9 in tumor lesions allows for estimation of the individual malignant potential by evaluation of tumor-host cell interaction. J. Nucl. Med. 56, 450–456 (2015).

  106. 106.

    Eisenblaetter, M. et al. Visualization of tumor-immune interaction - target-specific imaging of S100A8/A9 reveals pre-metastatic niche establishment. Theranostics 7, 2392–2401 (2017).

  107. 107.

    Weissleder, R., Nahrendorf, M. & Pittet, M. J. Imaging macrophages with nanoparticles. Nat. Mater. 13, 125–138 (2014).

  108. 108.

    Aghighi, M. et al. Magnetic resonance imaging of tumor-associated macrophages: clinical translation. Clin. Cancer Res. 24, 4110–4118 (2018).

  109. 109.

    Daldrup-Link, H. & Coussens, L. M. MR imaging of tumor-associated macrophages. Oncoimmunology 1, 507–509 (2012).

  110. 110.

    Daldrup-Link, H. E. et al. MRI of tumor-associated macrophages with clinically applicable iron oxide nanoparticles. Clin. Cancer Res. 17, 5695–5704 (2011).

  111. 111.

    Leimgruber, A. et al. Behavior of endogenous tumor-associated macrophages assessed in vivo using a functionalized nanoparticle. Neoplasia 11, 459–468 (2009).

  112. 112.

    Hammoud, D. A. Molecular imaging of inflammation: current status. J. Nucl. Med. 57, 1161–1165 (2016).

  113. 113.

    LeBleu, V. Imaging the tumor microenvironment. Cancer J. 21, 174–178 (2015).

  114. 114.

    Wang, M. et al. Role of tumor microenvironment in tumorigenesis. J. Cancer 8, 761–773 (2017).

  115. 115.

    Sharon, E., Streicher, H., Goncalves, P. & Chen, H. X. Immune checkpoint inhibitors in clinical trials. Chinese J. Cancer 33, 434–444 (2014).

  116. 116.

    Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

  117. 117.

    Jenkins, R. W., Barbie, D. A. & Flaherty, K. T. Mechanisms of resistance to immune checkpoint inhibitors. Br. J. Cancer 118, 9–16 (2018).

  118. 118.

    Ribas, A. et al. PD-1 blockade expands intratumoral memory T cells. Cancer Immunol. Res. 4, 194–203 (2016).

  119. 119.

    Crusz, S. M. & Balkwill, F. R. Inflammation and cancer: advances and new agents. Nat. Rev. Clin. Oncol. 12, 584–596 (2015).

  120. 120.

    Fruhwirth, G. O. et al. The potential of in vivo imaging for optimization of molecular and cellular anti-cancer immunotherapies. Mol. Imaging Biol. 20, 696–704 (2018).

  121. 121.

    Soria, F. et al. Pseudoprogression and hyperprogression during immune checkpoint inhibitor therapy for urothelial and kidney cancer. World J. Urol. 36, 1703–1709 (2018).

  122. 122.

    Hodi, F. S. et al. Evaluation of immune-related response criteria and RECIST v1.1 in patients with advanced melanoma treated with pembrolizumab. J. Clin. Oncol. 34, 1510–1517 (2016).

  123. 123.

    Wolchok, J. D. et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin. Cancer Res. 15, 7412–7420 (2009).

  124. 124.

    Nishino, M. et al. Developing a common language for tumor response to immunotherapy: immune-related response criteria using unidimensional measurements. Clin. Cancer Res. 19, 3936–3943 (2013).

  125. 125.

    Seymour, L. et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 18, e143–e152 (2017).

  126. 126.

    Hodi, F. S. et al. Immune-modified response evaluation criteria in solid tumors (imRECIST): refining guidelines to assess the clinical benefit of cancer immunotherapy. J. Clin. Oncol. 36, 850–858 (2018).

  127. 127.

    Shields, A. F. et al. Immune modulation therapy and imaging: workshop report. J. Nucl. Med. 59, 410–417 (2018).

  128. 128.

    Ehlerding, E. B. et al. ImmunoPET imaging of CTLA-4 expression in mouse models of non-small cell lung cancer. Mol. Pharm. 14, 1782–1789 (2017).

  129. 129.

    Donnelly, D. J. et al. Synthesis and biologic evaluation of a novel 18F-labeled adnectin as a PET radioligand for imaging PD-L1 expression. J. Nucl. Med. 59, 529–535 (2018).

  130. 130.

    Chatterjee, S., Lesniak, W. G. & Nimmagadda, S. Noninvasive imaging of immune checkpoint ligand PD-L1 in tumors and metastases for guiding immunotherapy. Mol. Imaging 16, 1536012117718459 (2017).

  131. 131.

    Chatterjee, S. et al. A humanized antibody for imaging immune checkpoint ligand PD-L1 expression in tumors. Oncotarget 7, 10215–10227 (2016).

  132. 132.

    Lesniak, W. G. et al. PD-L1 Detection in Tumors Using 64CuAtezolizumab with PET. Bioconjug. Chem. 27, 2103–2110 (2016).

  133. 133.

    Liu, Z. & Li, Z. Molecular imaging in tracking tumor-specific cytotoxic T lymphocytes (CTLs). Theranostics 4, 990–1001 (2014).

  134. 134.

    Wei, W., Jiang, D., Ehlerding, E. B., Luo, Q. & Cai, W. Noninvasive PET imaging of T cells. Trends Cancer 4, 359–373 (2018).

  135. 135.

    Tavaré, R. et al. An effective immuno-PET imaging method to monitor CD8-dependent responses to immunotherapy. Cancer Res. 76, 73–82 (2016).

  136. 136.

    Barrio, M. J. et al. Human biodistribution and radiation dosimetry of 18F-Clofarabine, a PET probe targeting the deoxyribonucleoside salvage pathway. J. Nucl. Med. 58, 374–378 (2017).

  137. 137.

    Liberti, M. V. & Locasale, J. W. The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016).

  138. 138.

    Zhao, Y., Butler, E. B. & Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 4, e532 (2013).

  139. 139.

    Miles, K. A. & Williams, R. E. Warburg revisited: imaging tumour blood flow and metabolism. Cancer Imaging 8, 81–86 (2008).

  140. 140.

    Shankar, L. K. et al. Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials. J. Nucl. Med. 47, 1059–1066 (2006).

  141. 141.

    Grootjans, W. et al. PET in the management of locally advanced and metastatic NSCLC. Nat. Rev. Clin. Oncol. 12, 395–407 (2015).

  142. 142.

    Tunariu, N., Kaye, S. B. & Desouza, N. M. Functional imaging: what evidence is there for its utility in clinical trials of targeted therapies? Br. J. Cancer 106, 619–628 (2012).

  143. 143.

    Gambhir, S. S. Molecular imaging of cancer with positron emission tomography. Nat. Rev. Cancer 2, 683–693 (2002).

  144. 144.

    Wang, X. et al. Diffusion kurtosis imaging combined with DWI at 3-T MRI for detection and assessment of aggressiveness of prostate cancer. AJR Am. J. Roentgenol. 211, 797–804 (2018).

  145. 145.

    Wang, J. et al. Magnetic resonance imaging of glucose uptake and metabolism in patients with head and neck cancer. Sci. Rep. 6, 30618 (2016).

  146. 146.

    Rivlin, M., Horev, J., Tsarfaty, I. & Navon, G. Molecular imaging of tumors and metastases using chemical exchange saturation transfer (CEST) MRI. Sci. Rep. 3, 3045 (2013).

  147. 147.

    Schuenke, P. et al. Fast and quantitative T1ρ-weighted dynamic glucose enhanced MRI. Sci. Rep. 7, 42093 (2017).

  148. 148.

    Skinner, J. G. et al. Metabolic and molecular imaging with hyperpolarised tracers. Mol. Imaging Biol. 20, 902–918 (2018).

  149. 149.

    Siddiqui, S. et al. The use of hyperpolarized carbon-13 magnetic resonance for molecular imaging. Adv. Drug Deliv. Rev. 113, 3–23 (2017).

  150. 150.

    von Morze, C. & Merritt, M. E. Cancer in the crosshairs: targeting cancer metabolism with hyperpolarized carbon-13 MRI technology. NMR Biomed. (2018).

  151. 151.

    Nelson, S. J. et al. Metabolic imaging of patients with prostate cancer using hyperpolarized 1-¹³Cpyruvate. Sci. Transl Med. 5, 198ra108 (2013).

  152. 152.

    Miloushev, V. Z. et al. Metabolic imaging of the human brain with hyperpolarized 13C pyruvate demonstrates 13C lactate production in brain tumor patients. Cancer Res. 78, 3755–3760 (2018).

  153. 153.

    Braren, R. F. & Siveke, J. T. Next-generation metabolic imaging in pancreatic cancer. Gut 65, 367–369 (2016).

  154. 154.

    Rossi, S. et al. Clinical characteristics of patient selection and imaging predictors of outcome in solid tumors treated with checkpoint-inhibitors. Eur. J. Nuclear Med. Mol. Imaging 44, 2310–2325 (2017).

  155. 155.

    Wen, P. Y. et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28, 1963–1972 (2010).

  156. 156.

    Bruix, J. et al. Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. J. Hepatol. 35, 421–430 (2001).

  157. 157.

    Lencioni, R. & Llovet, J. M. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin. Liver Dis. 30, 52–60 (2010).

  158. 158.

    Carter, B. W., Bhosale, P. R. & Yang, W. T. Immunotherapy and the role of imaging. Cancer 124, 2906–2922 (2018).

  159. 159.

    Couzin-Frankel, J. Breakthrough of the year 2013. Cancer immunotherapy. Science 342, 1432–1433 (2013).

  160. 160.

    Jansen, G., Gatenby, R. & Aktipis, C. A. Opinion: control versus eradication: applying infectious disease treatment strategies to cancer. Proc. Natl Acad. Sci. USA 112, 937–938 (2015).

  161. 161.

    Gatenby, R. A. A change of strategy in the war on cancer. Nature 459, 508–509 (2009).

  162. 162.

    Kelloff, G. J. et al. Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clin. Cancer Res. 11, 2785–2808 (2005).

  163. 163.

    Quandt, D. et al. Implementing liquid biopsies into clinical decision making for cancer immunotherapy. Oncotarget 8, 48507–48520 (2017).

  164. 164.

    Weissleder, R. & Pittet, M. J. Imaging in the era of molecular oncology. Nature 452, 580–589 (2008).

  165. 165.

    Maley, C. C. et al. Classifying the evolutionary and ecological features of neoplasms. Nat. Rev. Cancer 17, 605–619 (2017).

  166. 166.

    Ibragimova, M. K., Tsyganov, M. M. & Litviakov, N. V. Natural and chemotherapy-induced clonal evolution of tumors. Biochemistry Mosc. 82, 413–425 (2017).

  167. 167.

    Pedersen, K. et al. Pancreatic cancer heterogeneity and response to Mek inhibition. Oncogene 36, 5639–5647 (2017).

  168. 168.

    Galli, G. et al. Neoadjuvant chemotherapy exerts selection pressure towards luminal phenotype breast cancer. Breast Care (Basel) 12, 391–394 (2017).

  169. 169.

    Gahlaut, R. et al. Effect of neoadjuvant chemotherapy on breast cancer phenotype, ER/PR and HER2 expression - Implications for the practising oncologist. Eur. J. Cancer 60, 40–48 (2016).

  170. 170.

    Shi, Y.-J., Tsang, J. Y. S., Ni, Y.-B. & Tse, G. M. Intratumoral heterogeneity in breast cancer: a comparison of primary and metastatic breast cancers. Oncologist 22, 487–490 (2017).

  171. 171.

    Mendler, C. T., Gehring, T., Wester, H.-J., Schwaiger, M. & Skerra, A. 89Zr-labeled versus ¹24I-labeled αHER2 Fab with optimized plasma half-life for high-contrast tumor imaging in vivo. J. Nucl. Med. 56, 1112–1118 (2015).

  172. 172.

    Jauw, Y. W. S. et al. Immuno-positron emission tomography with zirconium-89-labeled monoclonal antibodies in oncology: what can we learn from initial clinical trials? Front. Pharmacol. 7, 131 (2016).

  173. 173.

    Bussink, J., van Herpen, C. M. L., Kaanders, J. H. A. M. & Oyen, W. J. G. PET-CT for response assessment and treatment adaptation in head and neck cancer. Lancet. Oncol. 11, 661–669 (2010).

  174. 174.

    Jansen, M. H. et al. Molecular drug imaging: 89Zr-Bevacizumab PET in children with diffuse intrinsic pontine glioma. J. Nucl. Med. 58, 711–716 (2017).

  175. 175.

    Ma, D. et al. Magnetic resonance fingerprinting. Nature 495, 187–192 (2013).

  176. 176.

    Braadland, P. R. et al. Ex vivo metabolic fingerprinting identifies biomarkers predictive of prostate cancer recurrence following radical prostatectomy. Br. J. Cancer 117, 1656–1664 (2017).

  177. 177.

    Yu, A. C. et al. Development of a combined MR fingerprinting and diffusion examination for prostate cancer. Radiology 283, 729–738 (2017).

  178. 178.

    Just, N. Improving tumour heterogeneity MRI assessment with histograms. Br. J. Cancer 111, 2205–2213 (2014).

  179. 179.

    Meyer, H.-J., Schob, S., Höhn, A. K. & Surov, A. MRI texture analysis reflects histopathology parameters in thyroid cancer - a first preliminary study. Transl Oncol. 10, 911–916 (2017).

  180. 180.

    Ytre-Hauge, S. et al. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J. Magn. Reson. Imaging. 48, 1637–1647 (2018).

  181. 181.

    Sandrasegaran, K., Lin, Y., Asare-Sawiri, M., Taiyini, T. & Tann, M. CT texture analysis of pancreatic cancer. Eur. Radiol. (2018).

  182. 182.

    Chowdhury, R. et al. The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis. Br. J. Radiol. 87, 20140065 (2014).

  183. 183.

    Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

  184. 184.

    Kiessling, F. The changing face of cancer diagnosis: from computational image analysis to systems biology. Eur. Radiol. 28, 3160–3164 (2018).

  185. 185.

    Carlsson, A. et al. Circulating tumor microemboli diagnostics for patients with non-small-cell lung cancer. J. Thorac. Oncol. 9, 1111–1119 (2014).

  186. 186.

    Gambhir, S. S., Ge, T. J., Vermesh, O. & Spitler, R. Toward achieving precision health. Sci. Transl Med. 10, eaao3612 (2018).

  187. 187.

    Herrmann, K. et al. A pilot study to evaluate 3΄-deoxy-3΄-18F-fluorothymidine pet for initial and early response imaging in mantle cell lymphoma. J. Nucl. Med. 52, 1898–1902 (2011).

  188. 188.

    Lassau, N. et al. Selection of an early biomarker for vascular normalization using dynamic contrast-enhanced ultrasonography to predict outcomes of metastatic patients treated with bevacizumab. Ann. Oncol. 27, 1922–1928 (2016).

  189. 189.

    Mains, J. R., Donskov, F., Pedersen, E. M., Madsen, H. H. T. & Rasmussen, F. Dynamic contrast-enhanced computed tomography as a potential biomarker in patients with metastatic renal cell carcinoma: preliminary results from the Danish Renal Cancer Group Study-1. Invest. Radiol. 49, 601–607 (2014).

  190. 190.

    Philipp-Abbrederis, K. et al. In vivo molecular imaging of chemokine receptor CXCR4 expression in patients with advanced multiple myeloma. EMBO Mol. Med. 7, 477–487 (2015).

Download references


W.E.B. acknowledges grant support from the Deutsche Krebshilfe (grant no. Berdel-70111004) for the tTF-NGR study.

Author information

Author notes

  1. These authors contributed equally: Michel Eisenblätter, Moritz Wildgruber.


  1. Institute of Clinical Radiology, University Hospital Muenster, Muenster, Germany

    • Mirjam Gerwing
    • , Anne Helfen
    • , Michel Eisenblätter
    •  & Moritz Wildgruber
  2. Department of Nuclear Medicine, University Hospital Essen, Essen, Germany

    • Ken Herrmann
  3. Department of Medicine A, University Hospital Muenster, Muenster, Germany

    • Christoph Schliemann
    •  & Wolfgang E. Berdel
  4. DFG Cluster of Excellence EXC 1003 ‘Cells in Motion’, Muenster, Germany

    • Wolfgang E. Berdel
    •  & Moritz Wildgruber
  5. Richard Dimbleby Department of Cancer Research, Randall Division & Division of Cancer Studies, King’s College London, London, UK

    • Michel Eisenblätter


  1. Search for Mirjam Gerwing in:

  2. Search for Ken Herrmann in:

  3. Search for Anne Helfen in:

  4. Search for Christoph Schliemann in:

  5. Search for Wolfgang E. Berdel in:

  6. Search for Michel Eisenblätter in:

  7. Search for Moritz Wildgruber in:


All authors made a substantial contribution to all aspects of the preparation of this manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Moritz Wildgruber.

Supplementary information

About this article

Publication history