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Imaging biomarker roadmap for cancer studies

James P. B. O'Connor, Eric O. Aboagye, Judith E. Adams and 74 more

Nature Reviews Clinical Oncology · 2016 · Review

doi:10.1038/nrclinonc.2016.162

Abstract

Imaging biomarkers (IBs) are used extensively in drug development and cancer research, but important differences exist between IBs and biospecimen-derived biomarkers. A tailored 'roadmap' is required for the development of new IBs to be used either in clinical research or for decision-making in healthcare. In this Consensus statement, a group of experts assembled by CRUK and the EORTC present 14 key recommendations for accelerating the clinical translation of IBs. Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

References 168

  1. 1.

    Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89-95.

    Not in our library10.1067/mcp.2001.113989

  2. 2.

    BEST (Biomarkers, Endpoints, and other tools) resource.

    Not in our library

  3. 3.

    Lesko LJ, Atkinson AJ. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu Rev Pharmacol Toxicol. 2001;41:347-366.

    Not in our library10.1146/annurev.pharmtox.41.1.347

  4. 4.

    Micheel C, Nass SJ, Omenn GS, Institute of Medicine Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials. Evolution of translational omics: lessons learned and the path forward. 2012.

    Not in our library

  5. 5.

    Workman P, et al. Minimally invasive pharmacokinetic and pharmacodynamic technologies in hypothesis-testing clinical trials of innovative therapies. J Natl Cancer Inst. 2006;98:580-598.

    Not in our library10.1093/jnci/djj162

  6. 6.

    Yap TA, Sandhu SK, Workman P, de Bono JS. Envisioning the future of early anticancer drug development. Nat Rev Cancer. 2010;10:514-523.

    Not in our library10.1038/nrc2870

  7. 7.

    Hait WN. Forty years of translational cancer research. Cancer Discov. 2011;1:383-390.

    Not in our library10.1158/2159-8290.cd-11-0196

  8. 8.

    La Thangue NB, Kerr DJ. Predictive biomarkers: a paradigm shift towards personalized cancer medicine. Nat Rev Clin Oncol. 2011;8:587-596.

    Not in our library10.1038/nrclinonc.2011.121

  9. 9.

    Poste G. Bring on the biomarkers. Nature. 2011;469:156-157.

    Not in our library10.1038/469156a

  10. 10.

    Hayes DF, et al. Breaking a vicious cycle. Sci Transl Med. 2013;5:196cm6.

    Not in our library10.1126/scitranslmed.3005950

  11. 11.

    Macleod MR, et al. Biomedical research: increasing value, reducing waste. Lancet. 2014;383:101-104.

    Not in our library10.1016/s0140-6736(13)62329-6

  12. 12.

    Lee JW, et al. Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharm Res. 2005;22:499-511.

    Not in our library10.1007/s11095-005-2495-9

  13. 13.

    Waterton JC, Pylkkanen L. Qualification of imaging biomarkers for oncology drug development. Eur J Cancer. 2012;48:409-415.

    Not in our library10.1016/j.ejca.2011.11.037

  14. 14.

    Cooksey D. A review of UK health research funding. 2006.

    Not in our library10.1136/bmj.39059.444120.80

  15. 15.

    Sung NS, et al. Central challenges facing the national clinical research enterprise. JAMA. 2003;289:1278-1287.

    Not in our library10.1001/jama.289.10.1278

  16. 16.

    Lee JW, et al. Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res. 2006;23:312-328.

    Not in our library10.1007/s11095-005-9045-3

  17. 17.

    Goodsaid FM, Frueh FW, Mattes W. Strategic paths for biomarker qualification. Toxicology. 2008;245:219-223.

    Not in our library10.1016/j.tox.2007.12.023

  18. 18.

    Srivastava S, et al. Translational Research Working Group developmental pathway for biospecimen-based assessment modalities. Clin Cancer Res. 2008;14:5672-5677.

    Not in our library10.1158/1078-0432.ccr-08-1267

  19. 19.

    Chetty RK, et al. A systematic approach to preclinical and clinical safety biomarker qualification incorporating Bradford Hill’s principles of causality association. Clin Pharmacol Ther. 2010;88:260-262.

    Not in our library10.1038/clpt.2010.77

  20. 20.

    Khleif SN, Doroshow JH, Hait WN. AACR-FDA-NCI Cancer Biomarkers Collaborative consensus report: advancing the use of biomarkers in cancer drug development. Clin Cancer Res. 2010;16:3299-3318.

    Not in our library10.1158/1078-0432.ccr-10-0880

  21. 21.

    Pharmacological biomarker roadmap.

    Not in our library

  22. 22.

    2004.

    Not in our library

  23. 23.

    Clinical trial imaging endpoint process standards guidance for industry [draft guidance]. 2015.

    Not in our library

  24. 24.

    Woodcock J, Woosley R. The FDA critical path initiative and its influence on new drug development. Annu Rev Med. 2008;59:1-12.

    Not in our library10.1146/annurev.med.59.090506.155819

  25. 25.

    Clarke LP, et al. The Quantitative Imaging Network: NCI’s historical perspective and planned Goals. Transl Oncol. 2014;7:1-4.

    Not in our library10.1593/tlo.13832

  26. 26.

    Shankar LK. The clinical evaluation of novel imaging methods for cancer management. Nat Rev Clin Oncol. 2012;9:738-744.

    Not in our library10.1038/nrclinonc.2012.186

  27. 27.

    Huang EP, et al. Meta-analysis of the technical performance of an imaging procedure: guidelines and statistical methodology. Stat Methods Med Res. 2015;24:141-174.

    Not in our library10.1177/0962280214537394

  28. 28.

    Sullivan DC, et al. Metrology standards for quantitative imaging biomarkers. Radiology. 2015;277:813-825.

    Not in our library10.1148/radiol.2015142202

  29. 29.

    Cancer Imaging Program.

    Not in our library

  30. 30.

    ESR statement on the stepwise development of imaging biomarkers. Insights Imaging. 2013;4:147-152.

    Not in our library10.1007/s13244-013-0220-5

  31. 31.

    Hunter AJ. The Innovative Medicines Initiative: a pre-competitive initiative to enhance the biomedical science base of Europe to expedite the development of new medicines for patients. Drug Discov Today. 2008;13:371-373.

    Not in our library10.1016/j.drudis.2008.02.009

  32. 32.
  33. 33.

    Tofts PS, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223-232.

    Not in our library10.1002/(sici)1522-2586(199909)10:3<223::aid-jmri2>3.0.co;2-s

  34. 34.
  35. 35.

    Shankar LK, 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. 2006;47:1059-1066.

    Not in our library

  36. 36.

    Padhani AR, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11:102-125.

    Not in our library10.1593/neo.81328

  37. 37.

    Leen E, et al. Dynamic contrast enhanced ultrasound assessment of the vascular effects of novel therapeutics in early stage trials. Eur Radiol. 2012;22:1442-1450.

    Not in our library10.1007/s00330-011-2373-2

  38. 38.

    Dorfman GS, Sullivan DC, Schnall MD, Matrisian LM. The Translational Research Working Group developmental pathway for image-based assessment modalities. Clin Cancer Res. 2008;14:5678-5684.

    Not in our library10.1158/1078-0432.ccr-08-1264

  39. 39.

    Buckler AJ, et al. Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities. Radiology. 2011;259:875-884.

    Not in our library10.1148/radiol.10100800

  40. 40.

    Barker A, Kettle JG, Nowak T, Pease JE. Expanding medicinal chemistry space. Drug Discov Today. 2013;18:298-304.

    Not in our library10.1016/j.drudis.2012.10.008

  41. 41.

    Blagg J, Workman P. Chemical biology approaches to target validation in cancer. Curr Opin Pharmacol. 2014;17:87-100.

    Not in our library10.1016/j.coph.2014.07.007

  42. 42.

    Breast imaging reporting and data system atlas (BI-RADS atlas). 2013.

    Not in our library

  43. 43.

    Edge SB, et al. AJCC Cancer Staging Handbook. 2010.

    Not in our library

  44. 44.

    Therasse P, et al. New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst. 2000;92:205-216.

    Not in our library10.1093/jnci/92.3.205

  45. 45.

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

    Not in our library10.1016/j.ejca.2008.10.026

  46. 46.

    Reid DM, et al. Guidance for the management of breast cancer treatment-induced bone loss: a consensus position statement from a UK Expert Group. Cancer Treat Rev. 2008;34(Suppl. 1):S3-S18.

    Not in our library10.1016/j.ctrv.2008.03.007

  47. 47.

    Plana JC, et al. Expert consensus for multimodality imaging evaluation of adult patients during and after cancer therapy: a report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2014;27:911-939.

    Not in our library10.1016/j.echo.2014.07.012

  48. 48.

    Summary of opinion.

    Not in our library

  49. 49.

    Novel drugs approved using surrogate endpoints.

    Not in our library

  50. 50.

    Bergstrom M, et al. Human positron emission tomography studies of brain neurokinin 1 receptor occupancy by aprepitant. Biol Psychiatry. 2004;55:1007-1012.

    Not in our library10.1016/j.biopsych.2004.02.007

  51. 51.

    Willett CG, et al. Direct evidence that the VEGF-specific antibody bevacizumab has antivascular effects in human rectal cancer. Nat Med. 2004;10:145-147.

    Not in our library10.1038/nm988

  52. 52.

    Avril N, Propper D. Functional PET imaging in cancer drug development. Future Oncol. 2007;3:215-228.

    Not in our library10.2217/14796694.3.2.215

  53. 53.

    O’Connor JP, Jackson A, Parker GJ, Roberts C, Jayson GC. Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol. 2012;9:167-177.

    Not in our library10.1038/nrclinonc.2012.2

  54. 54.

    Lassau N, et al. Standardization of dynamic contrast-enhanced ultrasound for the evaluation of antiangiogenic therapies: the French multicenter Support for Innovative and Expensive Techniques Study. Invest Radiol. 2012;47:711-716.

    Not in our library10.1097/rli.0b013e31826dc255

  55. 55.

    van Elmpt W, et al. The PET-boost randomised phase II dose-escalation trial in non-small cell lung cancer. Radiother Oncol. 2012;104:67-71.

    Not in our library10.1016/j.radonc.2012.03.005

  56. 56.

    Taylor FG, et al. Preoperative magnetic resonance imaging assessment of circumferential resection margin predicts disease-free survival and local recurrence: 5-year follow-up results of the MERCURY study. J Clin Oncol. 2014;32:34-43.

    Not in our library10.1200/jco.2012.45.3258

  57. 57.

    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646-674.

    Not in our library10.1016/j.cell.2011.02.013

  58. 58.

    Ueda S, et al. Baseline tumor oxygen saturation correlates with a pathologic complete response in breast cancer patients undergoing neoadjuvant chemotherapy. Cancer Res. 2012;72:4318-4328.

    Not in our library10.1158/0008-5472.can-12-0056

  59. 59.

    Gallagher FA, Kettunen MI, Brindle KM. Imaging pH with hyperpolarized 13C. NMR Biomed. 2011;24:1006-1015.

    Not in our library10.1002/nbm.1742

  60. 60.

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

    Not in our library10.1016/j.ejca.2011.11.036

  61. 61.

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563-577.

    Not in our library10.1148/radiol.2015151169

  62. 62.

    Fletcher JW, et al. Recommendations on the use of 18F-FDG PET in oncology. J Nucl Med. 2008;49:480-508.

    Not in our library10.2967/jnumed.107.047787

  63. 63.

    Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl. 1):122S-150S.

    Not in our library10.2967/jnumed.108.057307

  64. 64.

    Barrington SF, et al. Role of imaging in the staging and response assessment of lymphoma: consensus of the International Conference on Malignant Lymphomas Imaging Working Group. J Clin Oncol. 2014;32:3048-3058.

    Not in our library10.1200/jco.2013.53.5229

  65. 65.

    Na F, et al. Primary tumor standardized uptake value measured on F18-Fluorodeoxyglucose positron emission tomography is of prediction value for survival and local control in non-small-cell lung cancer receiving radiotherapy: meta-analysis. J Thorac Oncol. 2014;9:834-842.

    Not in our library10.1097/jto.0000000000000185

  66. 66.

    Kayani I, et al. Sequential FDG-PET/CT as a biomarker of response to sunitinib in metastatic clear cell renal cancer. Clin Cancer Res. 2011;17:6021-6028.

    Not in our library10.1158/1078-0432.ccr-10-3309

  67. 67.

    Sarker D, et al. First-in-human phase I study of pictilisib (GDC-0941), a potent pan-class I phosphatidylinositol-3-kinase (PI3K) inhibitor, in patients with advanced solid tumors. Clin Cancer Res. 2015;21:77-86.

    Not in our library10.1158/1078-0432.ccr-14-0947

  68. 68.

    Michaelis LC, Ratain MJ. Measuring response in a post-RECIST world: from black and white to shades of grey. Nat Rev Cancer. 2006;6:409-414.

    Not in our library10.1038/nrc1883

  69. 69.

    Lassau N, et al. Advanced hepatocellular carcinoma: early evaluation of response to bevacizumab therapy at dynamic contrast-enhanced US with quantification—preliminary results. Radiology. 2011;258:291-300.

    Not in our library10.1148/radiol.10091870

  70. 70.

    Patterson DM, Padhani AR, Collins DJ. Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy. Nat Clin Pract Oncol. 2008;5:220-233.

    Not in our library10.1038/ncponc1073

  71. 71.

    Waterton JC, Garrido L, Beckmann N. New Applications of NMR in Drug Discovery and Development. 2013;341-368.

    Not in our library

  72. 72.

    Parkinson DR, Johnson BE, Sledge GW. Making personalized cancer medicine a reality: challenges and opportunities in the development of biomarkers and companion diagnostics. Clin Cancer Res. 2012;18:619-624.

    Not in our library10.1158/1078-0432.ccr-11-2017

  73. 73.

    Gonzalez de Castro D, Clarke PA, Al-Lazikani B, Workman P. Personalized cancer medicine: molecular diagnostics, predictive biomarkers, and drug resistance. Clin Pharmacol Ther. 2013;93:252-259.

    Not in our library10.1038/clpt.2012.237

  74. 74.

    Hayes DF. Biomarker validation and testing. Mol Oncol. 2015;9:960-966.

    Not in our library10.1016/j.molonc.2014.10.004

  75. 75.

    Schilsky RL. Personalized medicine in oncology: the future is now. Nat Rev Drug Discov. 2010;9:363-366.

    Not in our library10.1038/nrd3181

  76. 76.

    Cai W, Chen X. Multimodality molecular imaging of tumor angiogenesis. J Nucl Med. 2008;49(Suppl. 2):113S-128S.

    Not in our library10.2967/jnumed.107.045922

  77. 77.

    Statistics–vocabulary and symbols–part 2: applied statistics.

    Not in our library

  78. 78.

    Barnes SL, Whisenant JG, Loveless ME, Yankeelov TE. Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation. Pharmaceutics. 2012;4:442-478.

    Not in our library10.3390/pharmaceutics4030442

  79. 79.

    Jackson A, et al. Reproducibility of quantitative dynamic contrast-enhanced MRI in newly presenting glioma. Br J Radiol. 2003;76:153-162.

    Not in our library10.1259/bjr/70653746

  80. 80.

    Galbraith SM, et al. Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR Biomed. 2002;15:132-142.

    Not in our library10.1002/nbm.731

  81. 81.

    Koh DM, et al. Reproducibility and changes in the apparent diffusion coefficients of solid tumours treated with combretastatin A4 phosphate and bevacizumab in a two-centre phase I clinical trial. Eur Radiol. 2009;19:2728-2738.

    Not in our library10.1007/s00330-009-1469-4

  82. 82.

    O’Connor JP, et al. Quantifying antivascular effects of monoclonal antibodies to vascular endothelial growth factor: insights from imaging. Clin Cancer Res. 2009;15:6674-6682.

    Not in our library10.1158/1078-0432.ccr-09-0731

  83. 83.

    Haacke EM, et al. New algorithm for quantifying vascular changes in dynamic contrast-enhanced MRI independent of absolute T1 values. Magn Reson Med. 2007;58:463-472.

    Not in our library10.1002/mrm.21358

  84. 84.

    Leach MO, et al. Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet. 2005;365:1769-1778.

    Not in our library10.1016/s0140-6736(05)66481-1

  85. 85.

    Kessler LG, et al. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res. 2015;24:9-26.

    Not in our library10.1177/0962280214537333

  86. 86.

    Lewis M, et al. Quality control within the multicentre perfusion CT study of primary colorectal cancer (PROSPeCT): results of an iodine density phantom study. Eur Radiol. 2014;24:2309-2318.

    Not in our library10.1007/s00330-014-3258-y

  87. 87.

    Henoumont C, Laurent S, Vander Elst L. How to perform accurate and reliable measurements of longitudinal and transverse relaxation times of MRI contrast media in aqueous solutions. Contrast Media Mol Imaging. 2009;4:312-321.

    Not in our library10.1002/cmmi.294

  88. 88.

    McShane LM, Hayes DF. Publication of tumor marker research results: the necessity for complete and transparent reporting. J Clin Oncol. 2012;30:4223-4232.

    Not in our library10.1200/jco.2012.42.6858

  89. 89.

    McShane LM, et al. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97:1180-1184.

    Not in our library10.1093/jnci/dji237

  90. 90.

    Chand M, et al. The prognostic significance of postchemoradiotherapy high-resolution MRI and histopathology detected extramural venous invasion in rectal cancer. Ann Surg. 2015;261:473-479.

    Not in our library10.1097/sla.0000000000000848

  91. 91.

    Aerts HJ, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

    Not in our library10.1038/ncomms5006

  92. 92.

    Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med. 1989;8:431-440.

    Not in our library10.1002/sim.4780080407

  93. 93.

    Freedman LS, Graubard BI, Schatzkin A. Statistical validation of intermediate endpoints for chronic diseases. Stat Med. 1992;11:167-178.

    Not in our library10.1002/sim.4780110204

  94. 94.

    Buyse M, Sargent DJ, Grothey A, Matheson A, de Gramont A. Biomarkers and surrogate end points—the challenge of statistical validation. Nat Rev Clin Oncol. 2010;7:309-317.

    Not in our library10.1038/nrclinonc.2010.43

  95. 95.

    Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295-300.

    Not in our library10.1177/003591576505800503

  96. 96.

    Wagner JA. Strategic approach to fit-for-purpose biomarkers in drug development. Annu Rev Pharmacol Toxicol. 2008;48:631-651.

    Not in our library10.1146/annurev.pharmtox.48.113006.094611

  97. 97.

    Maxwell RJ, et al. Evaluation of the anti-vascular effects of combretastatin in rodent tumours by dynamic contrast enhanced MRI. NMR Biomed. 2002;15:89-98.

    Not in our library10.1002/nbm.754

  98. 98.

    LoRusso PM, et al. Phase I clinical evaluation of ZD6126, a novel vascular-targeting agent, in patients with solid tumors. Invest New Drugs. 2008;26:159-167.

    Not in our library10.1007/s10637-008-9112-9

  99. 99.

    Drevs J, et al. Phase I clinical study of AZD2171, an oral vascular endothelial growth factor signaling inhibitor, in patients with advanced solid tumors. J Clin Oncol. 2007;25:3045-3054.

    Not in our library10.1200/jco.2006.07.2066

  100. 100.

    Jonker DJ, et al. A phase I study to determine the safety, pharmacokinetics and pharmacodynamics of a dual VEGFR and FGFR inhibitor, brivanib, in patients with advanced or metastatic solid tumors. Ann Oncol. 2011;22:1413-1419.

    Not in our library10.1093/annonc/mdq599

  101. 101.

    Gazelle GS, McMahon PM, Siebert U, Beinfeld MT. Cost-effectiveness analysis in the assessment of diagnostic imaging technologies. Radiology. 2005;235:361-370.

    Not in our library10.1148/radiol.2352040330

  102. 102.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

  103. 103.

    O’Connor JP, et al. Quantitative imaging biomarkers in the clinical development of targeted therapeutics: current and future perspectives. Lancet Oncol. 2008;9:766-776.

    Not in our library10.1016/s1470-2045(08)70196-7

  104. 104.

    Micheel CM, Ball JR. Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease. 2010.

    Not in our library

  105. 105.

    Qualification opinion. 2015.

    Not in our library

  106. 106.

    Polley MY, et al. Statistical and practical considerations for clinical evaluation of predictive biomarkers. J Natl Cancer Inst. 2013;105:1677-1683.

    Not in our library10.1093/jnci/djt282

  107. 107.

    Sargent DJ, et al. Validation of novel imaging methodologies for use as cancer clinical trial end-points. Eur J Cancer. 2009;45:290-299.

    Not in our library10.1016/j.ejca.2008.10.030

  108. 108.

    Biomarker qualification program.

    Not in our library

  109. 109.

    Dietrich CF, et al. An EFSUMB introduction into Dynamic Contrast-Enhanced Ultrasound (DCE-US) for quantification of tumour perfusion. Ultraschall Med. 2012;33:344-351.

    Not in our library10.1055/s-0032-1313026

  110. 110.

    Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307-310.

    Not in our library

  111. 111.

    Lee TY, et al. CT perfusion as an early biomarker of treatment efficacy in advanced ovarian cancer: An ECOG-ACRIN and NRG GOG study 9 [abstract]. J Clin Oncol. 2015;33:5522.

    Not in our library10.1158/1078-0432.ccr-16-1859

  112. 112.

    Chu R, et al. Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: a simulation study. BMC Med Res Methodol. 2011;11:21.

    Not in our library10.1186/1471-2288-11-21

  113. 113.

    Nuyts J, et al. Cardiac phantom measurement validating the methodology for a cardiac multi-centre trial with positron emission tomography. Eur J Nucl Med Mol Imaging. 2002;29:1588-1593.

    Not in our library10.1007/s00259-002-0940-0

  114. 114.

    Yankeelov TE, et al. Quantitative imaging in cancer clinical trials. Clin Cancer Res. 2016;22:284-290.

    Not in our library10.1158/1078-0432.ccr-14-3336

  115. 115.

    O’Connor JPB, et al. Oxygen-enhanced MRI accurately identifies, quantifies, and maps tumor hypoxia in preclinical cancer models. Cancer Res. 2016;76:787-795.

    Not in our library10.1158/0008-5472.can-15-2062

  116. 116.

    Workman P, et al. Guidelines for the welfare and use of animals in cancer research. Br J Cancer. 2010;102:1555-1577.

    Not in our library10.1038/sj.bjc.6605642

  117. 117.

    Frese KK, Tuveson DA. Maximizing mouse cancer models. Nat Rev Cancer. 2007;7:645-658.

    Not in our library10.1038/nrc2192

  118. 118.

    Nardella C, Lunardi A, Patnaik A, Cantley LC, Pandolfi PP. The APL paradigm and the “co-clinical trial” project. Cancer Discov. 2011;1:108-116.

    Not in our library10.1158/2159-8290.cd-11-0061

  119. 119.

    Sharpless NE, Depinho RA. The mighty mouse: genetically engineered mouse models in cancer drug development. Nat Rev Drug Discov. 2006;5:741-754.

    Not in our library10.1038/nrd2110

  120. 120.

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

    Not in our library10.1093/jnci/dju033

  121. 121.

    Hidalgo M, et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 2014;4:998-1013.

    Not in our library10.1158/2159-8290.cd-14-0001

  122. 122.

    Hodgkinson CL, et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat Med. 2014;20:897-903.

    Not in our library10.1038/nm.3600

  123. 123.

    Malaney P, Nicosia SV, Dave V. One mouse, one patient paradigm: new avatars of personalized cancer therapy. Cancer Lett. 2014;344:1-12.

    Not in our library10.1016/j.canlet.2013.10.010

  124. 124.

    Oncology co-clinical imaging research resources to encourage consensus on quantitative imaging methods and precision medicine (U24).

    Not in our library

  125. 125.

    Wason J, Marshall A, Dunn J, Stein RC, Stallard N. Adaptive designs for clinical trials assessing biomarker-guided treatment strategies. Br J Cancer. 2014;110:1950-1957.

    Not in our library10.1038/bjc.2014.156

  126. 126.

    Gould AL. Sample size re-estimation: recent developments and practical considerations. Stat Med. 2001;20:2625-2643.

    Not in our library10.1002/sim.733

  127. 127.

    Jennison C, Turnbull BW. Group Sequential Methods with Applications to Clinical Trials. 1999.

    Not in our library

  128. 128.

    Gerety EL, et al. Prospective study evaluating the relative sensitivity of 18F-NaF PET/CT for detecting skeletal metastases from renal cell carcinoma in comparison to multidetector CT and 99mTc-MDP bone scintigraphy, using an adaptive trial design. Ann Oncol. 2015;26:2113-2118.

    Not in our library10.1093/annonc/mdv289

  129. 129.

    O’Connor JP, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res. 2015;21:249-257.

    Not in our library10.1158/1078-0432.ccr-14-0990

  130. 130.

    Halle C, et al. Hypoxia-induced gene expression in chemoradioresistant cervical cancer revealed by dynamic contrast-enhanced MRI. Cancer Res. 2012;72:5285-5295.

    Not in our library10.1158/0008-5472.can-12-1085

  131. 131.

    Informatics technology for cancer research.

    Not in our library

  132. 132.

    ESR position paper on imaging biobanks. Insights Imaging. 2015;6:403-410.

    Not in our library10.1007/s13244-015-0409-x

  133. 133.

    Bernsen MR, Kooiman K, Segbers M, van Leeuwen FW, de Jong M. Biomarkers in preclinical cancer imaging. Eur J Nucl Med Mol Imaging. 2015;42:579-596.

    Not in our library10.1007/s00259-014-2980-7

  134. 134.

    Boult JK, et al. False-negative MRI biomarkers of tumour response to targeted cancer therapeutics. Br J Cancer. 2012;106:1960-1966.

    Not in our library10.1038/bjc.2012.208

  135. 135.
  136. 136.

    O’Connor JP, Jayson GC. Do imaging biomarkers relate to outcome in patients treated with VEGF inhibitors? Clin Cancer Res. 2012;18:6588-6598.

    Not in our library10.1158/1078-0432.ccr-12-1501

  137. 137.

    Stone M. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Series B Stat Methodol. 1974;36:111-147.

    Not in our library

  138. 138.

    Ein-Dor L, Zuk O, Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci USA. 2006;103:5923-5928.

    Not in our library10.1073/pnas.0601231103

  139. 139.

    Meads C, et al. Positron emission tomography/computerised tomography imaging in detecting and managing recurrent cervical cancer: systematic review of evidence, elicitation of subjective probabilities and economic modelling. Health Technol Assess. 2013;17:1-323.

    Not in our library10.3310/hta17120

  140. 140.

    Mowatt G, et al. The diagnostic accuracy and cost-effectiveness of magnetic resonance spectroscopy and enhanced magnetic resonance imaging techniques in aiding the localisation of prostate abnormalities for biopsy: a systematic review and economic evaluation. Health Technol Assess. 2013;17:1-281.

    Not in our library10.3310/hta17200

  141. 141.

    Lawler M, Kaplan R, Wilson RH, Maughan T. Changing the paradigm-multistage multiarm randomized trials and stratified cancer medicine. Oncologist. 2015;20:849-851.

    Not in our library10.1634/theoncologist.2015-0014

  142. 142.

    Wason JM, et al. A Bayesian adaptive design for biomarker trials with linked treatments. Br J Cancer. 2015;113:699-705.

    Not in our library10.1038/bjc.2015.278

  143. 143.

    Moher D, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c869.

    Not in our library10.1136/bmj.c869

  144. 144.

    Griebsch I, et al. Cost-effectiveness of screening with contrast enhanced magnetic resonance imaging versus X-ray mammography of women at a high familial risk of breast cancer. Br J Cancer. 2006;95:801-810.

    Not in our library10.1038/sj.bjc.6603356

  145. 145.

    Looking back on the millennium in medicine. N Engl J Med. 2000;342:42-49.

    Not in our library10.1056/nejm200001063420108

  146. 146.

    McLeod DG, et al. Bicalutamide 150 mg plus standard care versus standard care alone for early prostate cancer. BJU Int. 2005;97:247-254.

    Not in our library10.1111/j.1464-410x.2005.06051.x

  147. 147.

    WHO Handbook for Reporting Results of Cancer Treatment. 1979.

    Not in our library

  148. 148.

    Choi H, et al. Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol. 2007;25:1753-1759.

    Not in our library10.1200/jco.2006.07.3049

  149. 149.

    Zagar TM, Cardinale DM, Marks LB. Breast cancer therapy-associated cardiovascular disease. Nat Rev Clin Oncol. 2016;13:172-184.

    Not in our library10.1038/nrclinonc.2015.171

  150. 150.

    Herceptin. Highlights of prescribing information.

    Not in our library

  151. 151.

    Avram AM. Radioiodine scintigraphy with SPECT/CT: an important diagnostic tool for thyroid cancer staging and risk stratification. J Nucl Med. 2012;53:754-764.

    Not in our library10.2967/jnumed.111.104133

  152. 152.

    Kam BL, et al. Lutetium-labelled peptides for therapy of neuroendocrine tumours. Eur J Nucl Med Mol Imaging. 2012;39(Suppl. 1):S103-S112.

    Not in our library10.1007/s00259-011-2039-y

  153. 153.

    Ambrosini V, Nanni C, Fanti S. The use of gallium-68 labeled somatostatin receptors in PET/CT imaging. PET Clin. 2014;9:323-329.

    Not in our library10.1016/j.cpet.2014.03.008

  154. 154.

    Familial breast cancer: classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer. 2013.

    Not in our library

  155. 155.

    Summary of opinion.

    Not in our library

  156. 156.

    Quarles van Ufford HM, et al. Added value of baseline 18F-FDG uptake in serial 18F-FDG PET for evaluation of response of solid extracerebral tumors to systemic cytotoxic neoadjuvant treatment: a meta-analysis. J Nucl Med. 2010;51:1507-1516.

    Not in our library10.2967/jnumed.110.075457

  157. 157.

    Lara PN, et al. SU5416 plus interferon alpha in advanced renal cell carcinoma: a phase II California Cancer Consortium Study with biological and imaging correlates of angiogenesis inhibition. Clin Cancer Res. 2003;9:4772-4781.

    Not in our library

  158. 158.

    Bendell JC, et al. Phase I, dose-escalation study of BKM120, an oral pan-Class I PI3K inhibitor, in patients with advanced solid tumors. J Clin Oncol. 2012;30:282-290.

    Not in our library10.1200/jco.2011.36.1360

  159. 159.

    Maynard J, Ricketts SA, Gendrin C, Dudley P, Davies BR. 2-Deoxy-2-[18F]fluoro-D-glucose positron emission tomography demonstrates target inhibition with the potential to predict anti-tumour activity following treatment with the AKT inhibitor AZD5363. Mol Imaging Biol. 2013;15:476-485.

    Not in our library10.1007/s11307-013-0613-3

  160. 160.

    Engelman JA. Targeting PI3K signalling in cancer: opportunities, challenges and limitations. Nat Rev Cancer. 2009;9:550-562.

    Not in our library10.1038/nrc2664

  161. 161.

    Kierkels RG, et al. Comparison between perfusion computed tomography and dynamic contrast-enhanced magnetic resonance imaging in rectal cancer. Int J Radiat Oncol Biol Phys. 2010;77:400-408.

    Not in our library10.1016/j.ijrobp.2009.05.015

  162. 162.

    Naish JH, et al. Comparison of dynamic contrast-enhanced MRI and dynamic contrast-enhanced CT biomarkers in bladder cancer. Magn Reson Med. 2011;66:219-226.

    Not in our library10.1002/mrm.22774

  163. 163.

    Ng CS, et al. Dependence of DCE-MRI biomarker values on analysis algorithm. PLoS ONE. 2015;10:e0130168.

    Not in our library10.1371/journal.pone.0130168

  164. 164.

    Sabbatini P, et al. Prognostic significance of extent of disease in bone in patients with androgen-independent prostate cancer. J Clin Oncol. 1999;17:948-957.

    Not in our library10.1200/jco.1999.17.3.948

  165. 165.

    510(k) summary.

    Not in our library

  166. 166.

    FDA approves Lymphoseek to help locate lymph nodes in patients with certain cancers.

    Not in our library

  167. 167.

    Withdrawn applications, vynfinit.

    Not in our library

  168. 168.

    Lassau N, et al. Validation of dynamic contrast-enhanced ultrasound in predicting outcomes of antiangiogenic therapy for solid tumors: the French multicenter support for innovative and expensive techniques study. Invest Radiol. 2014;49:794-800.

    Not in our library10.1097/rli.0000000000000085

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