Image-based biomarkers for solid tumor quantification

  • Peter Savadjiev
  • Jaron Chong
  • Anthony Dohan
  • Vincent Agnus
  • Reza Forghani
  • Caroline Reinhold
  • Benoit GallixEmail author


The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.

Key Points

Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.

Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.

We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.


Diagnostic imaging Biomarkers Artificial intelligence (AI) Computer-assisted image processing Computer-assisted image interpretation 



18F-fluorodeoxyglucose positron emission tomography


Artificial intelligence


Convolutional neural network


European Association for the Study of the Liver


Modified Response Evaluation Criteria in Solid Tumors


Positron Emission Tomography Response Criteria in Solid Tumors


Response Evaluation Criteria in Solid Tumors


World Health Organization



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Benoit Gallix.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because this is a review article, and no study was performed.

Ethical approval

Institutional review board approval was not required because this is a review article, and no study was performed.


  1. 1.
    Food and Drug Administration & National Institutes of Health. BEST (biomarkers, endpoints, and other tools) resource. NCBI Accessed on 10 Jan 2019.
  2. 2.
    Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine. A review. JAMA Oncol 2(12):1636–1642CrossRefGoogle Scholar
  3. 3.
    Amin S, Bathe OF (2016) Response biomarkers: re-envisioning the approach to tailoring drug therapy for cancer. BMC Cancer 16:850CrossRefGoogle Scholar
  4. 4.
    Harry VN, Semple SI, Parkin DE, Gilbert FJ (2010) Use of new imaging techniques to predict tumour response to therapy. Lancet Oncol 11:92–102CrossRefGoogle Scholar
  5. 5.
    O’Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14(3):169–186Google Scholar
  6. 6.
    Savadjiev P, Chong J, Dohan A, et al (2019) Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol 29(3):1616–1624Google Scholar
  7. 7.
    World Health Organization. ( 1979) . WHO handbook for reporting results of cancer treatment. World Health Organization. Geneva, Switzerland
  8. 8.
    Miller AB, Hoogstraten B, Staquet M, Winkler A (1981) Reporting results of cancer treatment. Cancer 47(1):207–214CrossRefGoogle Scholar
  9. 9.
    Therasse P, Arbuck SG, Eisenhauer EA et al (2000) New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 92(3):205–216Google Scholar
  10. 10.
    Shah GD, Kesari S, Xu R, et al (2006) Comparison of linear and volumetric criteria in assessing tumor response in adult high-grade gliomas. Neuro Oncol 8(1):38–46Google Scholar
  11. 11.
    Dempsey MF, Condon BR, Hadley DM (2005) Measurement of tumor “size” in recurrent malignant glioma: 1D, 2D, or 3D? AJNR Am J Neuroradiol 26(4):770–776Google Scholar
  12. 12.
    Aghighi M, Boe J, Rosenberg J et al (2016) Three-dimensional radiologic assessment of chemotherapy response in Ewing sarcoma can be used to predict clinical outcome. Radiology 280(3):905–915CrossRefGoogle Scholar
  13. 13.
    Lubner MG, Stabo N, Lubner SJ, Del Rio AM, Song C, Pickhardt PJ (2017) Volumetric versus unidimensional measures of metastatic colorectal cancer in assessing disease response. Clin Colorectal Cancer 16(4):324–333CrossRefGoogle Scholar
  14. 14.
    Galanis E, Buckner JC, Maurer MJ et al (2006) Validation of neuroradiologic response assessment in gliomas: measurement by RECIST, two-dimensional, computerassisted tumor area, and computer-assisted tumor volume methods. Neuro Oncol 8(2):156–165Google Scholar
  15. 15.
    Jaffe CC (2006) Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol 24(20):3245–3251CrossRefGoogle Scholar
  16. 16.
    Atri M (2006) New technologies and directed agents for applications of cancer imaging. J Clin Oncol 24(20):3299–3308CrossRefGoogle Scholar
  17. 17.
    Bruix J, Sherman M, Llovet JM et al (2001) Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver. J Hepatol 35(3):421–430CrossRefGoogle Scholar
  18. 18.
    Lencioni R, Llovet JM (2010) Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 30:52–60CrossRefGoogle Scholar
  19. 19.
    Choi H, Charnsangavej C, Faria SC et al (2007) 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 25:1753–1759CrossRefGoogle Scholar
  20. 20.
    Maier-Hein L, Eisenmann M, Reinke A et al (2018) Why rankings of biomedical image analysis competitions should be interpreted with care. Nat Commun 9(1):5217Google Scholar
  21. 21.
    Kelloff GJ, Hoffman JM, Johnson B et al (2005) Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clin Cancer Res 11(8):2785–2808CrossRefGoogle Scholar
  22. 22.
    Wahl RL, Jacene H, Kasamon Y, Lodge MA (2009) From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 50(Suppl 1):122S–150SCrossRefGoogle Scholar
  23. 23.
    Segal E, Sirlin CB, Ooi C et al (2007) Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25:675–680CrossRefGoogle Scholar
  24. 24.
    Chun YS, Vauthey JN, Boonsirikamchai P et al (2009) Association of computed tomography morphologic criteria with pathologic response and survival in patients treated with bevacizumab for colorectal liver metastases. JAMA 302(21):2338–2344CrossRefGoogle Scholar
  25. 25.
    Jansen RW, van Amstel P, Martens RM, Kooi IE, Wesseling P, de Langen AJ (2018) Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 9(28):20134–20155CrossRefGoogle Scholar
  26. 26.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefGoogle Scholar
  27. 27.
    Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 90(1070):20160665CrossRefGoogle Scholar
  28. 28.
    O’Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21(2):249–257CrossRefGoogle Scholar
  29. 29.
    Vallières M, Freeman CR, Skamene SR, El Naq I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496CrossRefGoogle Scholar
  30. 30.
    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107CrossRefGoogle Scholar
  31. 31.
    Chamming’s F, Ueno Y, Ferré R et al (2017) Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy. Radiology.
  32. 32.
    Ueno Y, Forghani B, Forghani R et al (2017) Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification—a preliminary analysis. Radiology 284(3):748–757CrossRefGoogle Scholar
  33. 33.
    Parmar C, Leijenaar RTH, Grossmann P et al (2015) Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 5:11044CrossRefGoogle Scholar
  34. 34.
    Schad L (2004) Problems in texture analysis with magnetic resonance imaging. Dialogues Clin Neurosci 6(2):235–242Google Scholar
  35. 35.
    Schad L, Lundervold A (2006) Influence of resolution and signal to noise ratio on MR image texture. In: Hajek M, Dezortova M, Materka A, Lerski R (eds) Texture analysis for magnetic resonance imaging. HRaNa, Prague, pp 129–149Google Scholar
  36. 36.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  37. 37.
    Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  38. 38.
    Kraus WL (2015) Editorial: would you like a hypothesis with those data? Omics and the age of discovery science. Mol Endocrinol 29(11):1531–1534CrossRefGoogle Scholar
  39. 39.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  40. 40.
    Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37(7):2113–2131CrossRefGoogle Scholar
  41. 41.
    Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88CrossRefGoogle Scholar
  42. 42.
    Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18(8):500–510CrossRefGoogle Scholar
  43. 43.
    Ypsilantis PP, Siddique M, Sohn HM et al (2015) Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One 10(9):e0137036CrossRefGoogle Scholar
  44. 44.
    Li Z, Wang Y, Yu J, Guo Y, Cao W (2017) Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep 7(1):5467CrossRefGoogle Scholar
  45. 45.
    US Department of Health and Human Services. Guidance regarding methods for de-identification of protected health information in accordance with the Health Insurance Portability and Accountability Act (HIPAA) privacy rule. Accessed on 10 Jan 2019
  46. 46.
    Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. Retrieved from Google Scholar
  47. 47.
    Tajbakhsh N, Shin JY, Gurudu SR et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312CrossRefGoogle Scholar
  48. 48.
    Shin HC, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298CrossRefGoogle Scholar
  49. 49.
    Hinton G (2018) Deep learning—a technology with the potential to transform health care. JAMA 320(11):1101–1102CrossRefGoogle Scholar
  50. 50.
    Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216CrossRefGoogle Scholar
  51. 51.
    Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582CrossRefGoogle Scholar
  52. 52.
    Chang P, Grinband J, Weinberg BD et al (2018) Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol 39(7):1201–1207Google Scholar
  53. 53.
    Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the medical image computing and computer-assisted intervention. In: Lecture notes in computer science, vol 9351, pp 234–241Google Scholar
  54. 54.
    Setio AAA, Ciompi F, Litjens G et al (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35:1160–1169CrossRefGoogle Scholar
  55. 55.
    Ciompi F, Chung K, van Riel SJ et al (2017) Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep 7:46479CrossRefGoogle Scholar
  56. 56.
    Lai M (2015) Deep learning for medical image segmentation. Retrieved from Google Scholar
  57. 57.
    Bodalal Z, Trebeschi S, Beets-Tan R (2018) Radiomics: a critical step towards integrated healthcare. Insights Imaging 9(6):911–914CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Diagnostic RadiologyMcGill UniversityMontrealCanada
  2. 2.Department of Diagnostic Radiology, McGill University Health CentreMcGill UniversityMontrealCanada
  3. 3.Department of Body and Interventional Imaging, Hôpital Lariboisière-AP-HPUniversité Diderot-Paris 7 and INSERM U965Paris Cedex 10France
  4. 4.Institut de chirurgie guidée par l’image IHU StrasbourgStrasbourg CedexFrance
  5. 5.Department of RadiologyJewish General HospitalMontrealCanada

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