Skip to main content

How Can Radiomics Improve Clinical Choices?

  • Chapter
  • First Online:
Multidisciplinary Management of Rectal Cancer

Abstract

Over the past decade, we have witnessed a great expansion of the use and the role of medical imaging technologies in clinical oncology from a primarily diagnostic, qualitative tool to include a central role in the context of individualized medicine, with a dominant quantitative value [1].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. Lambin P, van Stiphout RGPM, Starmans MHW, Rios-Velazquez E, Nalbantov G, Aerts HJWL et al (2013) Predicting outcomes in radiation oncology – multifactorial decision support systems. Nat Rev Clin Oncol [Internet], Nature Publishing Group 10(1):27–40. [cited 2014 Mar 26]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23165123

  2. O’Connor JPB, Rose CJ, Waterton JC, Carano RAD, Parker GJM, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res [Internet] 21(2):249–57. [cited 2016 Mar 23]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4688961&tool=pmcentrez&rendertype=abstract

  3. Kurland BF, Gerstner ER, Mountz JM, Schwartz LH, Ryan CW, Graham MM et al (2012) Promise and pitfalls of quantitative imaging in oncology clinical trials. Magn Reson Imaging [Internet] 30(9):1301–12. [cited 2016 Mar 23]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3466405&tool=pmcentrez&rendertype=abstract

  4. Levy MA, Freymann JB, Kirby JS, Fedorov A, Fennessy M, Eschrich SA et al (2013) NIH public access. Magn Reson Imaging 30(9):1249–1256

    Article  Google Scholar 

  5. Clarke LP, Nordstrom RJ, Zhang H, Tandon P, Zhang Y, Redmond G et al (2014) The quantitative imaging network: NCI’s historical perspective and planned goals. Transl Oncol [Internet] 7(1):1–4. [cited 2016 Mar 24]. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1936523314800016

  6. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med [Internet] 366(10):883–92. [cited 2014 Aug 6]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22397650

  7. Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology [Internet] 269(1):8–15. [cited 2016 Mar 23]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3781355&tool=pmcentrez&rendertype=abstract

  8. Alic L, Niessen WJ, Veenland JF (2014) Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One [Internet] 9(10):e110300. [cited 2016 Mar 3]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4203782&tool=pmcentrez&rendertype=abstract

  9. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging [Internet], Elsevier Inc. 30(9):1234–48. [cited 2014 Mar 28]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3563280&tool=pmcentrez&rendertype=abstract

  10. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer [Internet] 48(4):441–6. [cited 2014 Mar 26]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22257792

  11. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology [Internet] 278(2):563–77. [cited 2016 Feb 12]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26579733

  12. Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A (2014) The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol [Internet] 87(1042):20140369. [cited 2016 Mar 24]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4170870&tool=pmcentrez&rendertype=abstract

  13. Davnall F, Yip CSP, Ljungqvist G, Selmi M, Ng F, Sanghera B et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging [Internet] 3(6):573–89. [cited 2016 Feb 19]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3505569&tool=pmcentrez&rendertype=abstract

  14. Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging [Internet] 13:140–9. [cited 2016 Mar 25]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3613789&tool=pmcentrez&rendertype=abstract

  15. Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging [Internet] 13(3):400–6. [cited 2016 Mar 28]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3781643&tool=pmcentrez&rendertype=abstract

  16. Miles KA, Ganeshan B, Rodriguez-Justo M, Goh VJ, Ziauddin Z, Engledow A et al (2014) Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer. J Nucl Med [Internet] 55(3):386–91. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24516257

  17. Song B, Zhang G, Lu H, Wang H, Zhu W, Pickhardt JP et al (2014) Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography. Int J Comput Assist Radiol Surg [Internet]. [cited 2016 Mar 28]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4185018&tool=pmcentrez&rendertype=abstract

  18. Cui C, Cai H, Liu L, Li L, Tian H, Li L (2011) Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. Eur Radiol [Internet] 21(11):2318–25. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21713526

  19. Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol [Internet] 14(12):1520–30. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18035281

  20. Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol [Internet] 62(8):761–8. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17604764

  21. Rao S-X, Lambregts DM, Schnerr RS, van Ommen W, van Nijnatten TJ, Martens MH et al (2014) Whole-liver CT texture analysis in colorectal cancer: does the presence of liver metastases affect the texture of the remaining liver? United Eur Gastroenterol J [Internet] 2(6):530–8. [cited 2016 Mar 28]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4245301&tool=pmcentrez&rendertype=abstract

  22. Ganeshan B, Burnand K, Young R, Chatwin C, Miles K (2011) Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Invest Radiol [Internet] 46(3):160–8. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21102348

  23. Ganeshan B, Miles KA, Young RCD, Chatwin CR (2009) Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol [Internet] 70(1):101–10. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18242909

  24. Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol [Internet] 14(9):1058–68. [cited 2016 Mar 28]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17707313

  25. Lubner MG, Stabo N, Lubner SJ, Del Rio AM, Song C, Halberg RB et al (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging [Internet], Springer US 40(7):2331–7. [cited 2016 Mar 26]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25968046

  26. Valentini V, van Stiphout RGPM, Lammering G, Gambacorta MA, Barba MC, Bebenek M et al (2015) Selection of appropriate end-points (pCR vs 2yDFS) for tailoring treatments with prediction models in locally advanced rectal cancer. Radiother Oncol [Internet] 114(3):302–9. [cited 2016 Apr 1]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25716096

  27. De Cecco CN, Ganeshan B, Ciolina M, Rengo M, Meinel FG, Musio D et al (2015) Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol [Internet] 50(4):239–45. [cited 2016 Apr 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25501017

  28. Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsótér N, Papp L et al (2014) Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med [Internet] 55(6):891–7. [cited 2016 Apr 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24752672

  29. Bang J-I, Ha S, Kang S-B, Lee K-W, Lee H-S, Kim J-S et al (2016) Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging [Internet] 43(3):422–31. [cited 2016 Apr 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26338180

  30. O’Connor JPB, Rose CJ, Jackson A, Watson Y, Cheung S, Maders F et al (2011) DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. Br J Cancer [Internet] 105(1):139–45. [cited 2016 Apr 4]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3137409&tool=pmcentrez&rendertype=abstract

  31. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology [Internet] 266(1):177–84. [cited 2016 Apr 5]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23151829

  32. Miles KA, Ganeshan B, Griffiths MR, Young RCD, Chatwin CR (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology [Internet] 250(2):444–52. [cited 2016 Apr 5]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19164695

  33. Doroshow JH, Kummar S (2014) Translational research in oncology – 10 years of progress and future prospects. Nat Rev Clin Oncol [Internet], Nature Publishing Group 11(11):649–62. [cited 2016 Apr 6].Available from:http://www.ncbi.nlm.nih.gov/pubmed/25286976

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roberto Gatta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer-Verlag Berlin Heidelberg

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Meldolesi, E., Dinapoli, N., Gatta, R., Damiani, A., Valentini, V., Farchione, A. (2018). How Can Radiomics Improve Clinical Choices?. In: Valentini, V., Schmoll, HJ., van de Velde, C. (eds) Multidisciplinary Management of Rectal Cancer. Springer, Cham. https://doi.org/10.1007/978-3-319-43217-5_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43217-5_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43215-1

  • Online ISBN: 978-3-319-43217-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics