Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy

  • Bulat IbragimovEmail author
  • Diego A. S. Toesca
  • Yixuan Yuan
  • Albert C. Koong
  • Daniel T. Chang
  • Lei Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.


  1. 1.
    Siegel, R.L., et al.: Cancer statistics, 2017. Cancer J. Clin. 67, 7–30 (2017)CrossRefGoogle Scholar
  2. 2.
    Toesca, D.A.S., Osmundson, E.C., von Eyben, R., Shaffer, J.L., Lu, P., Koong, A.C., Chang, D.T.: Central liver toxicity after SBRT: an expanded analysis and predictive nomogram. Radiother. Oncol. 122(1), 130–136 (2016)CrossRefGoogle Scholar
  3. 3.
    van Luijk, P., et al.: Sparing the region of the salivary gland containing stem cells preserves saliva production after radiotherapy for head and neck cancer. Sci. Transl. Med. 7(305), 147 (2015)Google Scholar
  4. 4.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)CrossRefGoogle Scholar
  5. 5.
    Murphy, K., et al.: Evaluation of registration methods on Thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)CrossRefGoogle Scholar
  6. 6.
    Agarwal, A., et al.: An introduction to computational networks and the computational network toolkit. Microsoft Technical report MSR-TR-2014-112 (2014)zbMATHGoogle Scholar
  7. 7.
    Ibragimov, B., Likar, B., Pernus, F., Vrtovec, T.: Shape representation for efficient landmark-based segmentation in 3D. IEEE Trans. Med. Imaging 33, 861–874 (2014)CrossRefGoogle Scholar
  8. 8.
    Multi-atlas labeling beyond the cranial vault - workshop and challenge - syn3193805.!Synapse:syn3193805/wiki/. Accessed 14 Sep 2017
  9. 9.
    Andratschke, N.H., Nieder, C., Heppt, F., Molls, M., Zimmermann, F.: Stereotactic radiation therapy for liver metastases: factors affecting local control and survival. Radiother. Oncol. 10, 69 (2015)CrossRefGoogle Scholar
  10. 10.
    Zhen, X., et al.: Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys. Med. Biolog. 62(21), 8246–8263 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bulat Ibragimov
    • 1
    Email author
  • Diego A. S. Toesca
    • 1
  • Yixuan Yuan
    • 1
    • 2
  • Albert C. Koong
    • 3
  • Daniel T. Chang
    • 1
  • Lei Xing
    • 1
  1. 1.Department of Radiation OncologyStanford UniversityStanfordUSA
  2. 2.Department of Electronic EngineeringCity Univeristy of Hong KongHong KongChina
  3. 3.Department of Radiation OncologyMD Anderson Cancer CenterHoustonUSA

Personalised recommendations