Advertisement

Towards Automated Reporting and Visualization of Lymph Node Metastases of Lung Cancer

  • Nico MertenEmail author
  • Philipp Genseke
  • Bernhard Preim
  • Michael C. Kreissl
  • Sylvia Saalfeld
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

For lung cancer staging, the involvement of lymph nodes in the mediastinum, meaning along the trachea and bronchi, has to be assessed. Depending on the staging results, treatment options include radiation therapy, chemotherapy, or lymph node resection. We present a processing pipeline to automatically generate visualization-supported case reports to simplify reporting and to improve interdisciplinary communication, e. g. between nuclear medicine physicians, radiologists, radiation oncologists, and thoracic surgeons. To evaluate our method, we obtained detailed feedback from the local division of nuclear medicine: Although patient-specific anatomy was not yet considered, the presented approach was deemed to be highly useful from a clinical perspective.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Bray F, Ferlay J, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;.Google Scholar
  2. 2.
    Popper HH. Progression and metastasis of lung cancer. Cancer Metastasis Rev. 2016;35(1):75–91.CrossRefGoogle Scholar
  3. 3.
    Mountain CF, Dresler CM. Regional lymph node classification for lung cancer staging. Dis Chest. 1997;111(6):1718–1723.CrossRefGoogle Scholar
  4. 4.
    Rusch VW, Asamura H, et al. The IASLC lung cancer staging project: a proposal for a new international lymph node map in the forthcoming 7th edition of the TNM classification for lung cancer. J Thorac Oncol. 2009;4(5):568–577.CrossRefGoogle Scholar
  5. 5.
    Rössling I, Dornheim J, et al. The tumor therapy Mmanager: design, refinement and clinical use of a software product for ENT surgery planning and documentation. Proc IPCAI. 2011; p. 1–12.Google Scholar
  6. 6.
    Birr S, Dicken V, et al. 3D-PDF: ein interaktives Tool für das onkologische Reporting und die Operationsplanung von Lungentumoren. Proc CURAC. 2011; p. 11–16.Google Scholar
  7. 7.
    Ritter F, Boskamp T, et al. Medical image analysis. IEEE Pulse. 2011;2(6):60–70.CrossRefGoogle Scholar
  8. 8.
    Selle D, Preim B, et al. Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging. 2002;21(11):1344–1357.CrossRefGoogle Scholar
  9. 9.
    Bade R, Konrad O, et al. Reducing artifacts in surface meshes extracted from binary volumes. J WSCG. 2007;15(1-3):67–74.Google Scholar
  10. 10.
    Barta P, Kovács B, et al. Order independent transparency with per-pixel linked lists. Budap Univ Technol Econ. 2011;.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Nico Merten
    • 1
    • 2
    Email author
  • Philipp Genseke
    • 3
  • Bernhard Preim
    • 1
    • 2
  • Michael C. Kreissl
    • 1
    • 3
  • Sylvia Saalfeld
    • 1
    • 2
  1. 1.Research Campus StimulateMagdeburgDeutschland
  2. 2.Department of and GraphicsOtto-von-Guericke UniversityMagdeburgDeutschland
  3. 3.Department of Radiology and Nuclear MedicineUniversity Hospital MagdeburgMagdeburgDeutschland

Personalised recommendations