Radiotherapy & Oncology
Volume 97, Issue 3 , Pages 572-578, December 2010

Tools for consensus analysis of experts’ contours for radiotherapy structure definitions

  • Rawan Allozi

      Affiliations

    • Washington University, Saint Louis, MO, USA
  • ,
  • X. Allen Li

      Affiliations

    • Medical College Wisconsin, Milwaukee, WI, USA
  • ,
  • Julia White

      Affiliations

    • Medical College Wisconsin, Milwaukee, WI, USA
  • ,
  • Aditya Apte

      Affiliations

    • Washington University, Saint Louis, MO, USA
  • ,
  • An Tai

      Affiliations

    • Medical College Wisconsin, Milwaukee, WI, USA
  • ,
  • Jeff M. Michalski

      Affiliations

    • Washington University, Saint Louis, MO, USA
  • ,
  • Walter R. Bosch

      Affiliations

    • Washington University, Saint Louis, MO, USA
  • ,
  • Issam El Naqa

      Affiliations

    • Washington University, Saint Louis, MO, USA
    • Corresponding Author InformationCorresponding author. Current address: Department of Radiation Oncology, Washington University School of Medicine, Campus-Box 8224, St. Louis, MO 63110, USA.

Received 26 January 2010; received in revised form 15 June 2010; accepted 22 June 2010. published online 13 August 2010.

Abstract 

Background and purpose

To demonstrate and examine the ability of a newly developed software tool to estimate and analyze consensus contours from manually created contours by expert radiation oncologists.

Material and methods

Several statistical methods and a graphical user interface were developed. For evaluation purposes, we used three breast cancer CT scans from the RTOG Breast Cancer Atlas Project. Specific structures were contoured before and after the experts’ consensus panel meeting. Differences in the contours were evaluated qualitatively and quantitatively by the consensus software tool. Estimates of consensus contours were analyzed for the different structures and Dice-similarity and Dice-Jaccard coefficients were used for comparative evaluation.

Results

Based on kappa statistics, highest levels of agreement were seen in the left-breast, lumpectomy, and heart. Significant improvements between pre- and post-consensus contours were seen in delineation of the chestwall and breasts while significant variations were noticed in the supraclavicular and internal mammary nodes. Dice calculations for all pre-consensus STAPLE estimations and final consensus panel structures reached 0.80 or greater for the heart, left/right-breast, case-A lumpectomy, and chestwall.

Conclusions

Using the consensus software tool incorporating STAPLE estimates provided the ability to create contours similar to the ones generated by expert physicians.

Keywords: Structure definition, Radiotherapy treatment planning, Experts’ consensus, Statistical modeling, Software tools

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PII: S0167-8140(10)00393-2

doi:10.1016/j.radonc.2010.06.009

Radiotherapy & Oncology
Volume 97, Issue 3 , Pages 572-578, December 2010