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A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.

Jinzhong Yang1, Beth M Beadle2, Adam S Garden2

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.

Medical Physics
|September 3, 2015
PubMed
Summary
This summary is machine-generated.

This study developed an automated segmentation algorithm using CT, PET, and MRI for head and neck cancer radiotherapy. The multimodality approach improved tumor volume delineation accuracy compared to PET alone.

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Computational Biology

Background:

  • Accurate tumor delineation is crucial for effective head and neck cancer radiotherapy.
  • Current segmentation methods can be time-consuming and may lack consistency.
  • Integrating multimodality imaging offers potential for improved target volume definition.

Purpose of the Study:

  • To develop and validate an automated segmentation algorithm for head and neck cancer radiotherapy.
  • The algorithm integrates computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) data.
  • The goal is to improve the accuracy and consistency of target volume delineation.

Main Methods:

  • An automated segmentation algorithm was developed using a multichannel Gaussian mixture model and expectation-maximization with Markov random fields.
  • Eleven patients with unresectable head and neck cancer underwent CT, PET/CT, and MRI scans.
  • Images were registered to the planning CT space, and the algorithm used resampled PET/MRI, CT, and a manual tumor mask for delineation.

Main Results:

  • The multimodality segmentation showed a median difference of -10.7% compared to physician-defined gross tumor volume (GTV), which was not statistically significant (p=0.43).
  • Segmentation using only PET images resulted in a statistically significant difference of -19.2% (p=0.0037).
  • The median Dice similarity coefficient between multimodality segmentation and physician-defined GTV was 0.75, with sensitivity of 0.76 and positive predictive value of 0.81.

Conclusions:

  • An automated multimodality segmentation algorithm was successfully developed and validated for head and neck cancer radiotherapy.
  • The developed algorithm demonstrated good agreement with physician-defined GTV, outperforming PET-only segmentation.
  • This approach is expected to enhance accuracy and consistency in target definition for radiotherapy planning.