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Related Concept Videos

Percentile01:18

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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Defining a Quantitative Threshold for Adaptive Re-planning using a Percentile-based Scoring System.

Alexandra O Leone1,2, Christine B Peterson3, Anna Lee4

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

International Journal of Particle Therapy
|February 25, 2026
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Summary
This summary is machine-generated.

Quantitative indicators for adaptive head and neck proton therapy planning are now available. A validated Daisy scoring system helps determine when plan adaptations are necessary, improving treatment quality for oropharyngeal cancers.

Keywords:
Adaptive Proton TherapyOffline Adaptive RadiotherapyPlan Quality Assessment

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Cancer Treatment

Background:

  • Adaptive planning is crucial for head and neck proton therapy but lacks quantitative decision-making indicators.
  • Current methods do not objectively guide when to adapt treatment plans.

Purpose of the Study:

  • To establish a quantitative threshold for plan quality changes indicating the need for adaptive planning.
  • To correlate plan quality differences with physician decisions in head and neck proton therapy.

Main Methods:

  • Analyzed 167 patients' intensity-modulated proton therapy plans for oropharyngeal cancers.
  • Utilized a percentile-based Daisy scoring system to assess plan quality differences.
  • Employed generalized linear mixed models and physician review to evaluate adaptation necessity.

Main Results:

  • A significant relationship was found between plan score differences and the need for adaptation (P=0.034).
  • Plans requiring adaptation showed a mean score drop of 37.7%, versus 19.3% for acceptable plans.
  • Clinical tumor volume and hotspot dose were key indicators for adaptation decisions.

Conclusions:

  • A percentile-based Daisy scoring system provides a reliable threshold for adaptive planning decisions.
  • This system can objectively identify when changes in plan quality necessitate adaptive proton therapy.