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Reliability of dose volume constraint inference from clinical data.

C M Lutz1, D S Møller1, L Hoffmann1

  • 1Department of Oncology, Aarhus University Hospital, Aarhus, Denmark.

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|March 29, 2017
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Summary

Reliably inferring dose volume histogram points (DVHPs) from clinical data is uncertain for typical radiotherapy patient cohort sizes. Larger cohorts, over 500 patients, are needed for accurate DVHP inference in non-small cell lung cancer treatment planning.

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Area of Science:

  • Radiation oncology
  • Medical physics
  • Biostatistics

Background:

  • Dose volume histogram points (DVHPs) are crucial dose constraints in radiotherapy planning.
  • Radiation pneumonitis in non-small cell lung cancer (NSCLC) is a relevant clinical context for DVHP studies.
  • Logistic regression is a common method for DVHP inference from clinical data.

Purpose of the Study:

  • To evaluate the reliability of DVHP inference from clinical radiotherapy data.
  • To investigate the impact of cohort size and complication incidence rates on DVHP inference accuracy.
  • To compare the performance of Bootstrap and Cohort Replication Monte Carlo (CoRepMC) methods.

Main Methods:

  • Generated an 'ideal' cohort from 102 NSCLC dose distributions and a postulated DVHP model.
  • Applied Bootstrap and CoRepMC methods to create 1000 equally sized populations for analysis.
  • Tested nine scenarios with cohort sizes of 102, 500, and 2000 patients and three DVHP models.
  • Analyzed inference frequency distributions and correct inference rates for each scenario.

Main Results:

  • Bootstrap method yielded chaotic results for cohort size 1, with improved concentration for sizes 500 and 2000.
  • CoRepMC showed increased inference frequency with larger cohort sizes and higher incidence rates.
  • Accurate inference rates (>90%) were only achieved with cohorts exceeding 500 patients.
  • Both methods indicated high uncertainty in DVHP inference for typical cohort sizes.

Conclusions:

  • DVHP inference from clinical data is highly uncertain for standard cohort sizes.
  • CoRepMC demonstrated more stable and reliable results compared to Bootstrap.
  • Larger patient cohorts (over 500) are necessary for robust DVHP inference in NSCLC radiotherapy.
  • Randomness in dose-response significantly influences the statistical analysis of DVHP inference.