Optimizing principal component models for representing interfraction variation in lung cancer radiotherapy

Ahmed M. Badawi, Elisabeth Weiss, William C. Sleeman, Chenyu Yan, Geoffrey D. Hugo

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Purpose: To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA). Methods: In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: (1) Individual models for the GTV and each lung from one image per week (week to week, W2W); (2) a W2W composite model of all structures; (3) individual models using all images (weekly plus repeat intrafraction images, allscans); and (4) composite model with all images. Models were reconstructed retrospectively (using all available images acquired) and prospectively (using only data acquired up to a time point during treatment). Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models. Results: An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7±0.2 mm, which increased to 1.1±0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors (p=0.15, paired t -test). The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2±0.5 mm and for the composite model after five measurements at 0.8±0.4 mm. Conclusions: Retrospective PCA models were capable of reconstructing original GTV and lung shapes and positions within several millimeters with three to four dominant modes, on average. Prospective models achieved similar accuracy after four to five measurements.

Original languageEnglish
Pages (from-to)5080-5091
Number of pages12
JournalMedical physics
Volume37
Issue number9
DOIs
StatePublished - Sep 2010

Keywords

  • adaptive radiotherapy planning
  • deformation modeling
  • geometry representation
  • lung cancer
  • tumor regression

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