title: Assessing the Clinical Impact of Approximations in Analytical Dose Calculations for Proton Therapy.
abstract: OBJECTIVE
  To assess the impact of approximations in current analytical dose calculation methods (ADCs) on tumor control probability (TCP) in proton therapy.
  
  
  METHODS
  Dose distributions planned with ADC were compared with delivered dose distributions as determined by Monte Carlo simulations. A total of 50 patients were investigated in this analysis with 10 patients per site for 5 treatment sites (head and neck, lung, breast, prostate, liver). Differences were evaluated using dosimetric indices based on a dose-volume histogram analysis, a γ-index analysis, and estimations of TCP.
  
  
  RESULTS
  We found that ADC overestimated the target doses on average by 1% to 2% for all patients considered. The mean dose, D95, D50, and D02 (the dose value covering 95%, 50% and 2% of the target volume, respectively) were predicted within 5% of the delivered dose. The γ-index passing rate for target volumes was above 96% for a 3%/3 mm criterion. Differences in TCP were up to 2%, 2.5%, 6%, 6.5%, and 11% for liver and breast, prostate, head and neck, and lung patients, respectively. Differences in normal tissue complication probabilities for bladder and anterior rectum of prostate patients were less than 3%.
  
  
  CONCLUSIONS
  Our results indicate that current dose calculation algorithms lead to underdosage of the target by as much as 5%, resulting in differences in TCP of up to 11%. To ensure full target coverage, advanced dose calculation methods like Monte Carlo simulations may be necessary in proton therapy. Monte Carlo simulations may also be required to avoid biases resulting from systematic discrepancies in calculated dose distributions for clinical trials comparing proton therapy with conventional radiation therapy.
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. Electronic address: jschuemann@mgh.harvard.edu.
authors.forename: Jan
authors.initials: J
authors.lastname: Schuemann
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Drosoula
authors.initials: D
authors.lastname: Giantsoudi
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Clemens
authors.initials: C
authors.lastname: Grassberger
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Maryam
authors.initials: M
authors.lastname: Moteabbed
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Chul Hee
authors.initials: CH
authors.lastname: Min
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Harald
authors.initials: H
authors.lastname: Paganetti
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000532:radiotherapy; D005260:Female; D006258:Head and Neck Neoplasms / Q000532:radiotherapy; D006801:Humans; D008113:Liver Neoplasms / Q000532:radiotherapy; D008175:Lung Neoplasms / Q000532:radiotherapy; D008297:Male; D009010:Monte Carlo Method; D009369:Neoplasms / Q000532:radiotherapy*; D058958:Organs at Risk / Q000528:radiation effects; D011336:Probability; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D061766:Proton Therapy / Q000706:statistics & numerical data*; D011874:Radiometry / Q000379:methods; D011879:Radiotherapy Dosage; D012007:Rectum / Q000528:radiation effects; D035501:Uncertainty; D001743:Urinary Bladder / Q000528:radiation effects