title: Assessment of uncertainties in radiation-induced cancer risk predictions at clinically relevant doses.
abstract: OBJECTIVE
  Theoretical dose-response models offer the possibility to assess second cancer induction risks after external beam therapy. The parameters used in these models are determined with limited data from epidemiological studies. Risk estimations are thus associated with considerable uncertainties. This study aims at illustrating uncertainties when predicting the risk for organ-specific second cancers in the primary radiation field illustrated by choosing selected treatment plans for brain cancer patients.
  
  
  METHODS
  A widely used risk model was considered in this study. The uncertainties of the model parameters were estimated with reported data of second cancer incidences for various organs. Standard error propagation was then subsequently applied to assess the uncertainty in the risk model. Next, second cancer risks of five pediatric patients treated for cancer in the head and neck regions were calculated. For each case, treatment plans for proton and photon therapy were designed to estimate the uncertainties (a) in the lifetime attributable risk (LAR) for a given treatment modality and (b) when comparing risks of two different treatment modalities.
  
  
  RESULTS
  Uncertainties in excess of 100% of the risk were found for almost all organs considered. When applied to treatment plans, the calculated LAR values have uncertainties of the same magnitude. A comparison between cancer risks of different treatment modalities, however, does allow statistically significant conclusions. In the studied cases, the patient averaged LAR ratio of proton and photon treatments was 0.35, 0.56, and 0.59 for brain carcinoma, brain sarcoma, and bone sarcoma, respectively. Their corresponding uncertainties were estimated to be potentially below 5%, depending on uncertainties in dosimetry.
  
  
  CONCLUSIONS
  The uncertainty in the dose-response curve in cancer risk models makes it currently impractical to predict the risk for an individual external beam treatment. On the other hand, the ratio of absolute risks between two modalities is less sensitive to the uncertainties in the risk model and can provide statistically significant estimates.
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg 69117, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Nguyen
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: M
authors.initials: M
authors.lastname: Moteabbed
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: H
authors.initials: H
authors.lastname: Paganetti
mesh_terms: D000293:Adolescent; D001932:Brain Neoplasms / Q000532:radiotherapy; D002648:Child; D002675:Child, Preschool; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009381:Neoplasms, Radiation-Induced / Q000209:etiology*; D016609:Neoplasms, Second Primary / Q000209:etiology*; D011829:Radiation Dosage; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D018570:Risk Assessment; D035501:Uncertainty