title: Correlation of a hypoxia based tumor control model with observed local control rates in nasopharyngeal carcinoma treated with chemoradiotherapy.
abstract: OBJECTIVE
  To extend the application of current radiation therapy (RT) based tumor control probability (TCP) models of nasopharyngeal carcinoma (NPC) to include the effects of hypoxia and chemoradiotherapy (CRT).
  
  
  METHODS
  A TCP model is described based on the linear-quadratic model modified to account for repopulation, chemotherapy, heterogeneity of dose to the tumor, and hypoxia. Sensitivity analysis was performed to determine which parameters exert the greatest influence on the uncertainty of modeled TCP. On the basis of the sensitivity analysis, the values of specific radiobiological parameters were set to nominal values reported in the literature for NPC or head and neck tumors. The remaining radiobiological parameters were determined by fitting TCP to clinical local control data from published randomized studies using both RT and CRT. Validation of the model was performed by comparison of estimated TCP and average overall local control rate (LCR) for 45 patients treated at the institution with conventional linear-accelerator-based or helical tomotherapy based intensity-modulated RT and neoadjuvant chemotherapy.
  
  
  RESULTS
  Sensitivity analysis demonstrates that the model is most sensitive to the radiosensitivity term alpha and the dose per fraction. The estimated values of alpha and OER from data fitting were 0.396 Gy(-1) and 1.417. The model estimate of TCP (average 90.9%, range 26.9%-99.2%) showed good correlation with the LCR (86.7%).
  
  
  CONCLUSIONS
  The model implemented in this work provides clinicians with a useful tool to predict the success rate of treatment, optimize treatment plans, and compare the effects of multimodality therapy.
authors.affiliation: Department of Medical Physics, Centro di Riferimento Oncologico, Aviano 33081, Italy. mavanzo@cro.it
authors.forename: Michele
authors.initials: M
authors.lastname: Avanzo
authors.forename: Joseph
authors.initials: J
authors.lastname: Stancanello
authors.forename: Giovanni
authors.initials: G
authors.lastname: Franchin
authors.forename: Giovanna
authors.initials: G
authors.lastname: Sartor
authors.forename: Rajesh
authors.initials: R
authors.lastname: Jena
authors.forename: Annalisa
authors.initials: A
authors.lastname: Drigo
authors.forename: Andrea
authors.initials: A
authors.lastname: Dassie
authors.forename: Marco
authors.initials: M
authors.lastname: Gigante
authors.forename: Elvira
authors.initials: E
authors.lastname: Capra
mesh_terms: D000465:Algorithms; D000818:Animals; D000970:Antineoplastic Agents / Q000494:pharmacology*; D002277:Carcinoma / Q000188:drug therapy* / Q000473:pathology* / Q000532:radiotherapy*; D003131:Combined Modality Therapy / Q000379:methods*; D004195:Disease Models, Animal; D004358:Drug Therapy / Q000379:methods; D006801:Humans; D000860:Hypoxia / Q000473:pathology*; D008495:Medical Oncology / Q000379:methods; D015233:Models, Statistical; D009303:Nasopharyngeal Neoplasms / Q000188:drug therapy* / Q000473:pathology* / Q000532:radiotherapy*; D011336:Probability; D011878:Radiotherapy / Q000379:methods; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents