title: Development of multivariate NTCP models for radiation-induced hypothyroidism: a comparative analysis.
abstract: BACKGROUND
  Hypothyroidism is a frequent late side effect of radiation therapy of the cervical region. Purpose of this work is to develop multivariate normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RHT) and to compare them with already existing NTCP models for RHT.
  
  
  METHODS
  Fifty-three patients treated with sequential chemo-radiotherapy for Hodgkin's lymphoma (HL) were retrospectively reviewed for RHT events. Clinical information along with thyroid gland dose distribution parameters were collected and their correlation to RHT was analyzed by Spearman's rank correlation coefficient (Rs). Multivariate logistic regression method using resampling methods (bootstrapping) was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). Models were tested against external published data on RHT and compared with other published NTCP models.
  
  
  RESULTS
  If we express the thyroid volume exceeding X Gy as a percentage (Vx(%)), a two-variable NTCP model including V30(%) and gender resulted to be the optimal predictive model for RHT (Rs = 0.615, p < 0.001. AUC = 0.87). Conversely, if absolute thyroid volume exceeding X Gy (Vx(cc)) was analyzed, an NTCP model based on 3 variables including V30(cc), thyroid gland volume and gender was selected as the most predictive model (Rs = 0.630, p < 0.001. AUC = 0.85). The three-variable model performs better when tested on an external cohort characterized by large inter-individuals variation in thyroid volumes (AUC = 0.914, 95% CI 0.760-0.984). A comparable performance was found between our model and that proposed in the literature based on thyroid gland mean dose and volume (p = 0.264).
  
  
  CONCLUSIONS
  The absolute volume of thyroid gland exceeding 30 Gy in combination with thyroid gland volume and gender provide an NTCP model for RHT with improved prediction capability not only within our patient population but also in an external cohort.
authors.affiliation: Institute of Biostructures and Bioimaging, National Council of Research (CNR), Naples, Italy. laura.cella@cnr.it
authors.forename: Laura
authors.initials: L
authors.lastname: Cella
authors.forename: Raffaele
authors.initials: R
authors.lastname: Liuzzi
authors.forename: Manuel
authors.initials: M
authors.lastname: Conson
authors.forename: Vittoria
authors.initials: V
authors.lastname: D'Avino
authors.forename: Marco
authors.initials: M
authors.lastname: Salvatore
authors.forename: Roberto
authors.initials: R
authors.lastname: Pacelli
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D004307:Dose-Response Relationship, Radiation; D005260:Female; D005500:Follow-Up Studies; D006689:Hodgkin Disease / Q000532:radiotherapy*; D006801:Humans; D007037:Hypothyroidism / Q000209:etiology*; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D012372:ROC Curve; D011832:Radiation Injuries / Q000209:etiology* / Q000517:prevention & control; D020266:Radiotherapy, Conformal / Q000009:adverse effects*; D012189:Retrospective Studies; D013961:Thyroid Gland / Q000528:radiation effects*; D016896:Treatment Outcome; D055815:Young Adult