title: Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemoradiotherapy.
abstract: OBJECTIVE
  To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile.
  
  
  METHODS
  Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve.
  
  
  RESULTS
  A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%.
  
  
  CONCLUSIONS
  Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.
authors: Department of Basic Medical Sciences, Ghent University, Ghent, Belgium. kim.deruyck@UGent.be
authors: Kim
authors: K
authors: De Ruyck
authors: Nick
authors: N
authors: Sabbe
authors: Cary
authors: C
authors: Oberije
authors: Katrien
authors: K
authors: Vandecasteele
authors: Olivier
authors: O
authors: Thas
authors: Dirk
authors: D
authors: De Ruysscher
authors: Phillipe
authors: P
authors: Lambin
authors: Jan
authors: J
authors: Van Meerbeeck
authors: Wilfried
authors: W
authors: De Neve
authors: Hubert
authors: H
authors: Thierens