title: Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy.
abstract: OBJECTIVE
  To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient's anatomic and dosimetric parameters for esophageal cancer patients.
  
  
  METHODS
  Eighty esophagus patients in the authors' institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman's rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model.
  
  
  RESULTS
  With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis.
  
  
  CONCLUSIONS
  It is feasible to use patients' anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Jiazhou
authors: J
authors: Wang
authors: The 1st Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325000, China.
authors: Xiance
authors: X
authors: Jin
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Kuaike
authors: K
authors: Zhao
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Jiayuan
authors: J
authors: Peng
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Jiang
authors: J
authors: Xie
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Junchao
authors: J
authors: Chen
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Zhen
authors: Z
authors: Zhang
authors: Department of Radiation Oncology, University of Miami-Miller School of Medicine, Miami, Florida 33136.
authors: Matthew
authors: M
authors: Studenski
authors: Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
authors: Weigang
authors: W
authors: Hu
mesh_terms: D004938:Esophageal Neoplasms / Q000532:radiotherapy*; D005240:Feasibility Studies; D006801:Humans; D008962:Models, Theoretical; D057285:Precision Medicine; D011874:Radiometry / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted