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Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma
Affiliation:1. Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China;2. Siemens Healthineers Digital Technology (Shanghai) Co., Ltd., Shanghai 201306, China
Abstract:BackgroundIntravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.MethodsEighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.ResultsThe radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).ConclusionsThe IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.
Keywords:NPC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_Cs4tF1Zrw5"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Nasopharyngeal carcinoma  MRI"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_FR9jFWMIYK"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Magnetic resonance imaging  CE T1WI"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_tlxYNKYyDU"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Contrast-enhanced T1-weighted imaging  DWI"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_cLbIM3r6mE"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Diffusion-weighted imaging  IVIM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_HlWcciiJsY"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Intravoxel incoherent motion  EBV-DNA"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_7kllSidQlR"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Epstein-Barr virus DNA  SD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_ngB7ghz7xJ"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Stable disease  PD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_oiO6Ijgv72"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Progressive disease  PR"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_vTchhgbY7X"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Partial response  CR"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_vav8tIKHCb"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Complete response  RECIST"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_duHAiwAS0y"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Response Evaluation Criteria in Solid Tumors  Non-CR"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_rrenwlEPbs"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Incomplete response  VOIs"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_n7vI1Dwi52"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Volumes of interest  ICCs"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_1VpbU4GXIw"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Inter-class correlation coefficents  GLDM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_JcqjMjiUQS"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Gray-level dependence matrix  GLRLM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_ewMyQCuw91"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Gray-level run-length matrix  GLSZM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_fOJcny4pzX"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Gray-level size zone matrix  NGTDM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_NGXMBOoqUJ"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Neighboring gray tone difference matrix  GLCM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_SHNgvqbE2M"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Gray-level co-occurrence matrix  LASSO"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_s202f29fB8"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Least shrinkage and selection operator  SVM"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_LeaxqY6utl"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Support vector machine  ROC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_XLd6sCggIG"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Receiver operating characteristic  AUC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_jOJvxV9vHr"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Area under the ROC curve  LR"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_qlpzGrW7Yp"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Logistic regression  RF"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  pc_tNjpCKo9yO"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Random forest
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