Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma |
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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 |
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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. |
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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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