[引用][C] A combined radiomics-dosiomics machine learning approach improves prediction of radiation pneumonitis compared to DVH data in lung cancer patients
Results While the dosiomics model performance was comparable to the DVH model, we find
that a radiomics+ dosiomics model with features extracted from the V 20 ROI (AUC= 0.713)
and combined V 20+ V 5 radiomics+ dosiomics model (AUC= 0.708) outperform the DVH
only model (AUC= 0.63). In addition, radiomics+ dosiomics+ clinical model with features
generated from the V 20 ROI (AUC= 0.771) and the radiomics+ dosiomics+ clinical model
with combined features from V 20 and V 5 ROI (AUC= 0.763) fared better than the clinical …
that a radiomics+ dosiomics model with features extracted from the V 20 ROI (AUC= 0.713)
and combined V 20+ V 5 radiomics+ dosiomics model (AUC= 0.708) outperform the DVH
only model (AUC= 0.63). In addition, radiomics+ dosiomics+ clinical model with features
generated from the V 20 ROI (AUC= 0.771) and the radiomics+ dosiomics+ clinical model
with combined features from V 20 and V 5 ROI (AUC= 0.763) fared better than the clinical …
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