:
Purpose Both CT and PET radiomics is considered as a potential prognostic biomarker in head and neck cancer.
This study investigate the value of fused pre-treatment functional imaging (18F-FDG PET/CT) radiomics for modeling of
local recurrence of head and neck cancers.
Material and Methods:
Firstly, 298 patients is divided into a training set (n = 192) and verification set (n = 106).
Secondly, PETs and CTs are fused based on wavelet transform. Thirdly, radiomics features are extracted from the 3D
tumor area from PETCT fusion. The training set is used to select the features reduction and predict local recurrence, and
the random forest prediction models combining radiomics and clinical variables are constructed. Finally, the ROC curve
and K-M analysis are used to evaluate the prediction efficiency of the model on the validation set.
Results:
Two PET / CT fusion radiomics features and three clinic parameters are extracted to construct the radiomics
model. AUC value in the verification set 0.70 is better than no fused sets 0.69. The accuracy of 0.66 is not the highest
value (0.67). Either consistency index CI 0.70 (from 0.67 to 0.70) or the p-value 0.025 (from 0.03 to 0.025) get the best
result in all four models.
Conclusion:
The radiomics model based on the fusion of PETCT is better than the model based on PET or CT alone in
predicting local recurrence, the inclusion of clinical parameters may result in more accurate predictions, which has certain
guiding significance for the development of personalized precise treatment scheme.
National Protocol for Model-Based Selection for Proton Therapy in Head and Neck Cancer