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This paper develops a method of biologically guided deep learning forpost-radiation FDG-PET image outcome prediction based on pre-radiation imagesand radiotherapy dose information. Based on the classic reaction-diffusionmechanism, a novel biological model was proposed using a partial differentialequation that incorporates spatial radiation dose distribution as apatient-specific treatment information variable. A 7-layerencoder-decoder-based convolutional neural network (CNN) was designed andtrained to learn the proposed biological model. As such, the model couldgenerate post-radiation FDG-PET image outcome predictions with possibletime-series transition from pre-radiotherapy image states to post-radiotherapystates. The proposed method was developed using 64 oropharyngeal patients withpaired FDG-PET studies before and after 20Gy delivery (2Gy/daily fraction) byIMRT. In a two-branch deep learning execution, the proposed CNN learns specificterms in the biological model from paired FDG-PET images and spatial dosedistribution as in one branch, and the biological model generates post-20GyFDG-PET image prediction in the other branch. The proposed method successfullygenerated post-20Gy FDG-PET image outcome prediction with breakdownillustrations of biological model components. Time-series FDG-PET imagepredictions were generated to demonstrate the feasibility of disease responserendering. The developed biologically guided deep learning method achievedpost-20Gy FDG-PET image outcome predictions in good agreement with ground-truthresults. With break-down biological modeling components, the outcome imagepredictions could be used in adaptive radiotherapy decision-making to optimizepersonalized plans for the best outcome in the future.

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under  Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer  Application