AUTHOR=Ji Hangjie , Lafata Kyle , Mowery Yvonne , Brizel David , Bertozzi Andrea L. , Yin Fang-Fang , Wang Chunhao 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.895544

DOI=10.3389/fonc.2022.895544

ISSN=2234-943X

ABSTRACT=Purpose: 
To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.
Methods: 
Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with break-down biological components for enhanced explainability. 
The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20Gy delivery (2Gy/daily fraction) by IMRT. In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution as in one branch, and the biological model generates post-20Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.
Results: 
The proposed method successfully generated post-20Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. SUV mean values in 18FDG high-uptake regions of predicted images (2.45±0.25) were similar to ground-truth results (2.51±0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using 5%/5mm criterion; such result was improved to 99.9%/99.6% when 10%/10mm was adopted. 
Conclusion: 
The developed biologically guided deep learning method achieved post-20Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With break-down biological modelling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in future.