Skip to main content

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

Publication ,  Journal Article
Ji, H; Lafata, K; Mowery, Y; Brizel, D; Bertozzi, AL; Yin, F-F; Wang, C
Published in: Front Oncol
2022

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 breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (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 in one branch, and the biological model generates post-20-Gy 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-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (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 the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted. CONCLUSION: The developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Front Oncol

DOI

ISSN

2234-943X

Publication Date

2022

Volume

12

Start / End Page

895544

Location

Switzerland

Related Subject Headings

  • 3211 Oncology and carcinogenesis
  • 3202 Clinical sciences
  • 1112 Oncology and Carcinogenesis
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ji, H., Lafata, K., Mowery, Y., Brizel, D., Bertozzi, A. L., Yin, F.-F., & Wang, C. (2022). Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application. Front Oncol, 12, 895544. https://doi.org/10.3389/fonc.2022.895544
Ji, Hangjie, Kyle Lafata, Yvonne Mowery, David Brizel, Andrea L. Bertozzi, Fang-Fang Yin, and Chunhao Wang. “Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.Front Oncol 12 (2022): 895544. https://doi.org/10.3389/fonc.2022.895544.
Ji, Hangjie, et al. “Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.Front Oncol, vol. 12, 2022, p. 895544. Pubmed, doi:10.3389/fonc.2022.895544.

Published In

Front Oncol

DOI

ISSN

2234-943X

Publication Date

2022

Volume

12

Start / End Page

895544

Location

Switzerland

Related Subject Headings

  • 3211 Oncology and carcinogenesis
  • 3202 Clinical sciences
  • 1112 Oncology and Carcinogenesis