ArticleFrontiers in oncology2022
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Development and application of a novel tumor habitat analysis technique based on dynamical modeling.Medical physics · 2025Article
- Characterization of arteriosclerosis based on computer-aided measurements of intra-arterial thickness.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery.Physics and imaging in radiation oncology · 2024Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.La Radiologia medica · 2024Article
- Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.Frontiers in immunology · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.Medical physics · 2023Article
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
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7 authors.
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Abstract
Purpose: To develop a method of biologically guided deep learning for post-radiation 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 Results: The proposed method successfully generated post-20-Gy Conclusion: The developed biologically guided deep learning method achieved post-20-Gy
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