ArticlePolymers2025
Comparative Analysis of Machine and Deep Learning Algorithms for Bragg Peak Estimation in Polymeric Materials for Tissue-Sparing Radiotherapy.
Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Machine Learning Prediction of Therapeutic Outcome After Transforaminal Epidural Steroid Injection for Radiculopathy from Herniated Lumbar Disc.Bioengineering (Basel, Switzerland) · 2025Article
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Abstract
Proton therapy has emerged as a highly precise and tissue-sparing radiotherapy technique, capitalizing on the unique energy deposition pattern of protons characterized by the Bragg peak. Ensuring treatment accuracy relies on calibration phantoms, often composed of tissue-equivalent polymeric materials. This study investigates the dosimetric behavior of four commonly used polymers-Parylene, Epoxy, Lexan, and Mylar-by analyzing their linear energy transfer (LET) values and Bragg curve characteristics across various proton energies. Experimental LET data were collected and used to train and evaluate the predictive power for Bragg peak of multiple artificial intelligence models, including kNN, SVR, MLP, RF, LWRF, XGBoost, 1D-CNN, LSTM, and BiLSTM. These algorithms were optimized using 10-fold cross-validation and assessed through statistical error and performance metrics including
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