ArticleRadiation oncology (London, England)2026
Machine learning approach to estimation of in vivo measured dose and treatment planning for total body irradiation.
Article in Radiation oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
purposeThis study aims to enhance the conventional Total Body Irradiation (TBI) dose calculation method which relies on manual calculation using tissue maximum ratio, off axis ratio, and scatter factors, by incorporating machine learning (ML) to predict in vivo measured doses and optimize the number of compensators needed, thereby improving the accuracy and efficacy of TBI treatments. MATERIALS AND
methodsA retrospective analysis was performed on patient data between 2018 and 2022, involving 96 patients. The data included demographics, treatment specifics, and in vivo dose measurements. We developed and evaluated various ML models, including Random Forest, XGBoost, LightGBM, and Gradient Boosting Regressor, using 70% of the data for training and 30% for testing. Model performance was assessed using the Mean Absolute Percentage Error (MAPE). The optimization process adjusted compensator numbers based on ML predictions to better align with the prescribed dose.
resultsAll ML models outperformed the conventional method, with Gradient Boosting Regressor demonstrating the best performance. It achieved an overall MAPE of 2.52% (maximum 2.95% at hip with hand, minimum 2.19% at umbilicus without arm) in predicting measured doses, which is a significant improvement compared to the conventional method’s overall MAPE of 4.01% (maximum 8.69% at head, minimum 2.74% at umbilicus without arm). Furthermore, the optimization process successfully generated treatment plans with a high degree of conformity to the prescribed doses, achieving a MAPE of 1.69% between the optimized and prescribed doses. The head region, which exhibited the largest discrepancy in the conventional method, saw substantial improvement, while the neck region required an increase in compensators to correct underestimation.
conclusionThe machine learning approach successfully improved the accuracy of in vivo dose predictions and compensator optimization, suggesting an enhancement of conventional methods. This advancement holds the potential to deliver more precise and personalized radiation therapy in TBI treatments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.