ArticleJournal of medical physics
Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images.
Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Advances in dual energy computed tomography approach for proton stopping power ratio computation in radiotherapy.World journal of radiology · 2025Review
- Artificial Intelligence in Proton Therapy: What Works, What Does Not, and What Is Next.Cancer journal (Sudbury, Mass.)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to accurately estimate proton stopping power ratio (SPR), relative electron density Methods: Empirical relationships between computed tomography (CT) number and SPR, Results: The proposed method gave modeling root mean square error (RMSE) near 1% at maximum for the case of SPR and Conclusion: The proposed method proves to be more flexible and robust among all presented methods since it has lower testing error in most cases and can be improved based on data using the machine learning algorithm. The algorithm can also improve estimation by adjusting the model as well as aid in automation and it's easy to implement.
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.