ArticleJournal of medical imaging and radiation sciences2022
Computational dose visualization & comparison in total skin electron treatment suggests superior coverage by the rotational versus the Stanford technique.
Article in Journal of medical imaging and radiation sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Feasibility study: Use of an optical scanning system to obtain 3D body surface images suitable for total skin electron therapy treatment dosimetry.Journal of applied clinical medical physics · 2026Article
- Patient Positioning and Treatment Techniques in Total Skin Irradiation: A Scoping Review.Cancers · 2025Review
- Development of a 3D-printed phantom for total skin electron therapy dose assessment.Journal of applied clinical medical physics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
INTRODUCTION/
backgroundThe goal of Total Skin Electron Therapy (TSET) is to achieve a uniform surface dose, although assessment of this is never really done and typically limited points are sampled. A computational treatment simulation approach was developed to estimate dose distributions over the body surface, to compare uniformity of (i) the 6 pose Stanford technique and (ii) the rotational technique.
methodsThe relative angular dose distributions from electron beam irradiation was calculated by Monte Carlo simulation for cylinders with a range of diameters, approximating body part curvatures. These were used to project dose onto a 3D body model of the TSET patient's skin surfaces. Computer animation methods were used to accumulate the dose values, for display and analysis of the homogeneity of coverage.
resultsThe rotational technique provided more uniform coverage than the Stanford technique. Anomalies of under dose were observed in lateral abdominal regions, above the shoulders and in the perineum. The Stanford technique had larger areas of low dose laterally. In the rotational technique, 90% of the patient's skin was within ±10% of the prescribed dose, while this percentage decreased to 60% or 85% for the Stanford technique, varying with patient body mass. Interestingly, the highest discrepancy was most apparent in high body mass patients, which can be attributed to the loss of tangent dose at low angles of curvature. DISCUSSION/
conclusionThis simulation and visualization approach is a practical means to analyze TSET dose, requiring only optical surface body topography scans. Under- and over-exposed body regions can be found, and irradiation could be customized to each patient. Dose Area Histogram (DAH) distribution analysis showed the rotational technique to have better uniformity, with most areas within 10% of the umbilicus value. Future use of this approach to analyze dose coverage is possible as a routine planning tool.
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.