ArticlePhysics and imaging in radiation oncology2022
Clinical implementation and validation of an automated adaptive workflow for proton therapy.
Article in Physics and imaging in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions.International journal of particle therapy · 2026Article
- Practical considerations for handling implants in proton therapy.Journal of applied clinical medical physics · 2025Article
- Triggered plan adaptation using multi-image optimization for improved robustness in head-and-neck cancer proton therapy.Physics and imaging in radiation oncology · 2025Article
- New guidelines and recommendations to advance treatment planning in proton therapy.Physics and imaging in radiation oncology · 2025Article
- A review of the clinical introduction of 4D particle therapy research concepts.Physics and imaging in radiation oncology · 2024Review
- Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients.The British journal of radiology · 2023Article
- Surveying the clinical practice of treatment adaptation and motion management in particle therapy.Physics and imaging in radiation oncology · 2023Article
- Parameter based 4D dose calculations for proton therapy.Physics and imaging in radiation oncology · 2023Article
- Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients.Physics and imaging in radiation oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Treatment quality of proton therapy can be monitored by repeat-computed tomography scans (reCTs). However, manual re-delineation of target contours can be time-consuming. To improve the workflow, we implemented an automated reCT evaluation, and assessed if automatic target contour propagation would lead to the same clinical decision for plan adaptation as the manual workflow. Materials and methods: This study included 79 consecutive patients with a total of 250 reCTs which had been manually evaluated. To assess the feasibility of automated reCT evaluation, we propagated the clinical target volumes (CTVs) deformably from the planning-CT to the reCTs in a commercial treatment planning system. The dose-volume-histogram parameters were extracted for manually re-delineated (CTV Results: In 92% (N = 229) of the reCTs correct flagging was obtained. Only 4% (N = 9) of the reCTs presented with false negatives (i.e., at least one clinical constraint failed for CTV Conclusion: Deformable target contour propagation was clinically acceptable. A script-based automatic reCT evaluation workflow has been introduced in routine clinical practice.
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.