ArticleFrontiers in oncology2026
Automated dose-gradient curve and dose-volume histogram analysis platform: development, validation, and clinical decision support with TG-119 datasets.
Article in Frontiers in oncology, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Highly conformal radiotherapy techniques, such as stereotactic radiosurgery and stereotactic ablative radiotherapy, require steep dose fall-off to spare organs at risk (OARs). The dose-volume histogram (DVH) provides limited spatial information, whereas the dose gradient curve (DGC) offers quantitative assessment of dose fall-off but has seen limited clinical use due to time-consuming manual processing. Purpose: This study aimed to develop and validate an automated DGC and DVH analysis platform to enable accurate and rapid dose gradient evaluation for radiotherapy plan assessment. Methods: The platform automatically processes DICOM RT dose and RT structure files, with optional RT Plan input for direct prescription dose extraction, to compute differential and cumulative dose gradient index (dDGI and cDGI), DVH metrics, and clinically informed dose gradient quality grading. Computational validation was performed using AAPM TG-119 phantom datasets, including prostate, head and neck, C-shape, and multi-target cases, at 1.25 mm and 2.5 mm dose grid resolutions. Clinical validation was conducted using 142 Gamma Knife stereotactic radiosurgery plans for vestibular schwannoma from the publicly available Vestibular-Schwannoma-SEG dataset (The Cancer Imaging Archive). Results: The platform reduced computation time from over 2 hours (original R-based workflow) to under 10 seconds, representing >99.8% efficiency improvement. All TG-119 cases met or exceeded recommended PTV coverage and OAR tolerances. Using prescription doses extracted from DICOM RT Plan file and marching-cubes-based surface area estimation, dDGI at the prescription dose ranged from 0.312 to 0.792 mm on the 1.25 mm grid and from 0.327 to 0.659 mm on the 2.5 mm grid; cDGI at 50% of the prescription dose ranged from 12.4 to 15.7 mm (1.25 mm) and from 11.7 to 15.5 mm (2.5 mm). Clinical validation with 142 vestibular schwannoma Gamma Knife SRS cases (median target volume 0.881 cc, range 0.036-7.183 cc; Rx = 12-13 Gy) yielded dDGI at Rx = Conclusions: The proposed platform enables rapid, automated DGC analysis practical for routine plan evaluation. The integrated quality grading bridges the gap between quantitative dose gradient metrics and actionable clinical decisions, complementing conventional DVH-based assessment.
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.