ArticleAdvances in radiation oncology2025
Comparative Analysis of Atlas and Neural Network Autosegmentation Methods for Pediatric Craniospinal Irradiation With the Development of a Knowledge-Based Quality Assurance Tool.
Article in Advances in radiation oncology, 2025. 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
8 authors.
Funding
Abstract
Purpose: This study aims to evaluate the performance of Atlas and neural network autosegmentation methods and develop a knowledge-based quality assurance (QA) tool for pediatric craniospinal irradiation (CSI). Methods and Materials: Autosegmentation was performed on 63 CSI patients using 3 methods: Atlas, commercial artificial intelligence (AI), and in-house AI. The performance of these methods was analyzed using 13 quantitative metrics, comprising 6 overlap and 7 distance metrics, across 13 critical organs and a linear mixed-effect model analysis was performed. Additionally, a knowledge-based QA tool was developed by leveraging distinctive computed tomography number distributions from 100 CSI patients for each organ, using the kernel density estimation (KDE) method to ensure robust error detection and validation. The QA tool was tested on 50 CSI cases by comparing baseline KDEs from 100 CSI patients. Results: The linear mixed-effect analysis showed that the in-house AI outperformed both the Atlas and commercial AI methods in overlap and distance metrics. The in-house AI outperformed the commercial AI with a higher average overlap of 0.01 ± 0.01 and surpassed the Atlas method by 0.02 ± 0.01. In terms of distance metrics, the in-house AI matched the commercial AI (-0.31 ± 0.72 mm) and exceeded the Atlas method by 3.10 ± 0.68 mm. Paired t-tests showed the in-house AI was superior to the Atlas in 13.0% of cases, while the Atlas outperformed the in-house method in 8.9% of comparisons. Similarly, the in-house AI was better than the commercial AI in 35.3% of tests, with the commercial AI outperforming in 32.7%. The QA tool results demonstrated that 100% agreement with baseline KDEs occurred in 46.4% of tests for Atlas, 46.5% for the commercial AI, and 60.7% for the in-house AI. Conclusions: The in-house AI excelled over the Atlas and commercial AI methods in autosegmentation accuracy for pediatric CSI patients. Furthermore, a knowledge-based QA tool enables clinicians to detect and correct gross errors in autosegmentation.
Identifiers
What Socratic 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 Socratic graph.