ArticleQuantitative imaging in medicine and surgery2021
Assessing the robustness of artificial intelligence powered planning tools in radiotherapy clinical settings-a phantom simulation approach.
Article in Quantitative imaging in medicine and surgery, 2021. 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, 2 citations in OpenAlex.
- Evaluation and tuning of a commercial automated planning system for prostate radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Planning issues on linac-based stereotactic radiotherapy.World journal of clinical cases · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) based radiotherapy treatment planning tools have gained interest in automating the treatment planning process. It is essential to understand their overall robustness in various clinical scenarios. This is an existing gap between many AI based tools and their actual clinical deployment. This study works to fill the gap for AI based treatment planning by investigating a clinical robustness assessment (CRA) tool for the AI based planning methods using a phantom simulation approach.
methodsA cylindrical phantom was created in the treatment planning system (TPS) with the axial dimension of 30 cm by 18 cm. Key structures involved in pancreas stereotactic body radiation therapy (SBRT) including PTV25, PTV33, C-Loop, stomach, bowel and liver were created within the phantom. Several simulation scenarios were created to mimic multiple scenarios of anatomical changes, including displacement, expansion, rotation and combination of three. The goal of treatment planning was to deliver 25 Gy to PTV25 and 33 Gy to PTV33 in 5 fractions in simultaneous integral boost (SIB) manner while limiting luminal organ-at-risk (OAR) max dose to be under 29 Gy. A previously developed deep learning based AI treatment planning tool for pancreas SBRT was identified as the validation object. For each scenario, the anatomy information was fed into the AI tool and the final fluence map associated to the plan was generated, which was subsequently sent to TPS for leaf sequencing and dose calculation. The final auto plan's quality was analyzed against the treatment planning constraint. The final plans' quality was further analyzed to evaluate potential correlation with anatomical changes using the Manhattan plot.
resultsA total of 32 scenarios were simulated in this study. For all scenarios, the mean PTV25 V25Gy of the AI based auto plans was 96.7% while mean PTV33 V33Gy was 82.2%. Large variation (16.3%) in PTV33 V33Gy was observed due to anatomical variations, a.k.a. proximity of luminal structure to PTV33. Mean max dose was 28.55, 27.68 and 24.63 Gy for C-Loop, bowel and stomach, respectively. Using D
conclusionsThe results showed promising robustness of the pancreas SBRT AI tool, providing important evidence of its readiness for clinical implementation. The established workflow could guide the process of assuring clinical readiness of future AI based treatment planning tools.
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.