Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

Luximon, Dishane C.; Neylon, John; Lamb, James

doi:10.1016/j.phro.2023.100427

Cited by 2 publications

(1 citation statement)

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“…An anatomical region labeling (ARL) model 17 was used to stream CBCT images to the appropriate anatomy‐specific pipeline. The ARL model was trained and tested on the UCLA patient datasets.…”

Section: Methodsmentioning

confidence: 99%

Proof‐of‐concept study of artificial intelligence‐assisted review of CBCT image guidance

Neylon

Luximon

Ritter

et al. 2023

J Applied Clin Med Phys

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PurposeAutomation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof‐of‐concept clinical implementation of an AI‐assisted review of CBCT registrations used for patient setup.MethodsAn automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45‐day period, 1357 pre‐treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day's registrations were produced. Initial action levels targeted 10% of cases to highlight for in‐depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI‐model performance.ResultsFollowing an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.ConclusionIn this work, we describe the implementation of an automated AI‐analysis pipeline for daily quantitative analysis of CBCT‐guided patient setup registrations. The AI‐model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors’ knowledge, there are no previous works performing AI‐assisted assessment of pre‐treatment CBCT‐based patient alignment.

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Section: Methodsmentioning

confidence: 99%