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Predicting Successful Voluntary Breath-Hold Candidates by Monitoring Breathing During Simulation

T Nano1, D Capaldi2, M Feng1, T Solberg1, A Witztum1*, (1) University of California, San Francisco, San Francisco, CA, (2) Stanford University, Stanford, CA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: To develop a method of predicting good candidates for voluntary breath-hold (BH) stereotactic body radiation treatment (SBRT) using spirometry-based breathing waveforms acquired at CT simulation.


Methods: A total of 20 patients under breathing control using SDX (Dyn’r Medical Systems Aix-en-Provence, France) were tracked from CT simulation to treatment. Treatment sites included were liver (n=10), pancreas (n=4), mediastinum (n=3), lung (n=3), and kidney (n=1). During simulation and each fraction, BH waveforms were recorded and quantitative metrics extracted to assess treatment including: BH time range, number of missed holds (BHs shorter than 2 seconds), and number of failed holds (BHs longer than 2 seconds but shorter than mean BH minus 5 seconds). For each patient, the number of missed and failed breath-holds during simulation and treatment were compared.


Results: During CT simulation, 13/20 patients had at least one missed BH, 9/20 patients had at least one failed BH, and 7/20 of these had both. There were 23 misses (16.4%) and 14 fails (10%) in 140 total simulation BHs. During treatment, there were 21 missed (2.8%) and no failed BH in 740 total BHs, with 10/20 (50%) of patients having at least one missed BH. All patients that had no missed or failed BHs during simulation (n=5) also had no missed/failed BHs during treatment (sensitivity=100%). 10/15 patients that had at least 1 missed/failed BH during simulation had at least 1 missed/failed BH during treatment (specificity=50%). 3/15 also had lower BH time-ranges (<25s) during simulation which further improves specificity to 80%.


Conclusion: Inconsistent breath-holds during CT simulation were accurately used to predict inconsistent breathing during treatment, even though there is a break between simulation and treatment. We have developed a method of identifying good candidate for breath-hold treatments and those candidates that would benefit from additional monitoring and breathing training.

Download ePoster [PDF]

Keywords

Quality Assurance, Respiration

Taxonomy

TH- External Beam- Photons: Motion management - interfraction

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