S2901
RTT- RTT operational practice and workflow innovations
ESTRO 2026
from truncated cardiac structures. The overall goal was to determine a metric that best predicts MHD for CBCT-to-synthetic CT (sCT) frameworks, enabling workflow-friendly, vendor neutral, RTT-led daily heart- dose monitoring and adaptive decision-making, even with incomplete cardiac imaging.
Conclusion: Multimodal FMEA enhances the robustness and clinical relevance of risk assessment in brachytherapy planning. While RPN provides a useful baseline, AP offers finer prioritization aligned with clinical urgency. RM supports stakeholder communication but benefits from integration with AP. A combined approach is recommended to strengthen safety culture, optimize workflows, and reduce preventable errors. References: 1. DGMP-Bericht Nr. 25: Eine Prozessbeschreibung zur Umsetzung des Risikomanagements für die Strahlenbehandlung gemäß §126 StrlSchV, 2022. 2. DGMP-Bericht Nr. 28: Eine Durchführungshilfe zur Umsetzung des Risikomanagements für die Strahlenbehandlung gemäß § 126 StrlSchV: Prozessbasierter Ansatz, 2024.3. Huq MS, Fraass BA, Dunscombe PB, Gibbons Jr JP, Ibbott GS, Mundt AJ, et al. The report of Task Group 100 of the AAPM: application of risk analysis methods to radiation therapy quality management. Med Phys 2016;43(7):4209. Keywords: FMEA, patient safety, workflow Proffered Paper 554 Integrating Mean Heart Dose Surrogates into an AI-Compatible Workflow for Daily Cardiac Monitoring in Breast Radiotherapy Rhoda Broni 1 , Aodh MacGairbhith 1 , Caitriona Kelly 1 , Jill Nicholson 1,2 , Frances Duane 1,2 , Ciaran Malone 1,3 1 St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland. 2 Trinity St. James’s Cancer Institute, Trinity College Dublin, Dublin, Ireland. 3 Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, Netherlands Purpose/Objective: Estimating mean heart dose (MHD) during breast radiotherapy is challenging when cone-beam CT (CBCT) field-of-view (FOV) only partially visualizes the heart, a limitation that persists even with AI-based synthetic imaging for monitoring and adaptation (Figure 1). This study aimed to identify robust, dosimetric surrogates for accurately estimating MHD
Material/Methods: 59 patients (48 deep inspiration breath-hold (DIBH), 11 free-breathing (FB)) receiving 40 Gy in 15 fractions were retrospectively analysed to represent a broad range of cardiac positions and dose levels of clinical variability. Full-heart structures were contoured on planning CTs, while limited-heart structures were created by constraining the full-heart contours to the CBCT FOV, simulating truncation during treatment. Heart structures were shifted 3, 5 and 7 mm toward the treatment fields to simulate typical treatment variability, resulting in 205 structures (original + shifted). Practical dose-volume surrogates (D20cc_lim, D40cc_lim, D100cc_lim) and limited mean heart dose (MHD_lim) were derived and compared with true MHD from planning CTs. Pearson (r) and Spearman ( ρ ) correlations and limited-to-full heart volume ratios assessed consistency and robustness across truncation levels. Results: All limited-heart surrogate metrics showed strong and statistically significant correlations with MHD (p < 1e- 4). D20cc_lim and D40cc_lim demonstrated strong correlations (r 0.81–0.85, ρ 0.85–0.88; R ² 0.65–0.72, RMSE 0.69–0.77Gy). 100cc_lim remained moderate to strong (r 0.77, ρ 0.75; R ² 0.59; RMSE 0.83 Gy). MHD_lim showed the strongest correlation (r 0.956, ρ 0.948; R ² 0.914, RMSE 0.381 Gy), particularly within the 1–3 Gy range, where linearity was maintained with minimal residuals (Figure 2). Limited heart volumes ranged from 15.5%–94.3% of the full-heart volume (51.7 ± 20.6%), yet MHD_lim remained highly predictive. Importantly, strong correlations were maintained across both DIBH and FB cohorts.
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