S1547
Physics - Autosegmentation
ESTRO 2026
1 Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands. 2 Department of Artificial Intelligence, Bernoulli Institute of Mathematics, Groningen, Netherlands. 3 Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden. 4 Department of Translational Medicine, Lund University, Malmö, Sweden. 5 Department of Radiation Oncology, University Medical Center Groningen, Groningen, Netherlands Purpose/Objective: Uncertainty maps can be used to quantify and visualise the estimated confidence of Deep Learning (DL) models in contouring predictions. It has been hypothesised that such maps can support clinicians during manual review, potentially reducing editing time. However, uncertainty maps are not currently presented in clinical practice, and data regarding their influence on clinical decision-making remains limited [1]. This study investigates the impact of simulated uncertainty maps on clinical behaviour during manual editing of high-quality mesorectum CTV contours in rectal cancer radiotherapy. Material/Methods: A retrospective dataset of ten rectal cancer patients, used in an earlier assessment of inter-observer variability (IOV) [2], was utilised. Each patient had five independent manual CTV contours, from which one was randomly selected as surrogate ‘DL contour’, while the inter-observer variation served as ‘DL uncertainty map’. This design allowed to focus on the impact of meaningful uncertainty maps using well-defined contours. Six clinicians participated in two editing phases, two months apart. In a within-subject, counterbalanced design, they manually edited 10 contours per phase, presented in random order, 5 with and 5 without theuncertainty maps. Clinicians were told that both contours and maps were created by a DL-model. To gather qualitative feedback on user experience, questionnaires were completed during each phase, followed by one-on-one interviews (Figure 1). Editing times were extracted from screen recordings, and editing amount quantified using the added path length (APL).
Results: Median editing time per patient did not differ with the use of uncertainty maps (4.2 ± 3.3 min vs. 4.1 ± 3.3 min), but was significantly shorter in Phase 2 (3.4 ± 2.1 min) compared to Phase 1 (6.2 ± 3.4 min) (p < 0.01; Figure 2a). Similarly, APL results indicated comparable editing amount with and without uncertainty maps, yet significantly decreased in Phase 2 (1.1 ± 3.5 cm³) compared to Phase 1 (3.0 ± 3.6 cm³) (p < 0.01; Figure 2b). Questionnaire and interview findings suggested that editing behaviour was influenced more by workload, memory and anchoring biases, mind-set, mood, and learning effect from task repetition, rather than by uncertainty maps. Clinicians reported limited trust in the uncertainty maps, using them primarily for confirmation rather than decision-making. Nonetheless, clinicians acknowledged potential value for low-quality contours if trust is established.
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