ESTRO 2024 - Abstract Book

S6018

RTT - Treatment planning, OAR and target definitions

ESTRO 2024

observers. Autocontouring software, such as Limbus, has the potential to reduce the workload and enhance efficiency. This study aims to assess the performance of Limbus software in the context of breast cancer by comparing its automated contours with two other datasets: contours generated by Limbus and subsequently adjusted manually, and entirely manual contours.

Material/Methods:

A total of 44 breast cancer patients who underwent adjuvant radiotherapy following breast-conserving surgery were included in the study. Organs at risk (OARs) were delineated using three different methods: (1) autocontours generated by Limbus, (2) autocontours generated by Limbus with further manual refinement, and (3) completely manual contours. The accuracy of OAR volumes was evaluated using the Dice similarity coefficient (DSC). Time-saving implications were also considered.

Results:

The DSC values were compared among the three contouring methods using the paired t-test, with statistical significance set at p < 0.05. The findings indicated that Limbus-generated autocontours, even when further adjusted manually, demonstrated comparable accuracy to manual contours in terms of OAR volumes (refer to Table 1). Additionally, the study observed a time-saving benefit in the process of adjusting Limbus software-generated contours (average time: 8 minutes; range: 2-29 minutes) compared to manual contouring (average time: 20 minutes; range: 10-36 minutes).

Conclusion:

Limbus software for automated contouring in breast cancer treatment exhibited promising outcomes, offering accurate and time-efficient delineation of target volumes and OARs. The software performed on a par with manual contouring, suggesting its potential to reduce inter-observer variations and the overall workload in the planning phase. Further studies with larger sample sizes and validation across multiple medical centers are advisable to confirm these findings.

Keywords: autocontouring software, breast cancer

2921

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Sophie E Alexander 1,2 , Robert A Mitchell 3 , Kian Morrison 1 , Jayde Nartey 1 , Rosalyne Westley 1,2 , Uwe Oelfke 3 , Helen A McNair 1,2 , Alison C Tree 1,2