A multi-centre evaluation of deep learning based radiotherapy planning for left-sided node-negative breast cancer

Abstract

Background and Purpose

Deep learning based planning (DLP) has the potential to improve consistency and efficiency in radiotherapy treatment planning. However, its clinical applicability remains limited, partly due to the need to translate a predicted dose into a deliverable dose. This study evaluated the generalisability of an institution specific DLP solution across multiple institutions by assessing its performance and developing a standardised translation parameter set.

Materials and Methods

Four institutions provided clinical treatment plans of 15 patients with left-sided node-negative breast cancer. Treatment plans delivering 40.05 Gy were generated using a deep learning prediction model trained on data from one institution. External validation was performed using national consensus criteria, by applying the initial parameter settings (InitialMimick) to datasets (n = 45) from three other institutions. A standardised parameter set (GenericMimick) was then developed based on data (n = 12) from all four institutions, whereafter it was evaluated on the remaining 48 patients of the dataset.

Results

InitialMimick plans showed higher average dose values in the planning target volume for the D_mean (40.5 vs. 40.1 Gy) and D_2% (42.4 vs. 41.4 Gy), with fewer cases meeting all clinical goals (15/45) compared to clinical plans (25/45). After parameter adjustment, GenericMimick plans resulted in more plans meeting all goals (28/48), comparable to the clinical plans (30/48), with D_mean of 40.3 vs. 40.1 Gy and D_2% of 41.9 vs. 41.5 Gy. Mean differences in organs at risk mean doses were less than 0.2 Gy.

Conclusion

DLP with a standardised translation parameter set demonstrated general applicability across multiple institutions.