A multi-centre evaluation of deep learning based radiotherapy planning for left-sided node-negative breast cancer
Physics and Imaging in Radiation Oncology, vol. 36, pp. 100839
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.
Authors 13
-
Marlie Besouw corresponding
Radboud University Nijmegen · Catharina Ziekenhuis · Radboud University Medical Center
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
Radboudumc, Department of Medical Imaging, Nijmegen, the Netherlands
-
Catharina Ziekenhuis · Eindhoven University of Technology
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
Faculty of Electrical Engineering, Technical University Eindhoven, Eindhoven, the Netherlands
-
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
-
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
-
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
-
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
-
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
-
Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum
Affiliation as printed
Haaglanden Medical Centre, Department of Radiation Oncology, The Hague, the Netherlands
-
Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum
Affiliation as printed
Haaglanden Medical Centre, Department of Radiation Oncology, The Hague, the Netherlands
-
Leiden University · Leiden University Medical Center
Affiliation as printed
Leiden University Medical Centre, Department of Radiation Oncology, Leiden, the Netherlands
-
Leiden University · Leiden University Medical Center
Affiliation as printed
Leiden University Medical Centre, Department of Radiation Oncology, Leiden, the Netherlands
-
Affiliation as printed
Radiotherapiegroep, Institution for Radiation Oncology, Arnhem, the Netherlands
-
Catharina Ziekenhuis · Eindhoven University of Technology
Affiliation as printed
Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
Faculty of Applied Physics, Technical University Eindhoven, Eindhoven, the Netherlands
Faculty of Electrical Engineering, Technical University Eindhoven, Eindhoven, the Netherlands
Cited by 0 stored of 0
No patents citing this paper on Lens.org (checked 2026-10-11).