Multi-center dosimetric predictions to improve plan quality for brachytherapy for cervical cancer treatment

Dominique M.W. Reijtenbagh*, Jérémy Godart, Astrid A.C. de Leeuw, Ina M. Jürgenliemk-Schulz, Jan Willem M. Mens, Michèle Huge, Mischa S. Hoogeman

*Corresponding author for this work

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Background and purpose: Image-guided adaptive brachytherapy (IGABT) is an important modality in the cervical cancer treatment, and plan quality is sensitive to time pressure in the workflow. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) has been demonstrated to detect suboptimal plans (outliers). This analysis quantifies the possible improvement of plans detected as outliers, and investigates its suitability as a clinical QA tool in a multi-center setting. Materials and methods: In previous work OVH-based models were investigated for the use of QA. In this work a total of 160 plans of 68 patients treated in accordance with the current state-of-the-art IGABT protocol from Erasmus MC (EMC) were analyzed, with a model based on 120 plans (60 patients) from UMC Utrecht (UMCU). Machine-learning models were trained to define QA thresholds, and to predict dose D2cm3 to bladder, rectum, sigmoid and small bowel with the help of OVHs of the EMC cohort. Plans out of set thresholds (outliers) were investigated and retrospectively replanned based on predicted D2cm3 values. Results: Analysis of replanned plans demonstrated a median improvement of 0.62 Gy for all Organs At Risk (OARs) combined and an improvement for 96 % of all replanned plans. Outlier status was resolved for 36 % of the replanned plans. The majority of the plans that could not be replanned were reported having implantation complications or insufficient coverage due to tumor geometry. Conclusion: OVH-based QA models can detect suboptimal plans, including both unproblematic BT applications and suboptimal planning circumstances in general. OVH-based QA models demonstrate potential for clinical use in terms of performance and user-friendliness, and could be used for knowledge transfer between institutes. Further research is necessary to differentiate between (sub)optimal planning circumstances.

Original languageEnglish
Article number109518
JournalRadiotherapy and Oncology
Publication statusPublished - May 2023

Bibliographical note

Funding Information:
This work was in part funded by a research grant of Elekta AB (Stockholm, Sweden). The funders had no role in study design, data collection and analysis, and decisions on preparation of the manuscript. Erasmus MC Cancer Institute also has research collaborations with Accuray Inc, Sunnyvale, USA and Varian Medical Systems Particle Therapy GmbH & Co. KG, Troisdorf, Germany. Dr. Hoogeman reports a membership of the advisory board Accuray, Sunnyvale, USA.

Publisher Copyright:
© 2023 The Author(s)


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