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Rule-based AI automated adaptive treatment planning for image guided cervical cancer brachytherapy.

作者信息

Rossi Linda, Bijman Rik, Chopra Supriya, Mittal Prachi, Panda Subhajit, Westerveld Henrike, Christianen Miranda, Kolkman-Deurloo Inger-Karine, Breedveld Sebastiaan, Nout Remi, Heijmen Ben

机构信息

Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.

Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.

出版信息

Brachytherapy. 2025 Sep-Oct;24(5):711-720. doi: 10.1016/j.brachy.2025.05.007. Epub 2025 Jul 10.

DOI:10.1016/j.brachy.2025.05.007
PMID:40640063
Abstract

BACKGROUND AND PURPOSE

A rule-based AI system for automated adaptive treatment planning for image guided adaptive brachytherapy (IGABT) of locally advanced cervical cancer (LACC) was developed at Erasmus MC, and internally and externally validated by Erasmus MC and Tata Memorial Centre (TMC), respectively.

MATERIALS AND METHODS

The BiCycle system generates automated plans with adapted requirements for each fraction, considering previously delivered external beam radiotherapy (EBRT) and BT doses, according EMBRACE-II protocol. It optimizes dosimetric parameters and loading patterns for available radioactive source positions. The system's effectiveness was validated by comparing automatically generated plans (AUTO) with manually generated, clinically delivered plans (MANUAL) of (1) dosimetry parameters and loading pattern visual inspection of 15 previously treated patients, for internal validation and (2) dosimetry and qualitative comparison by two TMC physicians of 20 previously treated patients, for external validation.

RESULTS

With comparable target doses, AUTO plans had reduced D (expressed as EBRT + BT total EQD2) for bladder, rectum, sigmoid and bowel compared to MANUAL plans with average gains of 5.3 Gy, 2.4 Gy, 2.5 Gy and 2.7 Gy, respectively, for internal validation, and of 3.6 Gy, 4.3 Gy, 1.9 Gy and 1.1 Gy, respectively, for external validation. The two TMC physicians preferred the AUTO plans in 76.3% and 75.0% of comparisons.

CONCLUSION

A novel AI-system for fully automated IGABT treatment planning for LACC allowed high-quality plan optimization in on average 1.6 min. AUTO plans were considered superior in quality compared to MANUAL plans in both internal and external validations, even without optimizing the system's configuration for the external center.

摘要

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