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Script-based automatic radiotherapy planning for cervical cancer.
- Source :
- Acta Oncologica; Dec2023, Vol. 62 Issue 12, p1798-1807, 10p
- Publication Year :
- 2023
-
Abstract
- This study aimed to develop fully automated script-based radiotherapy treatment plans for cervical cancer patients, and evaluate them against clinically accepted plans, as validation before clinical implementation. In this retrospective planning study, treatment plans for 25 locally advanced cervical cancer (LACC) patients with up to three dose levels were included. Fully automated plans were created using an in-house developed Python script in RayStation, and compared to clinically accepted manually made plans. Quantitatively, relevant dose statistics were compared, and average dose volume histograms (DVHs) were analyzed. Qualitatively, a blinded plan comparison was conducted between the clinical and automatic plans. The accuracy of treatment plan delivery was verified with the Delta4 Phantom+. The quantitative evaluation showed that target coverage was acceptable for all the automatic and clinical plans. The automatic plans were significantly more conformal than the clinical plans; median of 1.03 vs. 1.12. Mean doses to almost all organs at risk (OARs) were reduced in the automatic plans, with a median reduction of between 0.6 Gy and 1.9 Gy. In the blinded plan comparison, the automatic plans were the preferred plans or of equal quality as the clinical plans in 99% of the cases. In addition, plan delivery was excellent, with a mean gamma passing rate of 99.8%. Complete script-based plans were generated in 30–45 min; about four to ten times faster than manually made plans. The automatic plans had acceptable target coverage, lower doses to almost all OARs, more conformal dose distributions, and were predominantly preferred by the clinicians. Based on these results, our institution has implemented the script for clinical use. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0284186X
- Volume :
- 62
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Acta Oncologica
- Publication Type :
- Academic Journal
- Accession number :
- 173858273
- Full Text :
- https://doi.org/10.1080/0284186X.2023.2267171