Treatment planning for lung cancer reirradiation accounting for previously delivered dose.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
6 patients, versus 3/6 with the manual method, suggesting enhanced and potentially more efficient reirradiation planning.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
The integrated automated approach delivered higher target doses within organ-at-risk limits, maintained robustness, and was qualitatively preferable in blinded comparison. It produced clinically acceptable dose distributions for 5/6 patients, versus 3/6 with the manual method, suggesting enhanced and potentially more efficient reirradiation planning.
Reirradiation is increasingly used for recurrent and new primary cancers, but dedicated planning tools are limited.
APA
Walton DP, Thompson C, et al. (2026). Treatment planning for lung cancer reirradiation accounting for previously delivered dose.. Physics and imaging in radiation oncology, 38, 100912. https://doi.org/10.1016/j.phro.2026.100912
MLA
Walton DP, et al.. "Treatment planning for lung cancer reirradiation accounting for previously delivered dose.." Physics and imaging in radiation oncology, vol. 38, 2026, pp. 100912.
PMID
41853124 ↗
Abstract 한글 요약
Reirradiation is increasingly used for recurrent and new primary cancers, but dedicated planning tools are limited. We integrated deformable image registration and radiobiologically consistent dose accumulation into plan optimisation to improve plan quality and robustness, applying this approach to six lung cancer cases and comparing it with a manual, maximum point-dose method. The integrated automated approach delivered higher target doses within organ-at-risk limits, maintained robustness, and was qualitatively preferable in blinded comparison. It produced clinically acceptable dose distributions for 5/6 patients, versus 3/6 with the manual method, suggesting enhanced and potentially more efficient reirradiation planning.
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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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