医学
临床终点
肺癌
放射治疗
核医学
内科学
临床试验
肿瘤科
作者
Saskia A. Cooke,Dirk De Ruysscher,Bart Reymen,Maarten Lambrecht,G. Persson,C. Faivre‐Finn,Edith Dieleman,Rolf Lewensohn,Judi N.A. van Diessen,Karolina Sikorska,Ferry Lalezari,Wouter V. Vogel,Wouter van Elmpt,E. Damen,Jan‐Jakob Sonke,J. Belderbos
标识
DOI:10.1016/j.radonc.2023.109492
摘要
We aimed to assess if radiation dose escalation to either the whole primary tumour, or to an 18F-FDG-PET defined subvolume within the primary tumour known to be at high risk of local relapse, could improve local control in patients with locally advanced non-small-cell lung cancer.Patients with inoperable, stage II-III NSCLC were randomised (1:1) to receive dose-escalated radiotherapy to the whole primary tumour or a PET-defined subvolume, in 24 fractions. The primary endpoint was freedom from local failure (FFLF), assessed by central review of CT-imaging. A phase II 'pick-the-winner' design (alpha = 0.05; beta = 0.80) was applied to detect a 15 % increase in FFLF at 1-year.gov:NCT01024829.150 patients were enrolled. 54 patients were randomised to the whole tumour group and 53 to the PET-subvolume group. The trial was closed early due to slow accrual. Median dose/fraction to the boosted volume was 3.30 Gy in the whole tumour group, and 3.50 Gy in the PET-subvolume group. The 1-year FFLF rate was 97 % (95 %CI 91-100) in whole tumour group, and 91 % (95 %CI 82-100) in the PET-subvolume group. Acute grade ≥ 3 adverse events occurred in 23 (43 %) and 20 (38 %) patients, and late grade ≥ 3 in 12 (22 %) and 17 (32 %), respectively. Grade 5 events occurred in 19 (18 %) patients in total, of which before disease progression in 4 (7 %) in the whole tumour group, and 5 (9 %) in the PET-subvolume group.Both strategies met the primary objective to improve local control with 1-year rates. However, both strategies led to unexpected high rates of grade 5 toxicity. Dose differentiation, improved patient selection and better sparing of central structures are proposed to improve dose-escalation strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI