2025-12-11 エディンバラ大学
<関連情報>
- https://www.ed.ac.uk/news/scans-detect-early-prostate-radiotherapy-changes
- https://www.phiro.science/article/S2405-6316(25)00155-1/fulltext
正常組織イメージングバイオマーカーを用いて前立腺癌放射線治療における直腸毒性を最小限に抑えるための適応的再計画の最適な時期を特定する Identifying the optimal time point for adaptive re-planning in prostate cancer radiotherapy to minimise rectal toxicity using normal tissue imaging biomarkers
Zhuolin Yang ∙ David J. Noble ∙ Sarah Elliot ∙ … ∙ Duncan B McLaren ∙ Neil G. Burnet ∙ William H. Nailon
Physics and Imaging in Radiation Oncology Published:October 8, 2025
DOI:https://doi.org/10.1016/j.phro.2025.100850
Highlights
- Radiomic features before and during treatment predict late rectal bleeding.
- Radiomic features from week 1 showed strongest standalone predictive performance.
- Week 3 was optimal for re-planning in patients treated with 74 Gy in 37 fractions.
- Radiomics enable biologically informed adaptation beyond anatomy-based methods.
- Analysis includes both standard and moderately hypofractionated treatment regimens.
Graphical abstract
Abstract
Background and purpose
Adaptive radiotherapy (ART) in prostate cancer (PCa), although not yet standard practice, is typically triggered by inter-fractional anatomical changes that emerge progressively during treatment. This study investigates whether radiomics extracted before and during treatment can identify the optimal time point for re-planning, with the goal of reducing late rectal bleeding.
Materials and methods
This study included 187 PCa patients from the single-centre, prospectively collected VoxTox dataset (UK-CRN-ID-13716), treated with image-guided radiotherapy using TomoTherapy and daily MVCT. Patients received either 74 Gy in 37 fractions (N = 110) or 60 Gy in 20 fractions (N = 77). Radiomic features were extracted from pre-treatment planning CTs and daily MVCTs. Grade ≥ 1 rectal bleeding was assessed at 2 years post-treatment using CTCAE v4.03. Two analysis strategies were employed: a separate analysis, where weekly features were evaluated independently; and a cumulative analysis, which progressively incorporated features from previous weeks. Logistic regression models with elastic net penalty were trained and evaluated using AUC.
Results
In both groups, week 1 provided the highest standalone predictive performance (test AUC = 0.766 for 74 Gy; 0.734 for 60 Gy). In the cumulative analysis, week 3 was optimal for the 74 Gy group (test AUC = 0.767), balancing performance and timing. For the 60 Gy group, week 1 remained optimal but suffered from reduced generalisability (test AUC = 0.643).
Conclusions
Radiomic analysis of daily imaging can support early, proactive ART in PCa, offering a personalised strategy to reduce late rectal bleeding beyond conventional anatomy-based approaches.
