Evidence map›Paper›PMID 41141224›Full record

ArticlePhysics and imaging in radiation oncology2025

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, Leila Shelley, Thomas Berger, Raj Jena, Duncan B McLaren, Neil G Burnet, William H Nailon

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Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Zhuolin YangDepartment of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.
David J NobleEdinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Sarah ElliotDepartment of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.
Leila ShelleyDepartment of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.
Thomas BergerDepartment of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.
Raj JenaThe University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK.
Duncan B McLarenEdinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Neil G BurnetProton Centre, The Christie NHS Foundation Trust, Manchester M20 4BX, UK.
William H NailonDepartment of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Adaptive radiotherapyMachine learningProstate cancerRadiomics

Identifiers

PMID41141224
PMCPMC12547930

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.