Evidence map›Paper›PMID 40575283›Full record

ArticleBJR open2025

A unified workflow for classifying patterns of locoregional failure using radiotherapy treatment planning dose distributions.

Ceilidh Welsh, Karl Harrison, Sara Lightowlers, Ian Gleeson, Alfred J W Beard, Emma Harris, Gillian C Barnett, Rajesh Jena

Abstract read
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Article in BJR open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ceilidh WelshDepartment of Oncology, University of Cambridge, Cambridge, Cambridge Biomedical Campus, CB2 0AH, United Kingdom.ORCID https://orcid.org/0000-0002-7332-7392
Karl HarrisonCavendish Laboratory, University of Cambridge, Cambridge, CB3 0HE, United Kingdom.
Sara LightowlersDepartment of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, United Kingdom.
Ian GleesonMedical Physics, Cambridge University NHS Foundation Trust, Cambridge, CB2 0QQ, United Kingdom.
Alfred J W BeardSchool of Clinical Medicine, Addenbrookes Hospital, Cambridge, CB2 0QQ, United Kingdom.
Emma HarrisThe Joint Department of Physics, Institute of Cancer Research, London, SW7 3RP, United Kingdom.
Gillian C BarnettDepartment of Oncology, University of Cambridge, Cambridge, Cambridge Biomedical Campus, CB2 0AH, United Kingdom.
Rajesh JenaDepartment of Oncology, University of Cambridge, Cambridge, Cambridge Biomedical Campus, CB2 0AH, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This work describes a unified workflow for classifying patterns of locoregional recurrence (LRR) using radiotherapy planning dose distributions. This approach aims to incorporate dose parameters into LRR classifications and facilitate application across different treatment sites and dose prescriptions to standardise classification terminology. Methods: The relapse diagnostic CT (rCT) and manually delineated relapse gross tumour volume (rGTV) were co-registered with the radiotherapy planning CT (pCT) using deformable image registration (DIR). The DIR accuracy was quantified using the target registration error (TRE) using the absolute centroid distance between cancer site-specific regions of interest (ROIs). Dosimetric structures were delineated for planning regions receiving 95% of the dose prescribed to high-risk, intermediate-risk, and low-risk CTVs, relative to the cancer site or trial. The mapped rGTV was compared relative to each dose structure and classified into one of five categories: central and peripheral high-dose (Type A, Type B), central and peripheral elective-dose (Type C, Type D), and extraneous dose (Type E) failures. Results: The unified workflow was successfully implemented on two different patient use cases, one from the IMPORT HIGH breast cancer trial, one from the VoxTox head-and-neck study, classifying LRR as Type A and Type E failures, respectively. Conclusion: This workflow for classifying LRR is applicable across different cancer sites, despite differences in treatment protocol, target dose, and dose delivery. This provides a basis for utilising radiotherapy dose distributions to analyse patterns of failure irrespective of trial design or cancer-site. Advances in knowledge: Standardised classifications of LRR that are correlated with the planning dose distribution could provide insight into the underlying causes of LRR burden post-radiotherapy and allow for critical evaluation of the current concepts of defined clinical tumour volumes and optimal PTV dose regions.

Indexed as

image analysisimage registrationlocal recurrencemethodologyradiotherapy

Identifiers

PMID40575283
PMCPMC12201982

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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.