Evidence map›Paper›PMID 38948455›Full record

ArticleBJR open2024

Effect of synthetic CT on dose-derived toxicity predictors for MR-only prostate radiotherapy.

Christopher Thomas, Isabel Dregely, Ilkay Oksuz, Teresa Guerrero Urbano, Tony Greener, Andrew P King, Sally F Barrington

Registry-linked trialAbstract read
In one paragraph

Article in BJR open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03238170 (An Exploratory Study to Assess the Feasibility of Incorporating MR in the Radiotherapy Pathway of Patients With Prostate Cancer), which is not on this map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03238170 naunknown statusnot on this map

An Exploratory Study to Assess the Feasibility of Incorporating MR in the Radiotherapy Pathway of Patients With Prostate Cancer

TypeinterventionalSponsorGuy's and St Thomas' NHS Foundation TrustRan2017 to 2023Enrolled40ConditionsCancer of the PROSTATEArmsMRI scan
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Christopher ThomasSchool of Biomedical Engineering & Imaging Sciences, King's College London, SE17EH London, United Kingdom.
Isabel DregelySchool of Biomedical Engineering & Imaging Sciences, King's College London, SE17EH London, United Kingdom.ORCID https://orcid.org/0000-0003-4146-7363
Ilkay OksuzSchool of Biomedical Engineering & Imaging Sciences, King's College London, SE17EH London, United Kingdom.ORCID https://orcid.org/0000-0001-6478-0534
Teresa Guerrero UrbanoClinical Oncology, Guy's and St Thomas' Hospital NHS Foundation Trust, SE17EH London, United Kingdom.ORCID https://orcid.org/0000-0003-3090-541X
Tony GreenerMedical Physics Department, Guy's and St Thomas' Hospital NHS Foundation Trust, SE17EH London, United Kingdom.
Andrew P KingSchool of Biomedical Engineering & Imaging Sciences, King's College London, SE17EH London, United Kingdom.ORCID https://orcid.org/0000-0002-9965-7015
Sally F BarringtonSchool of Biomedical Engineering & Imaging Sciences, King's College London, SE17EH London, United Kingdom.ORCID https://orcid.org/0000-0002-2516-5288

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Toxicity-driven adaptive radiotherapy (RT) is enhanced by the superior soft tissue contrast of magnetic resonance (MR) imaging compared with conventional computed tomography (CT). However, in an MR-only RT pathway synthetic CTs (sCT) are required for dose calculation. This study evaluates 3 sCT approaches for accurate rectal toxicity prediction in prostate RT. Methods: Thirty-six patients had MR (T2-weighted acquisition optimized for anatomical delineation, and T1-Dixon) with same day standard-of-care planning CT for prostate RT. Multiple sCT were created per patient using bulk density (BD), tissue stratification (TS, from T1-Dixon) and deep-learning (DL) artificial intelligence (AI) (from T2-weighted) approaches for dose distribution calculation and creation of rectal dose volume histograms (DVH) and dose surface maps (DSM) to assess grade-2 (G2) rectal bleeding risk. Results: Maximum absolute errors using sCT for DVH-based G2 rectal bleeding risk (risk range 1.6% to 6.1%) were 0.6% (BD), 0.3% (TS) and 0.1% (DL). DSM-derived risk prediction errors followed a similar pattern. DL sCT has voxel-wise density generated from T2-weighted MR and improved accuracy for both risk-prediction methods. Conclusions: DL improves dosimetric and predicted risk calculation accuracy. Both TS and DL methods are clinically suitable for sCT generation in toxicity-guided RT, however, DL offers increased accuracy and offers efficiencies by removing the need for T1-Dixon MR. Advances in knowledge: This study demonstrates novel insights regarding the effect of sCT on predictive toxicity metrics, demonstrating clear accuracy improvement with increased sCT resolution. Accuracy of toxicity calculation in MR-only RT should be assessed for all treatment sites where dose to critical structures will guide adaptive-RT strategies. Clinical trial registration number: Patient data were taken from an ethically approved (UK Health Research Authority) clinical trial run at Guy's and St Thomas' NHS Foundation Trust. Study Name: MR-simulation in Radiotherapy for Prostate Cancer. ClinicalTrials.gov Identifier: NCT03238170.

Indexed as

artificial intelligencedeep learningprostaterectal toxicitysynthetic CT

Identifiers

PMID38948455
PMCPMC11213647

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Registered trials

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.