Evidence map›Paper›PMID 41141225›Full record

ArticlePhysics and imaging in radiation oncology2025

Early response prediction during radiotherapy in rectal cancer using sequential diffusion-weighted imaging at a magnetic resonance image-guided linear accelerator.

Johanna A Hundvin, Jonas Habrich, Cihan Gani, Jörg Assmus, Inger Marie Løes, Sara Pilskog, Kathrine R Redalen, Daniela Thorwarth

Abstract read
In one paragraph

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. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

8 authors.

Johanna A HundvinCancer Clinic, Haukeland University Hospital, Bergen, Norway.
Jonas HabrichSection for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
Cihan GaniDepartment of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
Jörg AssmusCentre for Clinical Research, Haukeland University Hospital, Bergen, Norway.
Inger Marie LøesCancer Clinic, Haukeland University Hospital, Bergen, Norway.
Sara PilskogCancer Clinic, Haukeland University Hospital, Bergen, Norway.
Kathrine R RedalenDepartment of Physics, Norwegian University of Science and Technology, Trondheim, Norway.
Daniela ThorwarthSection for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: Varying response to chemoradiotherapy (CRT) challenges the treatment of locally advanced rectal cancer (LARC). Our purpose was to timely stratify responders by investigating the predictive potential of sequential, in-treatment diffusion-weighted (DW) magnetic resonance (MR) images. Materials and methods: DW images were acquired with a 1.5 T MR-Linac at baseline and during MR-guided CRT in 15 patients with LARC prescribed long-course CRT, prospectively enrolled between 2018 and 2021. Tumour response was classified as good or poor based on pathologic tumour regression. Changes in mean tumour apparent diffusion coefficient (ADC) were analysed by linear mixed-effects models and compared to modelling using volume. Model coefficient analysis and receiver operating characteristics (ROCs) with area under the curve (AUC) were applied for timepoint investigation of response. Results: Six patients had good response, with significant difference in median (range) baseline ADC; ADC Conclusions: Early indication of treatment response in LARC was achieved by combining baseline and sequential ADC information. These encouraging results should be validated in a larger cohort.

Indexed as

ADCApparent diffusion coefficientBiomarkerDiffusion MRIMRI

Identifiers

PMID41141225
PMCPMC12550183

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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.