Evidence map›Paper›PMID 41253645›Full record

ArticleEBioMedicine2025

AI-powered immune profiling from histopathology slides for chemo-radiotherapy outcome prediction in rectal cancer: a study using clinical trial and real-world cohorts.

Zhuoyan Shen, Douglas Brand, Mikaël Simard, Adam P Levine, Sumeet Hindocha, Talisa Mistry, Dahmane Oukrif, Andre Lopes, Rubina Begum, Nicholas P West and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
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

16 authors.

Zhuoyan ShenDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK. Electronic address: zhuoyan.shen.18@ucl.ac.uk.
Douglas BrandDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK; Department of Radiotherapy, University College London Hospitals NHS Foundation Trust, London, UK.
Mikaël SimardDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK.
Adam P LevineResearch Department of Pathology, University College London, London, UK; Department of Cellular Pathology, University College London Hospitals NHS Foundation Trust, London, UK.
Sumeet HindochaDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK; Department of Radiotherapy, University College London Hospitals NHS Foundation Trust, London, UK.
Talisa MistryResearch Department of Pathology, University College London, London, UK.
Dahmane OukrifResearch Department of Pathology, University College London, London, UK.
Andre LopesCancer Institute, University College London, London, UK.
Rubina BegumCancer Institute, University College London, London, UK.
Nicholas P WestDivision of Pathology and Data Analytics, Leeds Institute of Medical Research, School of Medicine, University of Leeds, Leeds, UK; Division of Oncology, Leeds Institute of Medical Research, School of Medicine, University of Leeds, Leeds, UK.
Ying ZhangDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK.
Gary RoyleDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK.
Tim S MaughanDepartment of Oncology, University of Oxford, Oxford, UK.
David Sebag-MontefioreDivision of Oncology, Leeds Institute of Medical Research, School of Medicine, University of Leeds, Leeds, UK.
Maria A HawkinsDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK; Department of Radiotherapy, University College London Hospitals NHS Foundation Trust, London, UK.
Charles-Antoine Collins-FeketeDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe impact of the tumour-immune microenvironment on locally advanced rectal cancer (LARC) outcomes remains unclear. This study quantitatively assesses the synergistic influence of tumour-infiltrating lymphocytes (TILs), tumour-associated macrophages (TAMs), mitotic activity, and DNA mutations in predicting outcomes for LARC patients undergoing neoadjuvant chemo-radiotherapy (nCRT).

methodsThree cohorts (ARISTOTLE-RC, UCLH-RC, TCGA-CRC) were stratified by densities of AI-quantified TILs, TAMs, and mitotic figures with cut-offs identified on a hold-out subset and integrated with DNA mutations to assess correlations with disease-free survival (DFS) and overall survival (OS). Immune cell dynamics pre- and post-CRT were also evaluated.

findingsIn ARISTOTLE-RC, TIL

interpretationThese findings suggest the potential utility of AI-driven immune profiling for clinical decision-making in LARC patients undergoing nCRT.

fundingCancer Research UK (RRNPSF-Jan21/100001, A18745, C7893/A2899), UK Research and Innovation (MR/T040785/1).

Indexed as

Rectal NeoplasmsAdultAgedBiomarkers, TumorChemoradiotherapyFemaleHumansLymphocytes, Tumor-InfiltratingMacrophagesMaleMiddle AgedMutationNeoadjuvant TherapyPrognosisTreatment OutcomeTumor MicroenvironmentBiomarkers, TumorArtificial intelligenceChemo-radiotherapyColorectal cancerDigital pathologyTumour immune microenvironment

Identifiers

PMID41253645
PMCPMC12790592

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.