Evidence map›Paper›PMID 41719793›Full record

ArticleEBioMedicine2026

Fusing data from CT deep learning, CT radiomics and peripheral blood immune profiles to diagnose lung cancer in a cohort of patients experiencing symptoms.

Rami Mustapha, Balaji Ganeshan, Sam Ellis, Luigi Dolcetti, Mukunthan Tharmakulasingam, Karen DeSouza, Xiaolan Jiang, Courtney Savage, Sheena Lim, Emily Chan and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Observational
  2. 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

23 authors.

Rami MustaphaComprehensive Cancer Centre, King's College London, London, UK.
Balaji GaneshanUniversity College London, London, UK.
Sam EllisSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Luigi DolcettiComprehensive Cancer Centre, King's College London, London, UK.
Mukunthan TharmakulasingamComprehensive Cancer Centre, King's College London, London, UK.
Karen DeSouzaComprehensive Cancer Centre, King's College London, London, UK.
Xiaolan JiangComprehensive Cancer Centre, King's College London, London, UK.
Courtney SavageComprehensive Cancer Centre, King's College London, London, UK.
Sheena LimEast and North Hertfordshire NHS Trust, UK.
Emily ChanSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Andrew ThorntonUniversity College London, London, UK.
Luke HoyUniversity College London, London, UK.
Raymond EndozoUniversity College London, London, UK.
Rob ShortmanUniversity College London, London, UK.
Darren WallsUniversity College London, London, UK.
Shih-Hsin ChenUniversity College London, London, UK.
Mark RowleySaddle Point Science Ltd, York, UK.
Anthony C C CoolenSaddle Point Science Europe BV, 6525EC, Nijmegen, the Netherlands; Radboud University, 6525AJ, Nijmegen, the Netherlands.
Ashley M GrovesUniversity College London, London, UK.
Julia A SchnabelComprehensive Cancer Centre, King's College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Thida WinEast and North Hertfordshire NHS Trust, UK.
Paul R BarberComprehensive Cancer Centre, King's College London, London, UK. Electronic address: paul.barber@kcl.ac.uk.
Tony NgComprehensive Cancer Centre, King's College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting symptoms contributing to high mortality. There is a lack of specific, minimally invasive low-cost tests to screen patients ahead of the diagnostic biopsy.

methods344 patients experiencing symptoms from the lung clinic of Lister hospital suspected of lung cancer were recruited. Predictive covariates were successfully generated on 170 patients from Computed Tomography (CT) scans using CT Texture Analysis (CTTA) and Deep Learning Autoencoders (DLA) as well as from peripheral blood data for immunity using high depth flow-cytometry and for exosome protein components. Predictive signatures were formed by combining covariates using Bayesian regression on a randomly chosen 128-patient training set and validated on a 42-patient held-out set. Final signatures were generated by fusing the data sources at different levels.

findingsImmune, CTTA and DLA single modality signatures had overall AUCs of 0.69, 0.70 and 0.73 respectively. The final combined signature had a ROC AUC of 0.81. The overall sensitivity and specificity were 0.72 and 0.77 respectively.

interpretationCombining immune monitoring with CT scan data is an effective approach to improving sensitivity and specificity of Lung cancer screening even in patients experiencing symptoms.

fundingCRUK [C1519/A27375], Wellcome Trust/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z], NIHR Clinical Research Facility at Guy's and St Thomas' NHS Foundation Trust, NIHR Biomedical Research Centre.

Indexed as

Deep LearningLung NeoplasmsTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedRadiomicsROC CurveBlood testCT scanEarly diagnosisImmuneLung cancer

Identifiers

PMID41719793
PMCPMC12936772

What Socratic 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 Socratic graph.