Evidence map›Paper›PMID 40483288›Full record

ArticleNPJ precision oncology2025

Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.

Soumya Arun, Mariia Grosheva, Mark Kosenko, Jan Lukas Robertus, Oleg Blyuss, Rhian Gabe, Daniel Munblit, Judith Offman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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.

Soumya ArunCentre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK. s.arun@qmul.ac.uk.ORCID http://orcid.org/0000-0001-8850-725X
Mariia GroshevaDepartment of Paediatrics and Paediatric Infectious Diseases, Institute of Child's Health, I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, Russia.
Mark KosenkoDepartment of Paediatrics and Paediatric Infectious Diseases, Institute of Child's Health, I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, Russia.
Jan Lukas RobertusDepartment of Histopathology, Royal Brompton and Harefield, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Oleg BlyussCentre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
Rhian GabeCentre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
Daniel MunblitDepartment of Paediatrics and Paediatric Infectious Diseases, Institute of Child's Health, I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, Russia.
Judith OffmanCentre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.

Funding

Barts Charity G-001522Barts Charity G-001522, MGU0461
6 · The paper itself

Abstract

Clinical adoption of digital pathology-based artificial intelligence models for diagnosing lung cancer has been limited, partly due to lack of robust external validation. This review provides an overview of such tools, their performance and external validation. We systematically searched for external validation studies in medical, engineering and grey literature databases from 1st January 2010 to 31st October 2024. 22 studies were included. Models performed various tasks, including classification of malignant versus non-malignant tissue, tumour growth pattern classification and subtyping of adeno- versus squamous cell carcinomas. Subtyping models were most common and performed highly, with average AUC values ranging from 0.746 to 0.999. Although most studies used restricted datasets, methodological issues relevant to the applicability of models in real-world settings included small and/or non-representative datasets, retrospective studies and case-control studies without further real-world validation. Ultimately, more rigorous external validation of models is warranted for increased clinical adoption.

Identifiers

PMID40483288
PMCPMC12145442

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Registered trials

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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.