Evidence map›Paper›PMID 40840236›Full record

ReviewESMO open2025

The landscape of conventional and artificial intelligence-based clinical prediction models in non-small-cell lung cancer: from development to real-world validation.

H R Howard, M Hasanova, A Tiwari, A Ghose, R Winayak, T Nahar, V Chauhan, S Arun, K Palmer, A Houston and 11 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in ESMO open, 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. Article
  2. Review
  3. Article
  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

21 authors.

H R HowardCancer Institute, University College London, London, UK.
M HasanovaDivision of Biosciences, University College London, London, UK; OncoFlow™, London, UK.
A TiwariCancer Services, Princess Alexandra Hospital NHS Trust, Harlow, UK; Barts Cancer Institute, Queen Mary University of London, London, UK.
A GhoseOncoFlow™, London, UK; Barts Cancer Institute, Queen Mary University of London, London, UK; Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham, UK; Health Systems and Treatment Optimisation Network, European Cancer Organisation, Brussels, Belgium. Electronic address: aruni.ghose1@gmail.com.
R WinayakKent Oncology Centre, Maidstone and Tunbridge Wells NHS Trust, Maidstone, UK.
T NaharSchool of Medicine Dentistry and Biomedical Sciences, Queen's University, Belfast, UK.
V ChauhanBarts Cancer Institute, Queen Mary University of London, London, UK; Department of General Internal Medicine, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
S ArunCentre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
K PalmerDepartment of Clinical Oncology, University College London Hospitals NHS Foundation Trust, London, UK.
A HoustonBarts Life Sciences, Barts Health NHS Trust, London, UK; Digital Environmental Research Institute, Queen Mary University of London, London, UK.
N S KumarCollege of Life Sciences, University of Leicester, Leicester, UK; National Medical Research Association, London, UK.
S LalwaniBarts Cancer Institute, Queen Mary University of London, London, UK; Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
A JanuszewskiBarts Cancer Institute, Queen Mary University of London, London, UK; Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
J ConibearBarts Cancer Institute, Queen Mary University of London, London, UK; Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
A JainApollo Cancer Centre, Indraprastha Apollo Hospital, New Delhi, India; Deanesly Centre Cancer Services, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, UK.
A CortelliniOperative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy; Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy; Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, UK.
A PassaroDivision of Thoracic Oncology, European Institute of Oncology IRCCS, Milan, Italy.
A AddeoOncology Service, Geneva University Hospital, Geneva, Switzerland.
V NoronhaDepartment of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India.
G L BannaDepartment of Medical Oncology, Portsmouth Hospitals University NHS Trust, Portsmouth, UK; Faculty of Science and Health, School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, UK.
S BoussiosDepartment of Research and Innovation, Medway NHS Foundation Trust, Gillingham, UK; Department of Medical Oncology, Ioannina University Hospital, Ioannina, UK; Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, UK; AELIA Organization, Thessaloniki, Greece; Kent and Medway Medical School, University of Kent, Canterbury, UK; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, UK; Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globally, lung cancer remains the most common cause of cancer mortality, with non-small-cell lung cancer (NSCLC) being the most common subtype of lung cancer diagnosed. This review paper provides a comprehensive landscape of clinical prediction models (CPMs) in NSCLC, including in early-stage and metastatic disease, and the recent acceleration of artificial intelligence integration. Prediction models are developed using multimodal patient data to allow oncologists to make evidence-based decisions regarding patient treatment options. Despite these models in early-stage and metastatic NSCLC showing promise, their clinical application provides challenges, involving an unmet need for external validation, alongside a lack of prospective modelling. However, the continued advancements in this field, comprising production and accessibility of large-scale pathology databases and external validation of developed models, allow for continued research and progress. These models have potential to assist in personalised treatment selection, supporting oncologists in perceiving future risk factors or issues associated with a specific targeted therapy for an individual patient, ultimately optimising treatment to precise, personalised options for individuals diagnosed with NSCLC.

Indexed as

Artificial IntelligenceCarcinoma, Non-Small-Cell LungLung NeoplasmsHumansartificial intelligencecancer prognosisclinical predictionlung cancerNSCLCpredictive model

Identifiers

PMID40840236
PMCPMC12529306

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.