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 Howard¹, M Hasanova², A Tiwari³, A Ghose⁴, R Winayak⁵, T Nahar⁶, V Chauhan⁷, S Arun⁸, K Palmer⁹, A Houston¹⁰, N S Kumar¹¹, S Lalwani¹², A Januszewski¹², J Conibear¹², A Jain¹³, A Cortellini¹⁴, A Passaro¹⁵, A Addeo¹⁶, V Noronha¹⁷, G L Banna¹⁸, S Boussios¹⁹

Affiliations

¹ Cancer Institute, University College London, London, UK.
² Division of Biosciences, University College London, London, UK; OncoFlow™, London, UK.
³ Cancer Services, Princess Alexandra Hospital NHS Trust, Harlow, UK; Barts Cancer Institute, Queen Mary University of London, London, UK.
⁴ OncoFlow™, 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.
⁵ Kent Oncology Centre, Maidstone and Tunbridge Wells NHS Trust, Maidstone, UK.
⁶ School of Medicine Dentistry and Biomedical Sciences, Queen's University, Belfast, UK.
⁷ Barts Cancer Institute, Queen Mary University of London, London, UK; Department of General Internal Medicine, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
⁸ Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
⁹ Department of Clinical Oncology, University College London Hospitals NHS Foundation Trust, London, UK.
¹⁰ Barts Life Sciences, Barts Health NHS Trust, London, UK; Digital Environmental Research Institute, Queen Mary University of London, London, UK.
¹¹ College of Life Sciences, University of Leicester, Leicester, UK; National Medical Research Association, London, UK.
¹² Barts Cancer Institute, Queen Mary University of London, London, UK; Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
¹³ Apollo Cancer Centre, Indraprastha Apollo Hospital, New Delhi, India; Deanesly Centre Cancer Services, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, UK.
¹⁴ Operative 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.
¹⁵ Division of Thoracic Oncology, European Institute of Oncology IRCCS, Milan, Italy.
¹⁶ Oncology Service, Geneva University Hospital, Geneva, Switzerland.
¹⁷ Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India.
¹⁸ Department 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.
¹⁹ Department 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.

PMID: 40840236
PMCID: PMC12529306
DOI: 10.1016/j.esmoop.2025.105557

Review

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 et al. ESMO Open. 2025 Sep.

. 2025 Sep;10(9):105557.

doi: 10.1016/j.esmoop.2025.105557. Epub 2025 Aug 20.

Authors

Affiliations

¹ Cancer Institute, University College London, London, UK.
² Division of Biosciences, University College London, London, UK; OncoFlow™, London, UK.
³ Cancer Services, Princess Alexandra Hospital NHS Trust, Harlow, UK; Barts Cancer Institute, Queen Mary University of London, London, UK.
⁴ OncoFlow™, 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.
⁵ Kent Oncology Centre, Maidstone and Tunbridge Wells NHS Trust, Maidstone, UK.
⁶ School of Medicine Dentistry and Biomedical Sciences, Queen's University, Belfast, UK.
⁷ Barts Cancer Institute, Queen Mary University of London, London, UK; Department of General Internal Medicine, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
⁸ Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
⁹ Department of Clinical Oncology, University College London Hospitals NHS Foundation Trust, London, UK.
¹⁰ Barts Life Sciences, Barts Health NHS Trust, London, UK; Digital Environmental Research Institute, Queen Mary University of London, London, UK.
¹¹ College of Life Sciences, University of Leicester, Leicester, UK; National Medical Research Association, London, UK.
¹² Barts Cancer Institute, Queen Mary University of London, London, UK; Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
¹³ Apollo Cancer Centre, Indraprastha Apollo Hospital, New Delhi, India; Deanesly Centre Cancer Services, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, UK.
¹⁴ Operative 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.
¹⁵ Division of Thoracic Oncology, European Institute of Oncology IRCCS, Milan, Italy.
¹⁶ Oncology Service, Geneva University Hospital, Geneva, Switzerland.
¹⁷ Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India.
¹⁸ Department 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.
¹⁹ Department 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.

PMID: 40840236
PMCID: PMC12529306
DOI: 10.1016/j.esmoop.2025.105557

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.

Keywords: NSCLC; artificial intelligence; cancer prognosis; clinical prediction; lung cancer; predictive model.

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Figures

**Figure 1**
**Summary of the development of conventional and AI-based clinical prediction models in the NSCLC landscape and their integration into real-world clinical practice.** AI, artificial intelligence; NSCLC, non-small-cell lung cancer.

See this image and copyright information in PMC

References

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

Affiliations

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

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical