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Current and future applications of artificial intelligence in lung cancer and mesothelioma

Roche, Joshua J, Seyedshahi, Farzaneh, Rakovic, Kai, Thu, Akari Win, Le Quesne, John, Blyth, Kevin G·2025·Thorax
DOI 10.1136/thorax-2024-222054
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Abstract

Background: Considerable challenges exist in managing lung cancer and mesothelioma, including diagnostic complexity, treatment stratification, early detection and imaging quantification. Variable incidence in mesothelioma also makes equitable provision of high-quality care difficult. In this context, artificial intelligence (AI) offers a range of assistive/automated functions that can potentially enhance clinical decision-making, while reducing inequality and pathway delay. Aims: In this state-of-the-art narrative review, we synthesise evidence on this topic, focusing particularly on tools that ingest routine pathology and radiology images. We summarise the strengths and weaknesses of AI applied to common multidisciplinary team (MDT) functions, including histological diagnosis, therapeutic response prediction, radiological detection and quantification, and survival estimation. We also review emerging methods capable of generating novel biological insights and current barriers to implementation, including access to high-quality training data and suitable regulatory and technical infrastructure. Narrative: Neural networks trained on pathology images have proven utility in histological classification, prognostication, response prediction and survival. Self-supervised models can also generate new insights into biological features responsible for adverse outcomes. Radiology applications include lung nodule tools, which offer critical pathway support for imminent lung cancer screening and urgent referrals. Tumour segmentation AI offers particular advantages in mesothelioma, where response assessment and volumetric staging are difficult using human readers due to tumour size and morphological complexity. AI is also critical for radiogenomics, permitting effective integration of molecular and radiomic features for discovery of non-invasive markers for molecular subtyping and enhanced stratification. Conclusions: AI solutions offer considerable potential benefits across the MDT, particularly in repetitive or time-consuming tasks based on pathology and radiology images. Effective leveraging of this technology is critical for lung cancer screening and efficient delivery of increasingly complex diagnostic and predictive MDT functions. Future AI research should involve transparent and interpretable outputs that assist in explaining the basis of AI-supported decision making.

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  • Intraoperative AI applications such as augmented reality, real-time image-guided navigation, and robotic-assisted thoracic surgery have demonstrated increased surgical precision, reduced operative times, and enhanced safety.

    “Intraoperatively, AI applications such as augmented reality (AR), real-time image-guided navigation, and robotic-assisted thoracic surgery (RATS) have demonstrated increased surgical precision, reduced operative times, and enhanced safety [7,8,9].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [7]

    The LUNG CANCER section (page unavailable) mentions the development of robot-assisted surgical resection. It does not describe augmented reality or real-time image-guided navigation as intraoperative AI applications, or report increased surgical precision, shorter operative times, or enhanced safety from these approaches. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Roche, J. J., Seyedshahi, F., Rakovic, K., Thu, A. W., Le Quesne, J., & Blyth, K. G.
Affiliations
School of Cancer Sciences, University of Glasgow, Glasgow, UK
ORCID
Blyth, Kevin G
Venue
Thorax
Publisher
BMJ
Published
2025-06-20
Volume
80
Issue
12
Pages
957-965
ISSN
  • 0040-6376
  • 1468-3296
DOI
10.1136/thorax-2024-222054
Cited by
9
References
6
Language
en

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