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SYSTEMATIC REVIEW article

Front. Oncol.

Sec. Thoracic Oncology

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1424647

This article is part of the Research Topic Bridging Surgical Oncology and Personalized Medicine: The Role of Artificial Intelligence and Machine Learning in Thoracic Surgery View all articles

AI models for the identification of prognostic and predictive biomarkers in lung cancer: A systematic review and meta-analysis

Provisionally accepted
  • 1 King Fahd Medical City, Riyadh, Saudi Arabia
  • 2 School of Medicine, Faculty of Medicine & Health Sciences, Royal College of Surgeons in Ireland, Dublin, County Dublin, Ireland
  • 3 Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia, AlMajmaah, Saudi Arabia
  • 4 College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
  • 5 Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
  • 6 King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
  • 7 College of Medicine, Taibah University, Madina, Saudi Arabia
  • 8 King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
  • 9 Unaizah College of Pharmacy, Qassim University, Unaizah, Saudi Arabia
  • 10 College of Medicine, Taif University, Ta'if, Saudi Arabia
  • 11 ALRayan college, Al Madinah, Saudi Arabia

The final, formatted version of the article will be published soon.

    1) Introduction: This systematic review and meta-analysis aim to evaluate the efficacy of artificial intelligence (AI) models in identifying prognostic and predictive biomarkers in lung cancer. With the increasing complexity of lung cancer subtypes and the need for personalized treatment strategies, AI-driven approaches offer a promising avenue for biomarker discovery and clinical decision-making. (2) Methods: A comprehensive literature search was conducted in multiple electronic databases to identify relevant studies published up to date. Studies investigating AI models for the identification of prognostic and predictive biomarkers in lung cancer were included. Data extraction, quality assessment, and meta-analysis were performed according to PRISMA guidelines. (3) Results: A total of 34 studies met the inclusion criteria, encompassing diverse AI methodologies and biomarker targets. AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status. Most of the studies developed models for the prediction of EGFR, followed by PD-L1 and ALK biomarkers in lung cancer. Internal and external validation techniques confirmed the robustness and generalizability of AI-driven predictions across heterogeneous patient cohorts. According to our results, the pooled sensitivity and pooled specificity of AI models for the prediction of biomarkers of lung cancer were 0.77 (95% CI: 0.72 -0.82) and 0.79 (95% CI: 0.78 -0.84). ( 4) Conclusion: The findings of this systematic review and meta-analysis highlight the significant potential of AI models in facilitating non-invasive assessment of prognostic and predictive biomarkers in lung cancer. By enhancing diagnostic accuracy and guiding treatment selection, AI-driven approaches have the potential to revolutionize personalized oncology and improve patient outcomes in lung cancer management. Further research is warranted to validate and optimize the clinical utility of AI-driven biomarkers in large-scale prospective studies.

    Keywords: AI models, IDENTIFICATION, prognostic and predictive biomarkers, lung cancer, Systematic review, Meta-analysis

    Received: 28 Apr 2024; Accepted: 28 Jan 2025.

    Copyright: © 2025 AlOsaimi, Alshilash, Alsaif, Bosbait, albeladi, Almutairi, Alhazzaa, Alluqmani, Al Qahtani, Almohammadi, Alamri, Alkurdi, Aljohani, Alraddadi and Kanan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mohammed Kanan, King Fahd Medical City, Riyadh, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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