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The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review

Abbaker, Namariq, Minervini, Fabrizio, Guttadauro, Angelo, Solli, Piergiorgio, Cioffi, Ugo, Scarci, Marco·2024·Frontiers in Oncology
DOI 10.3389/fonc.2024.1347464PMC PMC10897973
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Abstract

Objectives: To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. Methods: A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. Conclusion: While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI’s ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.

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  • AI-driven systems enhance diagnostic accuracy, optimize preoperative planning, and support intraoperative decision-making in complex thoracic procedures such as lung cancer resection and mediastinal interventions.

    “Recent advances in machine learning, deep learning, and computer vision have enabled AI-driven systems to enhance diagnostic accuracy, optimize preoperative planning, and support intraoperative decision-making, particularly in complex procedures such as lung cancer resection and mediastinal interventions [1,2,3].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [3]

    The review’s “Imaging and staging” section describes AI-assisted improvements in lung-cancer diagnosis, while “Surgical candidacy” addresses preoperative assessment and “Surgical guidance and precision” describes intraoperative guidance for lung tumor resection (page numbers unavailable). Our copy does not establish the claimed application to mediastinal interventions, and some benefits are framed as potential rather than demonstrated outcomes. (LLM, conf=0.86)

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Cited by

Sorted by citations

Authors
Abbaker, N., Minervini, F., Guttadauro, A., Solli, P., Cioffi, U., & Scarci, M.
Venue
Frontiers in Oncology
Publisher
Frontiers Media SA
Published
2024-02-13
Volume
14
ISSN
2234-943X
DOI
10.3389/fonc.2024.1347464
PMC
PMC10897973
Cited by
22
References
5
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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