Preoperative Planning and Assessment for VATS Lung Cancer Resection

The future of thoracic oncology is increasingly defined by the integration of precision oncology with minimally invasive surgical platforms such as video-assisted thoracoscopic surgery (VATS). Comprehensive molecular testing, including next-generation sequencing for actionable driver mutations, programmed death ligand-1 (PD-L1) expression, tumor mutational burden, and emerging transcriptomic and immune signatures has transformed lung cancer from a stage-based disease model into a biologically stratified entity (1,2). These data now influence not only systemic therapy selection but also surgical timing, candidacy, and extent of resection. Neoadjuvant targeted therapy and immunotherapy, guided by tumor genomics, are reshaping perioperative strategy, while molecular risk profiling is refining decisions between lobectomy and parenchymal-sparing resections (3,4). In this framework, VATS remains central, offering reduced morbidity and faster recovery while being deployed more selectively based on tumor biology rather than anatomy alone.

The incorporation of circulating tumor DNA (ctDNA) and minimal residual disease (MRD) assessment further extends precision oncology into the postoperative period. Molecular surveillance may soon complement, or in some cases supersede, radiographic follow-up, enabling earlier detection of recurrence and more individualized adjuvant therapy decisions (5). Concurrently, artificial intelligence (AI) is emerging as a powerful integrative tool, combining radiographic features, genomic alterations, pathologic data, and outcomes to generate predictive models for recurrence risk and therapeutic response. Radiogenomic platforms may allow preoperative estimation of tumor biology, enhancing surgical planning and multidisciplinary coordination (3,5).

Together, these advances signal a shift in the thoracic surgeon’s role, from technical resectionist to biologically informed oncologic strategist. VATS will not diminish in importance; rather, it will function as a precise, minimally invasive instrument within a data-driven, molecularly guided treatment paradigm (6). The future of thoracic cancer care lies in seamless integration: genomic insight guiding surgical intervention, molecular monitoring informing postoperative management, and AI synthesizing complex datasets to personalize care at every stage. This series, with contributions of leading experts in the field of Thoracic Surgical Oncology and VATS, emphasizes the current management strategies, with focus on a multidisciplinary care coordination.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Video-Assisted Thoracic Surgery for the series “Preoperative Planning and Assessment for VATS Lung Cancer Resection”. The article did not undergo external peer review.

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://vats.amegroups.com/article/view/10.21037/vats-2026-0013/coif). The series “Preoperative Planning and Assessment for VATS Lung Cancer Resection” was commissioned by the editorial office without any funding or sponsorship. G.D.T. served as the unpaid Guest Editor of the series and serves as an unpaid editorial board member of Video-Assisted Thoracic Surgery from February 2025 to December 2026. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Gregory D. Trachiotis