Lobectomy or Sublobar Resection? Evidence-Based and AI-Enhanced Strategies for Early-Stage Non-Small Cell Lung Cancer

Abstract

Background. Lobectomy has historically been the gold standard surgical treatment for early-stage non-small cell lung cancer (NSCLC). However, the role of sublobar resections, particularly segmentectomy, has been redefined over the past decade, with recent randomized trials challenging traditional paradigms. In parallel, artificial intelligence (AI) is emerging as a transformative tool in thoracic surgery, offering new opportunities for preoperative risk stratification, surgical planning, and individualized treatment selection. This review synthesizes current evidence comparing sublobar resections and lobectomy in early-stage NSCLC, examining both oncologic and functional outcomes, and explores the potential of AI and real-world data to refine surgical decision-making. Methods. A narrative review of key randomized controlled trials, meta-analyses, and recent literature on AI in thoracic oncology was performed, emphasizing patient selection, risk assessment, and integration of AI-derived tools. Results. Trials such as JCOG0802/WJOG4607L and CALGB 140503 demonstrate non-inferior survival with anatomical segmentectomy for selected tumors ≤2 cm, though higher locoregional recurrence rates in specific subgroups underscore the importance of patient selection. Functional preservation after segmentectomy is modest and not universally superior to lobectomy. AI applications"ranging from radiomics-based tumor characterization to machine learning"driven risk prediction"offer promising avenues to support nuanced surgical decisions, but prospective validation and clinical integration remain limited. Conclusions. Surgical decision-making for early-stage NSCLC is evolving from a one-size-fits-all model toward precision strategies integrating oncologic, functional, and patient-specific factors. AI has the potential to enhance this process by providing individualized prognostic and functional predictions. Future research should focus on prospective evaluation of AI-assisted decision pathways, addressing barriers to adoption, and aligning surgical strategies with personalized cancer care.

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