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Background: The effect of lymph node dissection (LND) on the efficacy of immune checkpoint inhibitor (ICI) remains unclear. The purpose of this study was to examine the difference in the effect of ICI between patients with non-small cell lung cancer (NSCLC) according to the extent of LND performed in surgery prior to postoperative recurrence. Methods: A total of 134 patients with postoperative recurrence (surgery group, n=26) or unresectable advanced lung cancer (non-surgery group, n=108) who were treated with ICIs between January 2016 and December 2022 were included for analysis. In the surgery group, 16 patients underwent systematic LND, whereas the remaining 10 patients underwent selective LND. Progression-free survival with ICI treatment (ICI-PFS) and overall survival (OS) were compared between the surgery and non-surgery groups and between the systematic and selective LND groups using the inverse probability of treatment weighting (IPTW) method to adjust for patient background characteristics. Results: In the IPTW-adjusted analysis, the 2-year PFS rate with ICI treatment was 31.2% in the surgery group and 27.3% in the non-surgery group (P=0.19); the corresponding 2-year OS rates were 69.6% and 62.2%, respectively (P=0.10). In the surgery group, the 2-year PFS rates under ICI were 20.0% in the systematic LND group and 45.7% in the selective LND group (P=0.03). Conclusions: IPTW-adjusted analysis indicated no difference in prognosis between patients with postoperative recurrence and those with advanced unresectable lung cancer. However, in patients with postoperative recurrence, the extent of LND was a significant predictor of ICI-PFS. These findings suggest that systematic LND may reduce the efficacy of ICI, indicating that preoperative ICI administration may be warranted.
RESUMEN
OBJECTIVES: Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules. MATERIALS AND METHODS: Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments. RESULTS: Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies. CONCLUSION: In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.