Artificial Intelligence for Accurate Tumor Size Assessment and Non-Invasive Adenocarcinoma Prediction in Small-sized Lung Cancer
Menée au Japon à partir de données portant sur 324 patients atteints d'un adénocarcinome pulmonaire de petite taille, cette étude évalue la performance de l'intelligence artificielle pour mesurer avec précision la taille tumorale et identifier les tumeurs non invasives
Introduction : Accurate preoperative imaging is essential for improving the treatment of small lung cancers. Precise identification of non-invasive adenocarcinomas is critical for determining the suitability of sublobar resection. Conventional methodologies frequently demonstrate variability, particularly for small tumors or ground-glass nodules (GGNs). Artificial intelligence (AI) offers a consistent and objective alternative, enhancing non-invasive cancer diagnosis and facilitating more effective treatment decisions.
Materials and methods : A retrospective analysis was conducted on 324 patients who underwent surgical resection for small-sized lung adenocarcinomas at Tokyo Medical University. The Synapse Vincent system (Fujifilm Corporation, Japan) was employed to measure tumor size and classify the nodules as GGN (AI-GGN) or non-GGN (non-AI-GGN) based on confidence scores. The ability of AI to predict pathological non-invasive adenocarcinomas was evaluated.
Results : AI-measured tumor sizes were significantly more accurate than those measured by thoracic surgeons (p < 0.001) AI-GGN demonstrated a high specificity of 98.3% for predicting pathological non-invasive adenocarcinoma, closely aligned with the 98.3% specificity of the traditional consolidation tumor ratio (CTR) method. The positive predictive values of AI-GGN and CTR were similarly high, (98.5% and 98.2%, respectively), confirming the effectiveness of both methods in identifying non-invasive adenocarcinomas.
Conclusion : AI technology significantly enhances the precision of tumor size measurement and identification of non-invasive adenocarcinomas in small-sized lung tumors. By providing objective and automated evaluations, AI can refine surgical planning and decision-making. Further prospective multicenter studies are warranted to validate these findings and to fully integrate AI into clinical practice, ultimately improving patient outcomes.
European Journal of Surgical Oncology , résumé, 2026