Biopsies Can Predict Lung Adenocarcinoma IASLC Grade: A New Proposal and Grading Pitfalls.

Introduction: The International Association for the Study of Lung Cancer grading system for resected invasive lung adenocarcinoma provides robust prognostic stratification for recurrence risk and overall survival. However, most patients present at advanced stages, where only small biopsy samples are available, leaving no established prognostic tool for such cases. We propose a practical biopsy-based grading method for pulmonary adenocarcinoma. Methods: We analyzed 267 paired biopsies and resections. The following two biopsy grading (bxG) models were tested:•Model 1: Based on the predominant pattern-grade 1 (lepidic), grade 2 (acinar/papillary), grade 3 (solid/micropapillary/complex glandular).•Model 2: Any percentage of high-grade patterns classified as grade 3, regardless of predominance. BxG 1 and 2 are defined as predominant patterns as described.For analysis, grades 1 and 2 were combined (G1-2), and grade 3 was considered a separate category. Diagnostic performance was assessed using resections as the reference standard. Interobserver reliability was evaluated, and discrepant cases were reviewed. Results: Model 1 achieved 79.8% accuracy (sensitivity 57%, specificity 98.6%, positive predictive value [PPV] 97.2%, negative predictive value 73.5%, area under the curve 0.78). Model 2 performed better, with 88.4% accuracy (sensitivity 83.5%, specificity 92.5%, PPV 90.2%, negative predictive value 87.1%, area under the curve 0.89), and was used for further analysis. Discrepancies ( Conclusion: Biopsy grading using the presence of any high-grade pattern correlates well with International Association for the Study of Lung Cancer-based resection grading. Although bxG1 to 2 biopsies may underestimate tumor grade, bxG3 biopsies demonstrate more than 90% specificity and PPV for predicting G3 resections, supporting their potential utility as a clinical prognostic marker.