WCLC 2025 - Posters & ePosters-P2.06.05 Diagnosis of the Novel IASLC Grading System Using Intraoperative Frozen Section: A Prospective Multi-Center Trial

P2.06.05 Diagnosis of the Novel IASLC Grading System Using Intraoperative Frozen Section: A Prospective Multi-Center Trial

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This prospective, multi-center clinical trial evaluated the accuracy of intraoperative frozen section (FS) diagnosis using the novel IASLC grading system for invasive non-mucinous lung adenocarcinoma. Conducted from July 2022 to August 2023 at Fudan University Shanghai Cancer Center, the study aimed to assess FS concordance with final pathology (FP) grades and identify factors contributing to misdiagnosis.<br /><br />Among 827 early-stage lung adenocarcinoma cases, FS showed an overall concordance rate of 79.9% with FP in determining IASLC grades, with a weighted kappa statistic of 0.627, indicating good diagnostic agreement. Sensitivity, specificity, and accuracy varied by grade: Grade 1 tumors had 61.9% sensitivity and 91.4% accuracy; Grade 2 showed 86.9% sensitivity and 80.0% accuracy; Grade 3 yielded 69.0% sensitivity and 88.4% accuracy. Incorporating FS results into predictive models improved the Area Under the Curve (AUC) to 0.907 for identifying Grade 3 tumors, outperforming baseline and radiologic feature models alone.<br /><br />Interobserver agreement among pathologists analyzing FS was very good (κ = 0.821), reinforcing the reliability of the method. However, histologic patterns dominated by lepidic and micropapillary components were independently associated with higher misdiagnosis rates (P < 0.001), suggesting challenges in accurately grading these variants intraoperatively.<br /><br />In conclusion, FS is a valuable tool for intraoperative IASLC grading of early lung adenocarcinoma, potentially informing surgical decision-making. Still, caution is warranted in cases with specific histopathological patterns prone to misclassification. Further research is needed to validate these results and determine how incorporating FS-based grading into clinical workflows impacts treatment outcomes and prognosis.

Asset Subtitle

Fangqiu Fu

Meta Tag

Speaker Fangqiu Fu

Topic Pathology and Biomarkers

Keywords

intraoperative frozen section

IASLC grading system

lung adenocarcinoma

early-stage lung cancer

diagnostic accuracy

interobserver agreement

histologic patterns

misdiagnosis factors

clinical trial

pathology concordance