Abstract 1472: AI-derived nuclear morphometrics as a novel prognostic indicator in lung adenocarcinoma.

Abstract Background: In lung adenocarcinoma (LUAD), prognostication has largely relied on architectural features, including predominant invasive pattern and tumor invasive size. However, the prognostic relevance of nuclear morphology relatively remains underexplored in LUAD. Using AI-based cell morphology analyzer, this study aimed to systematically quantify various nuclear morphometrics of LUAD and evaluate its prognostic significance. Method: Whole slide images of surgically resected LUAD cases (n = 160) were retrieved, and CellViT was applied to extract nuclear-level morphometric features. From the generated contours, 114 features—including area, bounding-box area, convex-hull area, Feret diameter, maximum major/minor axis length, aspect ratio, circularity, form factor, eccentricity, compactness, solidity, orientation angle, and fractal dimension—were computed. Case-level features were generated for orientation-related features using orientation variance and entropy of orientation variance, and for all other variables using the median, IQR (interquartile range), IQR-based coefficient of variation, histogram entropy and quantiles (Q10, Q20, Q80, and Q90). Univariate Cox models for disease-specific survival (DSS) and recurrence-free survival (RFS) were fitted using these case-level features. Results: Multiple nuclear contour features, including maximum Feret diameter, perimeter, major axis length, bounding-box area, and convex-hull area, were significant prognostic factors for both DSS (107/114, 93.9%) and RFS (28/114, 24.6%). Discussion: These findings demonstrate that nuclear morphometrics can have prognostic implication in LUAD. Incorporating AI-based nuclear features into current grading or risk-stratification frameworks may improve prognostic precision and reduce dependence on subjective visual assessment. AI-driven nuclear shape profiling holds promise as a complementary biomarker in lung cancer pathology. Citation Format: Bokyung Ahn, Hee Sang Hwang, Hyun-Jung Sung, Se Jin Jang, Pil-Jong Kim, Heounjeong Go. AI-derived nuclear morphometrics as a novel prognostic indicator in lung adenocarcinoma abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1472.