AI-derived nuclear morphometrics as a novel prognostic indicator in lung adenocarcinoma

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. Univariable analysis of various nuclear morphometrics of LUAD Disease specific survival feature Adjusted P-value Recurrence free survival feature Adjusted P-value Maximum Feret diameter_q90 <0.001 Major axis length_q90 <0.001 Perimeter_q90 <0.001 Maximum Feret diameter_q90 <0.001 Major axis length_q90 <0.001 Perimeter_q90 <0.001 Area_bbox_q90 <0.001 Area_bbox_q90 0.001 Area_convex_q90 <0.001 Area_convex_q90 0.003 Area_q90 <0.001 Major axis length_q80 0.004 Maximum Feret diameter_q80 <0.001 Maximum Feret diameter_q80 0.004 Major axis length_q80 <0.001 Area_q90 0.004 Fractal dimension_q20 <0.001 FD_median 0.010 Fractal dimension_q10 <0.001 Perimeter_q80 0.011