A computational radiomics model reported by Tianjin Chest Hospital aims to distinguish whether pure ground-glass lung adenocarcinoma lesions are pre-cancerous or early invasive based on high-resolution CT features. The study uses imaging-derived quantitative markers to estimate invasiveness without surgical intervention. If validated externally, such models could reduce unnecessary procedures or help prioritize patients for more intensive workups, depending on how performance compares to current clinical decision thresholds. For radiology workflows, the most actionable value is translating high-dimensional imaging signals into reliable risk stratification. The work reflects the growing shift from subjective imaging interpretation toward standardized, model-based readouts designed for consistent triage.