Texture Analysis of Subsolid Nodules Detected on Computed Tomography for Differentiating Minimally Invasive From Invasive Adenocarcinoma.

Abstract

Objective: Lung cancer is the leading cause of cancer-related deaths worldwide, and lung nodules serve as early indicators. This study aimed to evaluate computed tomography (CT) texture features to differentiate minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in subsolid nodules (SSNs).

Methods: This retrospective study included patients with lung adenocarcinoma presenting as subsolid nodules. Based on preoperative pathologic findings, patients were categorized into MIA and IAC groups. Morphologic characteristics-including location, size, density, shape, and lobulation-were extracted from preoperative thin-section CT scans. CT texture features were quantified using MaZda software through histogram parameters (mean, standard deviation, skewness, and kurtosis) and gray-level co-occurrence matrix metrics (contrast, entropy, inverse difference moment, and autocorrelation).

Results: Among 298 patients, 134 had MIA and 164 had IAC. Significant differences were found in CT morphologic features: IAC demonstrated larger size (11.65±3.17 mm vs. 9.32±2.41 mm, P<0.001), a higher prevalence of mixed ground-glass nodules (58.56% vs. 21.35%, P<0.001), more irregular shapes (64.86% vs. 32.58%, P<0.001), and more frequent lobulation (54.95% vs. 20.22%, P<0.001). Key texture features distinguishing IAC from MIA included higher mean attenuation (-448.69±46.52 HU vs. -562.47±145.28 HU, P<0.001) and entropy (6.95±0.22 vs. 6.66±0.38, P<0.001). The predictive nomogram integrating these features achieved an area under the curve of 0.831 in the training set and 0.893 in the validation set.

Conclusion: Quantitative CT texture analysis-particularly entropy and mean attenuation-serves as a valuable tool for differentiating MIA from IAC in SSNs. This noninvasive approach enhances preoperative risk stratification and supports personalized surgical planning.