Texture Analysis of Subsolid Nodules Detected on Computed Tomography for Differentiating Minimally Invasive From Invasive Adenocarcinoma
Shih-Min Lin, Yi-Ching Chen, Jao Perng Lin, Wan-Ling ChangObjective:
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,
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.