Preliminary Study on the Evaluation Value of Extracellular Volume Fraction in the Pathological Grading of Lung Invasive Adenocarcinoma.

Abstract

Introduction: This study aims to evaluate the diagnostic value of extracellular volume fraction (ECV) and spectral CT parameters in assessing the pathological grading of lung invasive adenocarcinoma (IAC) presenting as solid or subsolid nodules.

Methods: A retrospective collection of patients who were pathologically confirmed as IAC with solid or subsolid pulmonary nodules at our hospital from March 2023 to November 2024 was conducted. Relevant data were recorded, and the patients were divided into two groups: intermediate/high differentiation and low differentiation. The parameters including arterial phase iodine concentration (IC^A), arterial phase normalized iodine concentration (NIC^A), arterial phase normalized effective atomic number (nZef^fA), arterial phase extracellular volume fraction (ECV^A), venous phase iodine concentration (IC^V), venous phase Normalized Iodine Concentration (NIC^V), venous phase normalized effective atomic number (nZeff^V), and venous phase extracellular volume fraction (ECV^V) were compared between the two groups. Parameters with statistical significance were evaluated for their diagnostic performance using Receiver Operating Characteristic (ROC) curves.

Results: A total of 61 patients were included, comprising 40 in the intermediate to high differentiation group and 21 in the low differentiation group. The intermediate/high differentiation group had higher values of ECV^A, NIC^A, ECV^V, IC^V, NIC^V, and nZeff^V than the low differentiation group (P < 0.05). The AUC values for these parameters were 0.679, 0.620, 0.757, 0.688, 0.724, and 0.693, respectively. Among these, ECV^V had the largest AUC, with a sensitivity and specificity of 72.5% and 71.4%, respectively. Through binary logistic regression analysis, five features were identified: the maximum diameter of the lesion, bronchus encapsulated air sign, lobulation sign, spiculation sign, and pleural traction sign. The integration of these imaging features with ECV^V resulted in a model with enhanced diagnostic performance, characterized by an AUC of 0.886, a sensitivity of 85.7%, and a specificity of 80.0%.

Discussion: ECVV outperforms other spectral parameters in differentiating IAC grades, reflecting changes in the tumor microenvironment. Combining ECV^V with imaging features enhances diagnostic accuracy, though the study's single-center design and small sample size limit generalizability.

Conclusion: Extracellular volume fraction can provide more information for the pathological grading assessment of invasive adenocarcinoma of the lung. Compared to other spectral parameters, ECV^V exhibits the highest diagnostic performance, and its combination with conventional imaging features can further enhance diagnostic accuracy.