Utility of Dynamic Volume Perfusion CT in Noninvasive Differentiation of Lung Cancer Histological Subtypes

Abstract Neoangiogenesis, a key feature of tumor progression, results in increased blood flow (BF), blood volume (BV), and vascular permeability. Dynamic volume perfusion computed tomography (dVPCT) complements conventional morphological assessment by providing valuable insights into tumor vascularity, treatment response, and prognosis. Emerging evidence shows strong correlations between dVPCT parameters, microvessel density, and angiogenic markers, underscoring its potential utility in the comprehensive management of lung cancer. The aim of this study was to assess the role of dVPCT in the noninvasive evaluation of lung cancer by correlating perfusion parameters with histopathology. A prospective study was conducted on 40 patients with clinically suspected lung cancer who met the inclusion criteria. All participants underwent dVPCT using a standardized protocol, Adaptive 4D Spiral acquisition followed by postprocessing based on the deconvolution method. Perfusion parameters, including BV, BF, mean transit time (MTT), and flow extraction product (FEP), were analyzed and compared across different histological subtypes. A p-value of less than 0.05 was considered statistically significant. Among 40 lung cancer patients, dVPCT revealed significant differences in perfusion parameters across histological subtypes. Adenocarcinoma (AC) demonstrated the highest BV (40.62 ± 21.42 mL/100 mL) and FEP (21.85 ± 4.91 mL/100 g/min), both significantly higher than in nonAC groups (p = 0.013 and p = 0.002, respectively). FEP showed the best diagnostic performance with 90% sensitivity and 80% specificity in differentiating AC. BF and MTT did not show statistically significant variation among subtypes. dVPCT shows potential as a noninvasive tool for differentiating lung cancer subtypes, with BV and FEP offering significant diagnostic value.