Objective To explore the application value of dual-phase dual-energy CT (DECT) perfusion imaging in preoperative lung function assessment of lung cancer patients. Methods Data were collected from patients with stageⅠA non-small cell lung cancer who underwent surgical treatment in the Department of Thoracic Surgery, the First Affiliated Hospital of Nanjing Medical University, from November 2022 to June 2024. All patients underwent DECT perfusion imaging and pulmonary function testing (PFT) before surgery. PFT observation indicators included ventilation function indicators such as forced expiratory volume in one second (FEV1), forced vital capacity (FVC), 1-second rate (FEV1/FVC), maximal voluntary ventilation (MVV), and diffusion function indicators such as diffusing capacity for carbon monoxide (DLCO) and DLCO per liter of alveolar volume (DLCO/VA). The software eXamine was used to obtain quantitative parameters of DECT perfusion imaging, including volume parameters and perfusion parameters of both lungs and each lung lobe. The correlation between the volume parameters and perfusion parameters of both lungs and the ventilation and diffusion function indicators of the patients, as well as the differences in quantitative parameters of each lung lobe, was analyzed. Results The end-inspiration lung volume and biphasic volume difference were strongly positively correlated with FEV1 and FVC (r=0.636, r=0.682, r=0.614, r=0.624, P<0.001) and moderately positively correlated with MVV and DLCO (r=0.499, r=0.514, r=0.549, r=0.447, P<0.001); the end-expiration lung volume was weakly negatively correlated with DLCO/VA (r=−0.295, P=0.026); the volume ratio was positively correlated with FEV1, FVC, MVV, and MVV% (r=0.424, r=0.399, r=0.415, r=0.310, P<0.05); the end-inspiration iodine content was weakly positively correlated with DLCO/VA% (rs=0.292, P=0.030); the end-expiration iodine content was weakly positively correlated with FEV1, FVC, MVV, DLCO%, and DLCO/VA (r=0.307, r=0.299, r=0.295, r=0.366, r=0.320, P<0.05) and moderately positively correlated with DLCO (r=0.439, P<0.001); the end-inspiration iodine concentration was negatively correlated with FEV1, FVC, MVV, and MVV% (rs=−0.407, rs=−0.426, rs=−0.352, rs=−0.277, P＜0.05); the end-expiratory phase iodine concentration was moderately positively correlated with DLCO/VA (r=0.403, P=0.002); both the iodine concentration difference and the iodine concentration ratio were moderately positively correlated with FEV1, FEV1%, FVC, MVV, MVV% (P<0.05). The lung volume and iodine concentration ratio values were both highest in the left upper lung lobe and lowest in the right middle lung lobe; the differences in lung volume, lung volume ratio, intrapulmonary iodine content, and intrapulmonary iodine concentration were all highest in the lower lobes of both lungs and lowest in the middle lobe of the right lung. Conclusion Dual-phase DECT perfusion imaging can accurately assess overall lung function and quantify regional lung function.

Objective To investigate the risk factors for lymph node metastasis in resectable lung adenocarcinoma by combining spatial location, clinical, and imaging features, and to construct a lymph node metastasis prediction model. Methods A retrospective study on patients who underwent chest CT at the First Affiliated Hospital of Nanjing Medical University from June 2016 to June 2020 and were surgically confirmed to have invasive lung adenocarcinoma with or without lymph node metastasis was conducted. Patients were divided into a positive group and a negative group based on the presence or absence of lymph node metastasis. Clinical and imaging data of the patients were collected, and the independent risk factors for lymph node metastasis in resectable lung adenocarcinoma were analyzed using univariate and multivariate logistic regression. A combined spatial location-clinical-imaging feature prediction model for lymph node metastasis was established and compared with the traditional lymph node metastasis prediction model that does not include spatial location features. Results A total of 611 patients were included, with 333 in the positive group, including 172 males and 161 females, with an average age of (58.9±9.7) years; and 278 in the negative group, including 127 males and 151 females, with an average age of (60.1±11.4) years. Univariate and multivariate logistic regression analyses showed that the spatial relationship of the lesion to the lung hilum, nodule type, pleural changes, and serum carcinoembryonic antigen (CEA) levels were independent risk factors for lymph node metastasis. Based on this, the combined spatial location-clinical-imaging feature prediction model had a sensitivity of 91.67%, specificity of 74.05%, accuracy of 87.88%, and area under the curve (AUC) of 0.885. The traditional lymph node metastasis prediction model, which did not include spatial location features, had a sensitivity of 76.40%, specificity of 72.10%, accuracy of 53.86%, and AUC of 0.827. The difference in AUC between the two prediction methods was statistically significant (P=0.026). Compared with the traditional prediction model, the predictive performance of the combined spatial location-clinical-imaging feature prediction model was significantly improved. Conclusion In patients with resectable lung adenocarcinoma, those with central/inner spatial location, solid density, pleural changes with wide base depression, and elevated serum CEA levels have a higher risk of lymph node metastasis.