Investigation of the correlation between the change in the projected lung area and forced vital capacity using biphasic chest dynamic digital radiography: a cross-sectional study.

Background: Chest dynamic digital radiography (DDR) is used as a supplementary tool for the routine pulmonary function test (PFT); however, its potential as a novel standard PFT method has yet to be explored. Therefore, the present study aimed to investigate the correlation between the change in the projected lung area (ΔPLA) and forced vital capacity (FVC) using chest DDR, and to establish a DDR-FVC estimation model and a predictive value model for the ΔPLA. Methods: In total, 139 participants who underwent chest DDR and the PFT in the same period at The First Affiliated Hospital of Guangzhou Medical University from April 2022 to February 2023 were prospectively included in the study. The patients' age, gender, height, and weight measurements were recorded. Additionally, the ΔPLA was measured, and the IWS workstation software was used for automated outlining and calculation. Subsequently, a correlation analysis and regression analysis models were employed to examine the relationship between the ΔPLA, FVC, and individual physiological characteristics. Additionally, an independent sample t-test was used to determine whether there were any significant differences between the normal and abnormal FVC groups. Results: The 139 participants were grouped according to the results of the ratio of measured/predicted FVC values (FVC%pred); those with an FVC%pred ≥80%, were allocated to the normal FVC group, and those with an FVC%pred <80% were allocated to the abnormal FVC group. The correlation coefficient was >0.8 in the full sample; the ΔPLA showed a significant linear correlation with the measured FVC value [r=0.81, 95% confidence interval (CI): 0.75-0.86, P<0.001]. There was a significant difference in the ΔPLA between the normal and abnormal FVC groups. With the ΔPLA, age, gender, height, and weight as predictor variables, the following DDR-FVC estimation model was established: DDR-FVC estimation model = -0.997 + 1.35×10-4 × ΔPLA + 0.017 × height - 0.014 × age + 0.249 × gender (1 for male and 0 for female) [adjusted R2 (adj. R2)=0.731, F=94.615, P<0.001]. The following formula was used to determine the predictive value of the ΔPLA: Predictive value of ΔPLA = -12,504.287 + 173.185 × height + 62.971 × weight - 84.933 × age (adj. R2=0.393, F=20.453, P<0.001). Conclusions: There was a linear correlation between the ΔPLA measured by biphasic chest DDR and the FVC. A model for estimating the FVC was established based on the ΔPLA, which allows the FVC to be assessed by the ΔPLA measured by biphasic chest DDR. A predictive value model for the ΔPLA was also established to provide ΔPLA reference values for assessment and comparison.

Development of a nomogram for predicting survival in clinical T1N0M1 lung adenocarcinoma: a population-based study.

OBJECTIVE: This study aimed to establish a prognostic model for clinical T1N0M1 (cT1N0M1) lung adenocarcinoma patients to evaluate the prognosis of patients in terms of overall survival (OS) rate and cancer-specific survival (CSS) rate. METHODS: Data of patients with metastatic lung adenocarcinoma from 2010 to 2016 were collected from the Surveillance, Epidemiology and End Results database. Multivariate Cox regression analysis was conducted to identify relevant prognostic factors and used to develop nomograms. The receiver operating characteristic (ROC) curve and calibration curve are used to evaluate the predictive ability of the nomograms. RESULTS: A total of 45610 patients were finally included in this study. The OS and CSS nomograms were constructed by same clinical indicators such as age (<60 years or ≥60 years), sex (female or male), race (white, black, or others), surgery, radiation, chemotherapy, and the number of metastatic sites, based on the results of statistical Cox analysis. From the perspective of OS and CSS, surgery contributed the most to the prognosis. The ROC curve analysis showed that the survival nomograms could accurately predict OS and CSS. According to the points obtained from the nomograms, survival was estimated by the Kaplan-Meier method, then cT1N0M1 patients were divided into three groups: low-risk group, intermediate-risk group, and high-risk group, and the OS ( P  < 0.001) and CSS ( P  < 0.001) were significantly different among the three groups. CONCLUSION: The nomograms and risk stratification model provide a convenient and reliable tool for individualized evaluation and clinical decision-making of patients with cT1N0M1 lung adenocarcinoma.