ObjectiveTo investigate the relationship between the expression of programmed cell death ligand-1 (PD-L1) and the maximal standardized uptake value (SUVmax) in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and the correlation of clinical factors between SUVmax values and PD-L1.MethodsThe clinical data of 84 patients with invasive lung adenocarcinoma diagnosed pathologically in West China Hospital, Sichuan University from August 2016 to November 2018 were analyzed retrospectively, including 38 males and 46 females, aged 60 (32-85) years. The tumor was acinar-predominant in 37 patients, papillary in 20, lepidic in 19, solid in 5 and micropapillary in 3. Multivariate analysis of the relationship between SUVmax value and other clinicopathological features was performed by linear regression. Logistic regression analysis was used to analyze the relationship between PD-L1 protein expression and other pathological features.ResultsThe SUVmax of the PD-L1 expression group was significantly higher than that of the non-PD-L1 expression group in the whole invasive lung adenocarcinoma group (P=0.002) and intermediate-grade histologic subtype (P=0.016). The SUVmax cut-off value of PD-L1 expression in the whole invasive lung adenocarcinoma group and intermediate-grade histologic subtype was 5.34 (AUC: 0.732, P=0.002) and 5.34 (AUC: 0.720, P=0.017), respectively. Multivariate analysis showed that pleura involvement, vascular tumor thrombus and the increase of tumor diameter could cause the increase of the SUVmax value, while the SUVmax value decreased in the moderately differentiated tumor compared with the poorly differentiated tumor. The SUVmax cut-off value between low-grade histologic subtype and intermediate-grade histologic subtype, intermediate-grade histologic subtype and high-grade histologic subtypes was 1.54 (AUC: 0.854, P<0.001) and 5.79 (AUC: 0.889, P<0.001), respectively. Multivariate analysis of PD-L1 expression showed pleura involvement (P=0.021, OR=0.022, 95%CI 0.001 to 0.558) and moderate differentiation (opposite to poor differentiation) (P=0.004, OR=0.053, 95%CI 0.007 to 0.042) decreased the expression of PD-L1.ConclusionThe SUVmax of the PD-L1 expression group is significantly higher than that of the non-PD-L1 expression group in the whole invasive lung adenocarcinoma group and intermediate-grade histologic subtype. The level of SUVmax and the expression of PD-L1 in invasive lung adenocarcinoma are related to many clinical factors.
Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.
Objective to develop machine learning models based on radiomics features extracted from the primary tumor, peritumoral region, and habitat region to predict lymph node metastasis status in patients with invasive pulmonary adenocarcinoma, and to evaluate the predictive performance and generalizability of different radiomic features. Methods A total of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Provincial People’s Hospital from 2016 to 2019 were enrolled. The habitat regions on CT images were delineated by applying K-means clustering (with an average cluster number of 2) based on grayscale values. The peritumoral region was defined as a uniform 3 mm expansion around the primary tumor, which was segmented using an automatic V-net model combined with manual correction. Radiomic features were then extracted from these predefined regions respectively, and stacking machine learning models were constructed. The model performance was evaluated on the training, testing, and internal validation cohorts using metrics including the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the inclusion criteria, 651 patients were finally included in the study, consisting of 181 males and 287 females, with a mean age of 58.39±11.23 years (range 29-78). Although the habitat radiomics-based model did not show the best performance in the training set, it demonstrated outstanding results in the internal validation set, achieving an AUC of 0.95 [95% CI (0.87, 1.00)], an F1 score of 0.85, and a precision-recall area under the curve (PR-AUC) of 0.89, outperforming models based on the primary tumor and peritumoral regions.ConclusionThe model constructed based on habitat radiomics exhibited superior performance in the internal validation set, suggesting it may possess better generalizability and clinical utility in predicting lymph node metastasis status in pulmonary adenocarcinoma.