Application of immune cell infiltration in the diagnosis and prognosis of non-small cell lung cancer / 生物工程学报

Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.

CT texture analysis in bladder carcinoma: histologic grade characterization / 中华肿瘤杂志

Objective@#To explore the value of CT texture analysis (CTTA) in differentiating the pathological grade of urothelial carcinoma of the bladder (UCB).@*Methods@#A total of 53 lesions from 43 patients with bladder cancer confirmed by postoperative pathology were retrospectively analyzed, including 27 cases of high-grade urothelial carcinoma (HGUC) and 26 cases of low-grade urothelial carcinoma (LGUC). All the patients took pelvic CT and enhanced scanning in the same CT scanner with same scanning parameters. Lesions on both plain and enhanced CT images were delineated on software by two radiologists to extract the corresponding volumes of interest (VOI) and then 92 parameters based on feature classes were generated. The average values of two radiologists were obtained. The difference parameters between HGUC group and LGUC group were screened by nonparametric test, and the receiver operating characteristic (ROC) was drawn. The corresponding optimal thresholds were determined and diagnostic effect was assessed.@*Results@#Nine difference texture parameters between HGUC group and LGUC group were selected, including 5 parameters on unenhanced images, namely, skewness, root mean squared, cluster shade, zone percentage and large area high gray level emphasis. There were 4 parameters on enhanced images, namely, skewness, kurtosis, cluster shade and zone percentage. The largest area under curve of 0.840±0.058 (95% CI 0.726-0.955) was obtained from skewness generated by VOI of unenhanced images. The cut-off value of skewness was 0.186 5, which permitted the diagnosis of HGUC with sensitivity of 92.59%, specificity of 73.08%, positive predictive value of 78.13%, negative predictive value of 90.48% and accuracy of 83.02%.@*Conclusion@#CTTA can effectively distinguish between LGUC and HGUC. Skewness from unenhanced CT images had the optimal diagnostic performance.