8th Edition Tumor, Node, and Metastasis T-Stage Prognosis Discrepancies: Solid Component Diameter Predicts Prognosis Better than Invasive Component Diameter

Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence

An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5-1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0-I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.

A three serum miRNA panel as diagnostic biomarkers of radiotherapy-related metastasis in non-small cell lung cancer

Serum microRNAs (miRNAs) have been implicated as noninvasive biomarkers for lung cancer diagnosis. However, there are no sensitive and specific biomarkers for the detection of radiotherapy-related non-small cell lung cancer (NSCLC) metastasis. The present study aimed to investigate the role of three serum miRNAs, namely miRNA (miR)-130a, miR-25 and miR-191*, in diagnosing NSCLC, and their biological functions in radiation-mediated development of metastatic properties in A549 cells. To determine this, serum samples were collected from 84 patients with NSCLC and 42 age- and sex-matched healthy controls. Differential expression of serum miRNAs was analyzed by quantitative PCR. Significant associations between miRNA expression and overall survival of patients with NSCLC were identified using the Cox proportional regression model. A receiver operating characteristic curve was generated to evaluate diagnostic accuracy. The functions of miR-130a, miR-25 and miR-191* in lung cancer cells were studied by transfecting A549 cells with miRNA mimics and inhibitors. The results of the present study demonstrated that the expression levels of miR-130a, miR-25 and miR-191* in the serum of patients with NSCLC were increased compared with those in healthy controls, and these increases were associated with advanced age (≥60 years), radiotherapy, histological type (squamous carcinoma), low survival rate and low median survival time. Additionally, irradiation induced the upregulation of miR-130a, miR-25 and miR-191* expression in A549 cells in vitro and in a xenograft mouse model. Irradiation also promoted the invasiveness of A549 cells in vitro and metastasis in vivo. In conclusion, miR-130a, miR-25 and miR-191* may be potential biomarkers for the diagnosis of patients with NSCLC and may serve oncogenic roles in radiation-mediated metastasis of NSCLC.