{Reference Type}: Journal Article
{Title}: Established the prediction model of early-stage non-small cell lung cancer spread through air spaces (STAS) by radiomics and genomics features.
{Author}: Wang Y;Li C;Wang Z;Wu R;Li H;Meng Y;Liu H;Song Y;
{Journal}: Asia Pac J Clin Oncol
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jul 1
{Factor}: 1.926
{DOI}: 10.1111/ajco.14099
{Abstract}: <B>BACKGROUND: </B>This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.<BR><B>METHODS: </B>We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).<BR><B>RESULTS: </B>The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).<BR><B>CONCLUSIONS: </B>The prediction model based on radiomics and genomics features has a good prediction performance for STAS.