TY - JOUR AU - Yang, Minglei AU - Ren, Yijiu AU - She, Yunlang AU - Xie, Dong AU - Sun, Xiwen AU - Shi, Jingyun AU - Zhao, Guofang AU - Chen, Chang PY - 2019 TI - Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer JF - Annals of Translational Medicine; Vol 7, No 12 (June 26, 2019): Annals of Translational Medicine Y2 - 2019 KW - N2 - Background: Dry pleural dissemination (DPD) in non-small cell lung cancer (NSCLC) is defined as having solid pleural metastases without malignant pleural effusion. We aim to identify DPD by applying radiomics, a novel approach to decode the tumor phenotype. Methods: Preoperative chest computed tomographic images and basic clinical feature were retrospectively evaluated in patients with surgically resected NSCLC between January 1, 2015 and December 31, 2016. Propensity score was applied to match the DPD and non-DPD groups. One thousand and eighty radiomics features were quantitatively extracted by the 3D slicer software and “pyradiomics” package. Least absolute shrinkage and selection operator (LASSO) binary model was applied for feature selection and developing the radiomics signature. The discrimination was evaluated using area under the curve (AUC) and Youden index. Results: Sixty-four DPD patients and paired 192 non-DPD patients were enrolled. Using the LASSO model, this study developed a radiomics signature including 10 radiomic features. The mean ± standard deviation values of the radiomics signature with DPD status (−2.129±1.444) was significantly higher compared to those with non-DPD disease (0.071±0.829, P Conclusions: The signature based on radiomics features can provide potential predictive value to identify DPD in patients with NSCLC. UR - https://atm.amegroups.org/article/view/25865