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Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer

  
@article{ATM25865,
	author = {Minglei Yang and Yijiu Ren and Yunlang She and Dong Xie and Xiwen Sun and Jingyun Shi and Guofang Zhao and Chang Chen},
	title = {Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer},
	journal = {Annals of Translational Medicine},
	volume = {7},
	number = {12},
	year = {2019},
	keywords = {},
	abstract = {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},
	issn = {2305-5847},	url = {http://atm.amegroups.com/article/view/25865}
}