1. A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma
- Author
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Lili Liu, Qingyu Wang, Bin Wang, Lu Wang, Ting Yan, Xiaofei Zhuang, Huijuan Liu, Meilan Peng, Zhenpeng Yan, Yanchun Ma, Yongping Cui, and Shan Zhang
- Subjects
Oncology ,medicine.medical_specialty ,business.industry ,Non invasive ,Neuroscience (miscellaneous) ,Nomogram ,Esophageal squamous cell carcinoma ,Cellular and Molecular Neuroscience ,Text mining ,Radiomics ,Internal medicine ,Medicine ,Progression-free survival ,business - Abstract
Objectives: To construct a prognostic model for preoperative prediction based on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC). Methods: Radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) with high throughput radiomics features that extracted from the CT images of 272 patients (204 in training and 68 in validation cohort), who were pathologically confirmed ESCC. Multivariable logistic regression was adopted to build the radiomics signature and another predictive nomogram model, which was composed with radiomics signature, traditional TNM stage and clinical features. Then its performance was assessed by the calibration and decision curve analysis (DCA). Results: 16 radiomics features were selected from 954 to build a radiomics signature，which were significantly associated with progression-free survival (PFS) (pvs 0.628; pvs 0.660; pConclusions: Multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.
- Published
- 2022