CT-Based Radiomics Nomogram for Prediction of Progression-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma

Abstract

Chang Yan,1 De-Song Shen,1 Xiao-Bo Chen,2 Dan-Ke SU,3 Zhong-Guo Liang,1 Kai-Hua Chen,1 Ling Li,1 Xia Liang,1 Hai Liao,3 Xiao-Dong Zhu1,4 1Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People’s Republic of China; 2School of First Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 3Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People’s Republic of China; 4Affiliated Wuming Hospital of Guangxi Medical University, Nanning, Guangxi, 530100, People’s Republic of ChinaCorrespondence: Xiao-Dong ZhuDepartment of Radiation Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning City, Guangxi, 530021, People’s Republic of ChinaTel [phone]Email [email protected] LiaoDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People’s Republic of ChinaTel [phone]Email [email protected]: We aimed to construct of a nomogram to predict progression-free survival (PFS) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) with risk stratification using computed tomography (CT) radiomics features and clinical factors.Patients and Methods: A total of 311 patients diagnosed with LA-NPC (stage III–IVa) at our hospital between 2010 and 2014 were included. The region of interest (ROI) of the primary nasopharyngeal mass was manually outlined. Independent sample t-test and LASSO-logistic regression were used for selecting the most predictive radiomics features of PFS, and to generate a radiomics signature. A nomogram was built with clinical factors and radiomics features, and the risk stratification model was tested accordingly.Results: In total, 20 radiomics features most associated with prognosis were selected. The radiomics nomogram, which integrated the radiomics signature and significant clinical factors, showed excellent performance in predicting PFS, with C-index of 0.873 (95% CI: 0.803∼ 0.943), which was better than that of the clinical nomogram (C-index, 0.729, 95% CI: 0.620∼ 0.838) as well as of the TNM staging system (C-index, 0.689, 95% CI: 0.592– 0.787) in validation cohort. The calibration curves and the decision curve analysis (DCA) plot obtained suggested satisfying accuracy and clinical utility of the model. The risk stratification tool was able to predict differences in prognosis of patients in different risk categories (p< 0.001).Conclusion: CT-based radiomics features, an in particular, radiomics nomograms, have the potential to become an accurate and reliable tool for assisting with prognosis prediction of LA-NPC.Keywords: computed tomography, locoregionally advanced nasopharyngeal carcinoma, radiomics, nomogram

Keywords

• computed tomography
• locoregionally advanced nasopharyngeal carcinoma， radiomics
• nomogram