J Cancer 2019; 10(18):4217-4225. doi:10.7150/jca.33345
Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging
1. Department of Radiology, Guangdong Provincial People's Hospital/ Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China
2. Institute of Automation, Chinese Academy of Sciences, CAS Key Laboratory of Molecular Imaging, Beijing, PR China
3. Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China
4. Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China
5. Affiliated Hospital of Guizhou Medical University, Guiyang, Department of Radiology Guiyang, Guizhou, PR China
*Lu Zhang, Hongyu Zhou, Dongsheng Gu, and Jie Tian contributed equally to this work.
Zhang L, Zhou H, Gu D, Tian J, Zhang B, Dong D, Mo X, Liu J, Luo X, Pei S, Dong Y, Huang W, Chen Q, Liang C, Lian Z, Zhang S. Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging. J Cancer 2019; 10(18):4217-4225. doi:10.7150/jca.33345. Available from http://www.jcancer.org/v10p4217.htm
Background: To develop and validate a radiomic nomogram incorporating radiomic features with clinical variables for individual local recurrence risk assessment in nasopharyngeal carcinoma (NPC) patients before initial treatment.
Methods: One hundred and forty patients were randomly divided into a training cohort (n = 80) and a validation cohort (n = 60). A total of 970 radiomic features were extracted from pretreatment magnetic resonance (MR) images of NPC patients from May 2007 to December 2013. Univariate and multivariate analyses were used for selecting radiomic features associated with local recurrence, and multivariate analyses was used for building radiomic nomogram.
Results: Eight contrast-enhanced T1-weighted (CET1-w) image features and seven T2-weighted (T2-w) image features were selected to build a Cox proportional hazard model in the training cohort, respectively. The radiomic nomogram, which combined radiomic features and multiple clinical variables, had a good evaluation ability (C-index: 0.74 [95% CI: 0.58, 0.85]) in the validation cohort. The radiomic nomogram successfully categorized those patients into low- and high-risk groups with significant differences in the rate of local recurrence-free survival (P <0.05).
Conclusions: This study demonstrates that MR imaging-based radiomics can be used as an aid tool for the evaluation of local recurrence, in order to develop tailored treatment targeting specific characteristics of individual patients.
Keywords: Magnetic Resonance Imaging, Nasopharyngeal Carcinoma, Local Recurrence, Radiomic Feature, Nomogram