Additional file 1 of Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study
Creators: name: Liu, Ting
name: Dong, Di
name: Zhao, Xun
name: Ou, Xiao-Min
name: Yi, Jun-Lin
name: Guan, Jian
name: Zhang, Ye
name: Xiao-Fei, Lv
name: Xie, Chuan-Miao
name: Luo, Dong-Hua
name: Sun, Rui
name: Chen, Qiu-Yan
name: Xing, Lv
name: Guo, Shan-Shan
name: Liu, Li-Ting
name: Lin, Da-Feng
name: Chen, Yan-Zhou
name: Lin, Jie-Yi
name: Luo, Mei-Juan
name: Yan, Wen-Bin
name: He, Mei-Lin
name: Mao, Meng-Yuan
name: Zhu, Man-Yi
name: Chen, Wen-Hui
name: Shen, Bo-Wen
name: Wang, Shi-Qian
name: Li, Hai-Lin
name: Zhong, Lian-Zhen
name: Hu, Chao-Su
name: Wu, De-Hua
name: Mai, Hai-Qiang
name: Tian, Jie
name: Tang, Lin-Quan
Publisher: name: figshare
Year: 2023
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Cancer

description: Additional file 1: Supplementary methods A1-A13. Figures S1-S8. Tables S1-S11. Methods A1. Inclusion and exclusion criteria. Methods A2. Treatment protocols. Methods A3. Patient characteristics. Methods A4. Diagnosis of PRNN and follow-up protocol. Methods A5. ROI segmentation. Methods A6. Radiomic features extraction. Methods A7. Feature selection and radiomic signature building. Methods A8. Candidate clinical variables and model building. Methods A9. Model performance. Methods A10. Transcriptome sequencing. Methods A11. GSEA. Methods A12. Radiomics quality score. Methods A13. Random forest model parameters and the included radiomic features. Figure S1. Clinical manifestations of post-radiation nasopharyngeal necrosis. Figure S2. The diagnostic workflow of post-radiation nasopharyngeal necrosis. Figure S3. Segmentation criteria of region-of-interest. Figure S4. Overall survival of patients with and without post-radiation nasopharyngeal necrosis. Figure S5. Pair-wise correlations among six selected radiomic features. Figure S6. Confusion matrix for the prediction of PRNN using radiomics model. Figure S7. Nomogram predicting risk of post-radiation nasopharyngeal necrosis at different timepoints. Figure S8. Prognostic significance of the radiomic signature. Table S1. Acquisition parameters of MRI scanning. Table S2. Summary of clinical outcomes of patients in training and validation cohorts. Table S3. Choices of methods for radiomic signature development. Table S4. Number of features remained after each step of feature selection. Table S5. Details of the six radiomic features included in the random forest model. Table S6. Candidate clinical and radiomics variables for prediction of PRNN in training cohort. Table S7. Performance of the radiomics and clinical models in training and validation cohorts. Table S8. Generalizability of the radiomic signature across medical centers and MRI parameters. Table S9. Generalizability of the radiomic signature across patient subgroups. Table S10. Prognostic significance of the radiomic signature in multivariate Cox analyses. Table S11. Core enrichment genes of the biological processes associated with the radiomic signature.