Table S1. Numbers of features remained after each selection step for CT-based signature building. Table S2. Numbers of features remained after each selection step for PET-based signature building. Table S3. Results of radiomics feature selection and Rad-score building. Table S4. Comparison of signatures combining features of different categories. Table S5. Results of univariate analysis. Table S6. Multivariate analysis results for disease-free survival in training set. Table S7. Baseline information of nomogram-defined high-risk and low-risk groups. Table S8. Survival outcomes between radiomics nomogram-defined high and low risk groups. Table S9. Results of multivariate analysis for the whole cohort. Table S10. Results of multivariate analysis within the high-risk group. Table S11. Results of multivariate analysis within the low-risk group. Table S12. Results of multivariate analysis within the low-risk group defined by nomogram A. Table S13. Results of multivariate analysis within the high-risk group defined by nomogram A. Table S14. Results of multivariate analysis within the low-risk group defined by nomogram B. Table S15. Results of multivariate analysis within the high-risk group defined by nomogram B.
Funding
National Natural Science Foundation of China
National Science and Technology
Natural Science Foundation of Guangdong Province
Health and Medical Collaborative Innovation Project of Guangzhou City, China
Program of Introducing Talents of Discipline to Universities
Innovation Team Development Plan of the Ministry of Education
Beijing Natural Science Foundation
National Key R&D Program of China
Youth Innovation Promotion Association CAS
ARTICLE ABSTRACT
We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)–based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC).
We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set (n = 470), and then validated it on a test set (n = 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein–Barr virus (EBV) DNA.
A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS (P < 0.001). Using these signatures, we proposed a radiomics nomogram with a C-index of 0.754 [95% confidence interval (95% CI), 0.709–0.800] in the training set and 0.722 (95% CI, 0.652–0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively (P < 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC.
Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.
