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Title
Prognostic Value of Deep Learning PET/CT-based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma
Purpose
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).
Experimental Design
We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in 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.
Results
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 training set and 0.722 (95% CI, 0.652-0.792) in 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 EBV DNA-based model (C-index: 0.754 vs. 0.675 in training set and 0.722 vs. 0.671 in test set) in risk stratification and guiding IC.
Conclusion
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.
Figure 1. Radiomics workflow in this study.
Figure 2. (A) Radiomics nomogram; (B) Radiomics nomogram calibration curves. PET, positron emission tomography; CT, computed tomography; DFS, disease-free survival.