Nitric oxide (NO) is a pleiotropic regulator, critical to numerous biological processes, including vasodilatation, neurotransmission and macrophage-mediated immunity. The family of nitric oxide synthases (NOS) comprises inducible NOS (iNOS), endothelial NOS (eNOS), and neuronal NOS (nNOS). Interestingly, various studies have shown that all three isoforms can be involved in promoting or inhibiting the etiology of cancer. NOS activity has been detected in tumour cells of various histogenetic origins and has been associated with tumour grade, proliferation rate and expression of important signaling components associated with cancer development such as the oestrogen receptor. It appears that high levels of NOS expression (for example, generated by activated macrophages) may be cytostatic or cytotoxic for tumor cells, whereas low level activity can have the opposite effect and promote tumour growth. Paradoxically therefore, NO (and related reactive nitrogen species) may have both genotoxic and angiogenic properties. Increased NO-generation in a cell may select mutant p53 cells and contribute to tumour angiogenesis by upregulating VEGF. In addition, NO may modulate tumour DNA repair mechanisms by upregulating p53, poly(ADP-ribose) polymerase (PARP) and the DNA-dependent protein kinase (DNA-PK). An understanding at the molecular level of the role of NO in cancer will have profound therapeutic implications for the diagnosis and treatment of disease.

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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 a training set (n ¼ 470), and then validated it on a test set (n ¼ 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 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. Conclusions: 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.

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