1. Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma
- Author
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Wen Fei Li, Lei Chen, Wei Fan, Li Tian, Di Dong, Lu Li, Yan Ping Mao, Ling Long Tang, Jun Ma, Hao Peng, Li Zhi Liu, Ying Sun, Ai Hua Lin, Mengjie Fang, and Jie Tian
- Subjects
Adult ,Male ,Cancer Research ,medicine.medical_specialty ,Adolescent ,Concordance ,030218 nuclear medicine & medical imaging ,Young Adult ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Risk Factors ,Positron Emission Tomography Computed Tomography ,medicine ,Humans ,Child ,Survival rate ,Aged ,Retrospective Studies ,PET-CT ,Nasopharyngeal Carcinoma ,medicine.diagnostic_test ,Receiver operating characteristic ,business.industry ,Induction chemotherapy ,Nasopharyngeal Neoplasms ,Induction Chemotherapy ,Middle Aged ,Nomogram ,Prognosis ,medicine.disease ,Survival Rate ,Nomograms ,ROC Curve ,Oncology ,Nasopharyngeal carcinoma ,Positron emission tomography ,030220 oncology & carcinogenesis ,Female ,Radiology ,business - Abstract
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 = 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 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.
- Published
- 2019