期刊
EBIOMEDICINE
卷 45, 期 -, 页码 70-80出版社
ELSEVIER
DOI: 10.1016/j.ebiom.2019.06.034
关键词
Head and neck squamous cell carcinoma; Radiomics; DNA methylation; Genomics; Transcriptomics
资金
- National Institute of Dental & Craniofacial Research (NIDCR) [U01 DE025188]
- National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIBIB) [R01 EB020527]
- National Cancer Institute (NCI) [U01 CA217851]
- China Scholarship Council [201606320087]
- China Medical Board Collaborating Program [15-216]
- Cyrus Tang Foundation
- Zhejiang University Education Foundation
- Sao Paulo State Foundation for Teaching and Research (FAPESP)
Background: Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC). Methods: We included 113 HNSCC patients from The Cancer Genome Atlas (TCGA-HNSCC) project. Molecular phenotypes analyzed were RNA-defined HPV status, five DNA methylation subtypes, four gene expression subtypes and five somatic gene mutations. A total of 540 quantitative image features were extracted from pretreatment CT scans. Features were selected and used in a regularized logistic regression model to build binary classifiers for each molecular subtype. Models were evaluated using the average area under the Receiver Operator Characteristic curve (AUC) of a stratified 10-fold cross-validation procedure repeated 10 times. Next, an HPV model was trained with the TCGA-HNSCC, and tested on a Stanford cohort (N = 53). Findings: Our results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for RNA-defined HPV+ (AUC = 0.73), DNA methylation subtypes MethylMix HPV+ (AUC = 0.79), non-CIMP-atypical (AUC = 0.77) and Stem-like-Smoking (AUC = 0.71), and mutation of NSD1 (AUC = 0.73). We externally validated the HPV prediction model (AUC = 0.76) on the Stanford cohort. When compared to clinical models, radiomic models were superior to subtypes such as NOTCH1 mutation and DNA methylation subtype non-CIMP-atypical while were inferior for DNA methylation subtype CIMP-atypical and NSD1 mutation. Interpretation: Our study demonstrates that radiomics can potentially serve as a non-invasive tool to identify treatment-relevant subtypes of HNSCC, opening up the possibility for patient stratification, treatment allocation and inclusion in clinical trials. (C) 2019 Published by Elsevier B.V.
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