Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
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DOI：
10.1016/j.ebiom.2019.06.034
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发表时间：
2019-07-01
期刊：
影响因子：
11.1
通讯作者：
Gevaert, Olivier
中科院分区：
文献类型：
--
作者：
Huang, Chao;Cintra, Murilo;Gevaert, Olivier
背景：基于影像组学的非侵入性生物标志物有望促进头颈部鳞状细胞癌（HNSCC）患者治疗相关分子亚型的转化，以用于治疗分配。
方法：我们纳入了来自癌症基因组图谱（TCGA - HNSCC）项目的113例HNSCC患者。所分析的分子表型包括RNA定义的人乳头瘤病毒（HPV）状态、5种DNA甲基化亚型、4种基因表达亚型以及5种体细胞基因突变。从治疗前的CT扫描中提取了总共540个定量影像特征。对特征进行选择，并用于正则化逻辑回归模型中，为每种分子亚型构建二元分类器。使用分层10折交叉验证程序重复10次的受试者工作特征曲线（ROC曲线）下平均面积（AUC）对模型进行评估。接下来，使用TCGA - HNSCC训练一个HPV模型，并在斯坦福队列（N = 53）中进行测试。
研究结果：我们的结果表明，定量影像特征能够区分几种分子表型。我们在RNA定义的HPV +（AUC = 0.73）、DNA甲基化亚型MethylMix HPV +（AUC = 0.79）、非CpG岛甲基化表型（CIMP）-非典型（AUC = 0.77）和干细胞样 - 吸烟（AUC = 0.71）以及NSD1突变（AUC = 0.73）方面获得了显著的预测性能。我们在斯坦福队列中对HPV预测模型进行了外部验证（AUC = 0.76）。与临床模型相比，影像组学模型在诸如NOTCH1突变和DNA甲基化亚型非CIMP - 非典型等亚型方面表现更优，而在DNA甲基化亚型CIMP - 非典型和NSD1突变方面表现较差。
解释：我们的研究表明，影像组学有可能作为一种非侵入性工具来识别HNSCC的治疗相关亚型，为患者分层、治疗分配以及纳入临床试验开辟了可能性。（C）2019由爱思唯尔出版集团出版
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.