Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients.
Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients.
复制标题
通过口咽头和颈癌患者中的原发性肿瘤纹理分析来研究用于局部复发的放射素特征。
DOI：
10.1038/s41598-017-14687-0
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发表时间：
2018-01-24
影响因子：
4.6
通讯作者：
M. D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group
中科院分区：
文献类型：
--
作者：
M. D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group
放射组学是一种这样的“大数据”方法，其应用先进的图像细化/数据表征算法来生成可以以非侵入性方式对肿瘤表型进行定量分类的成像特征。我们假设口咽癌（OPC）原发性肿瘤的某些纹理特征与患者结局（如局部控制）具有统计学显著相关性。来自IRB批准的数据库的因局部晚期OPC接受（化疗）放疗的患者纳入本回顾性系列。提取治疗前对比CT扫描，并使用在Matlab平台上运行的成像生物标志物探测器（IBEX）软件对原发性疾病的总肿瘤体积（GTVp）进行基于放射学的分析。数据集被随机分为训练数据集和测试和调整保留数据集。应用机器学习方法来产生由具有最小重叠和最大预后意义的特征组成的放射组学特征。放射组学特征适于区分患者，与其他关键临床指标一致。465例患者可用于分析。基于强度直接和相邻强度差方法，推导出由来自治疗前成像的2个放射组学特征组成的签名。对所得分组的分析显示，复发概率和Kaplan-Meier估计的局部控制率（LCR）在“有利”和“不利”聚类之间存在显著差异。
Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.
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影响因子：
3.8
作者：
Deasy, JO;Blanco, AI;Clark, VH
通讯作者：
Clark, VH
影响因子：
2.6
作者：
Ganeshan, B.;Skogen, K.;Miles, K.
通讯作者：
Miles, K.
影响因子：
4.6
作者：
Parmar C;Grossmann P;Bussink J;Lambin P;Aerts HJWL
通讯作者：
Aerts HJWL
DOI：
10.1016/j.compmedimag.2015.04.006
发表时间：
2015-09-01
影响因子：
5.7
作者：
Fave, Xenia;Cook, Molly;Court, Laurence
通讯作者：
Court, Laurence
影响因子：
51.1
作者：
O'Sullivan, Brian;Huang, Shao Hui;Xu, Wei
通讯作者：
Xu, Wei