用于头颈癌预后生物标志物的放射组学机器学习分类器
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.
作者信息
Parmar Chintan, Grossmann Patrick, Rietveld Derek, Rietbergen Michelle M, Lambin Philippe, Aerts Hugo J W L
机构信息
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School , Boston, MA , USA ; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School , Boston, MA , USA ; Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University , Maastricht , Netherlands.
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School , Boston, MA , USA ; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School , Boston, MA , USA ; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute , Boston, MA , USA.
出版信息
Front Oncol. 2015 Dec 3;5:272. doi: 10.3389/fonc.2015.00272. eCollection 2015.
INTRODUCTION
"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients.
METHODS
Two independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n = 95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework.
RESULTS
We observed that the three feature selection methods minimum redundancy maximum relevance (AUC = 0.69, Stability = 0.66), mutual information feature selection (AUC = 0.66, Stability = 0.69), and conditional infomax feature extraction (AUC = 0.68, Stability = 0.7) had high prognostic performance and stability. The three classifiers BY (AUC = 0.67, RSD = 11.28), RF (AUC = 0.61, RSD = 7.36), and NN (AUC = 0.62, RSD = 10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance).
CONCLUSION
Our study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics-based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.
引言
“放射组学”以非侵入性且经济高效的方式提取和挖掘大量医学影像特征。放射组学的基本假设是这些影像特征能够量化整个肿瘤的表型特征。为提高放射组学在临床肿瘤学中的适用性，需要高度准确且可靠的机器学习方法。在这项放射组学研究中，对13种特征选择方法和11种机器学习分类方法在预测头颈癌患者总生存期方面的性能和稳定性进行了评估。
方法
对两个独立的头颈癌队列进行了研究。训练队列HN1由101名头颈癌患者组成。队列HN2（n = 95）用于验证。从CT图像中分割出的肿瘤区域共提取了440个放射组学特征。使用无偏评估框架对特征选择和分类方法进行了比较。
结果
我们观察到，最小冗余最大相关性（AUC = 0.69，稳定性 = 0.66）、互信息特征选择（AUC = 0.66，稳定性 = 0.69）和条件最大信息特征提取（AUC = 0.68，稳定性 = 0.7）这三种特征选择方法具有较高的预后性能和稳定性。BY（AUC = 0.67，相对标准偏差 = 11.28）、RF（AUC = 0.61，相对标准偏差 = 7.36）和NN（AUC = 0.62，相对标准偏差 = 10.52）这三种分类器也显示出较高的预后性能和稳定性。对性能变异性的分析表明，分类方法的选择是导致性能变异的主要因素（占总变异的29.02%）。
结论
我们的研究确定了用于预测头颈癌患者总生存期的具有预后性且可靠的机器学习方法。识别基于放射组学的预后分析的最佳机器学习方法可以拓宽放射组学在精准肿瘤学和癌症护理中的应用范围。
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