Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.
Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.
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DOI：
10.1016/j.radonc.2018.06.025
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发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Fan Y
中科院分区：
文献类型：
--
作者：
Li H;Galperin-Aizenberg M;Pryma D;Simone CB 2nd;Fan Y
为了预测接受立体定向体部放射治疗（SBRT）的NSCLC患者的治疗反应和生存率，我们开发了一种无监督的机器学习方法，用于对患者进行分层并同时基于成像数据提取元特征。本研究基于100例接受SBRT治疗早期NSCLC的连续患者的18F-FDG-PET数据集进行。每个病人的肿瘤的特点是722放射组学特征。一个无监督的双向聚类方法被用来同时识别患者组和放射组学特征。比较各组患者的生存率和无淋巴结衰竭的情况。计算元特征以建立生存模型来预测生存和无节点失效。当患者在生存率（p = 0.003）和无淋巴结衰竭（p = 0.038）方面被分为3组时，发现2组患者之间存在差异。预测生存率和淋巴结衰竭的平均一致性指标分别为0.640 ± 0.029和0.664 ± 0.063，优于基于临床变量建立的预测模型（p < 0.04）。评估结果表明，我们的方法使我们能够对患者进行分层，并预测生存率和节点故障的自由度，其性能优于目前的替代方法。
To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. This study was performed based on an 18F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient’s tumor was characterized by 722 radiomic features. An unsupervised two-way clustering method was used to identify groups of patients and radiomic features simultaneously. The groups of patients were compared in terms of survival and freedom from nodal failure. Meta-features were computed for building survival models to predict survival and free of nodal failure. Differences were found between 2 groups of patients when the patients were clustered into 3 groups in terms of both survival (p = 0.003) and freedom from nodal failure (p = 0.038). Average concordance measures for predicting survival and nodal failure were 0.640 ± 0.029 and 0.664 ± 0.063 respectively, better than those obtained by prediction models built upon clinical variables (p < 0.04). The evaluation results demonstrate that our method allows us to stratify patients and predict survival and freedom from nodal failure with better performance than current alternative methods.
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DOI：
10.1016/j.radonc.2016.04.004
发表时间：
2016-06
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Coroller TP;Agrawal V;Narayan V;Hou Y;Grossmann P;Lee SW;Mak RH;Aerts HJ
通讯作者：
Aerts HJ
影响因子：
5.7
作者：
Huynh, Elizabeth;Coroller, Thibaud P.;Aerts, Hugo J. W. L.
通讯作者：
Aerts, Hugo J. W. L.
影响因子：
3.9
作者：
Hawkins, Samuel H.;Korecki, John N.;Gillies, Robert J.
通讯作者：
Gillies, Robert J.
放射组学特征：预测早期（I 期或 II 期）非小细胞肺癌无病生存的潜在生物标志物
影响因子：
19.7
作者：
Huang, Yanqi;Liu, Zaiyi;Liang, Changhong
通讯作者：
Liang, Changhong
影响因子：
7.7
作者：
Grossmann P;Stringfield O;El-Hachem N;Bui MM;Rios Velazquez E;Parmar C;Leijenaar RT;Haibe-Kains B;Lambin P;Gillies RJ;Aerts HJ
通讯作者：
Aerts HJ