A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.
A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.
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DOI：
10.1038/s41598-017-13448-3
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发表时间：
2017-10-16
影响因子：
4.6
通讯作者：
Richter C
中科院分区：
文献类型：
--
作者：
Leger S;Zwanenburg A;Pilz K;Lohaus F;Linge A;Zöphel K;Kotzerke J;Schreiber A;Tinhofer I;Budach V;Sak A;Stuschke M;Balermpas P;Rödel C;Ganswindt U;Belka C;Pigorsch S;Combs SE;Mönnich D;Zips D;Krause M;Baumann M;Troost EGC;Löck S;Richter C
放射组学将机器学习算法应用于定量成像数据，以表征肿瘤表型并预测临床结果。对于放射组学风险模型的开发，有多种不同的算法可用，但不清楚哪一种算法能给出最佳结果。因此，我们通过一致性指数（C-Index）评估了11种机器学习算法结合12种特征选择方法的性能，以预测头颈部鳞状细胞癌患者的局部区域肿瘤控制（LRC）和总生存期。所考虑的算法能够处理连续的时间到事件生存数据。在多中心队列（213例患者）中进行特征选择和模型构建，并使用独立队列（80例患者）进行验证。我们发现了几种机器学习算法和特征选择方法的组合，取得了类似的结果，例如MSR-RF: C-Index = 0.71, BT-COX: C-Index = 0.70，结合Spearman特征选择。使用表现最好的模型，将患者分为低复发风险组和高复发风险组。在验证队列中，两组之间的LRC有显著差异。基于上述分析，我们确定了未来放射组学研究中应考虑的算法子集，以开发稳定且临床相关的时间到事件终点预测模型。
Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.
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影响因子：
2
作者：
Pencina, MJ;D'Agostino, RB
通讯作者：
D'Agostino, RB
影响因子：
5.7
作者：
Linge, Annett;Lohaus, Fabian;Baumann, Michael
通讯作者：
Baumann, Michael
影响因子：
4.6
作者：
Parmar C;Grossmann P;Bussink J;Lambin P;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
4.6
作者：
Parmar C;Leijenaar RT;Grossmann P;Rios Velazquez E;Bussink J;Rietveld D;Rietbergen MM;Haibe-Kains B;Lambin P;Aerts HJ
通讯作者：
Aerts HJ
影响因子：
10.6
作者：
Gomez, W.;Pereira, W. C. A.;Infantosi, A. F. C.
通讯作者：
Infantosi, A. F. C.