Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study
Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study
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DOI：
10.1016/j.ejmp.2017.10.008
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发表时间：
2018-01-01
影响因子：
3.4
通讯作者：
Kazemnejad, Anoshirvan
中科院分区：
文献类型：
--
作者：
Abdollahi, Hamid;Mostafaei, Shayan;Kazemnejad, Anoshirvan
目的：头颈癌（H&N）放化疗（CRT）后，患者可能发生严重的感音神经性听力损失（SNHL），影响患者的生活质量。提出放射学特征分析预测CRT所致SNHL。材料与方法：对47例不同类型H&N癌行三维适形放射治疗（3DCRT）的患者94例耳蜗490个图像特征进行CT提取。不同的机器学习（ML）算法以及最小绝对收缩和选择算子（LASSO）惩罚逻辑回归对放射学特征进行特征选择、分类和预测。结果建模采用LASSO惩罚logistic模型。结果：10种ML方法（准确度、精密度和受试者工作特征曲线下面积）的预测能力均在70%以上。根据LASSO惩罚逻辑模型，490个放射性特征中有10个被选为与SNHL状态相关的特征。10个特征均与SNHL有统计学相关性（p值均< 0.001）。结论：本研究提出的CT放射组学分析有助于预测放化疗所致的听力损失。我们的研究还表明，放射学特征与临床和剂量学变量的结合可以模拟放疗结果，如SNHL。
Objectives: Immediately or after head-and-neck (H&N) cancer chemoradiotherapy (CRT), patients may undergone significant sensorineural hearing loss (SNHL) which could affect their quality of life. Radiomic feature analysis is proposed to predict SNHL induced by CRT. Material and methods: 490 image features of 94 cochlea from 47 patients treated with three dimensional conformal RT (3DCRT) for different H&N cancers were extracted from CT images. Different machine learning (ML) algorithms and also least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented on radiomic features for feature selection, classification and prediction. Also, LASSO penalized logistic model was used for outcome modelling. Results: The predictive power of ten ML methods was more than 70% (in accuracy, precision and area under the curve of receiver operating characteristic curve (AUC)). According to the LASSO penalized logistic modelling, 10 of the 490 radiomic features selected as the associated features with SNHL status. All of the 10 features were statistically associated with SNHL (all of adjusted P-values < .001). Conclusion: CT radiomic analysis proposed in this study, could help in the prediction of hearing loss induced by chemoradiation. Our study also, demonstrates that combination of radiomic features with clinical and dosimetric variables can model radiotherapy outcome such as SNHL.