Differentiating Brain Metastases from Different Pathological Types of Lung Cancers Using Texture Analysis of T1 Postcontrast MR
Differentiating Brain Metastases from Different Pathological Types of Lung Cancers Using Texture Analysis of T1 Postcontrast MR
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DOI：
10.1002/mrm.26029
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发表时间：
2016-11-01
影响因子：
3.3
通讯作者：
Li, Baosheng
中科院分区：
文献类型：
--
作者：
Li, Zhenjiang;Mao, Yu;Li, Baosheng
目的：本研究的目的是探讨利用T1增强MR图像的纹理分析（TA）区分不同类型肺癌脑转移瘤的可行性。使用Kruskal-Wallis检验和受试者操作特征分析研究了每种纹理对不同类型肺癌进行分类的能力。采用K-最近邻（KNN）分类器模型和反向传播人工神经网络（BP-ANN）分类器模型建立模型，提高TA的预测能力。结果：基于纹理的病灶分类在区分不同类型肺癌脑转移瘤方面具有高度特异性，错误分类率分别为3.1%，4.3%，5.8%和8.1%，用于小细胞肺癌、鳞状细胞癌、腺癌和大细胞肺癌。BP-ANN模型的预测能力优于KNN模型。结论：TA可以预测不同病理类型肺癌脑转移瘤的差异。纹理参数反映了肿瘤的组织病理学结构，可作为临床准确诊断的辅助工具，值得进一步研究。(C)2015年国际医学磁共振学会
Purpose: The goal of this study was to investigate the feasibility of differentiating brain metastases from different types of lung cancers using texture analysis (TA) of T1 postcontrast MR images.Methods: TA was performed, and four subset textures were extracted and calculated separately. The capability of each texture to classify the different types of lung carcinoma was investigated using the Kruskal-Wallis test and receiver operating characteristic analysis. K-nearest neighbor (KNN) classifier model and back-propagation artificial neural network (BP-ANN) classifier model were used to build models and improve the predictive ability of TA.Results: Texture-based lesion classification was highly specific in differentiating brain metastases originated from different types of lung cancers, with misclassification rates of 3.1%, 4.3%, 5.8%, and 8.1%, respectively, for small cell lung carcinoma, squamous cell carcinoma, adenocarcinoma, and large cell lung carcinoma. The BP-ANN model had a better predictive ability than the KNN model. No texture feature could distinguish between all four types of lung cancer.Conclusions: TA may predict the differences among various pathological types of lung cancer with brain metastases. The texture parameters, which reflect the tumor histopathology structure, may serve as an adjunct tool for clinically accurate diagnoses and deserves further investigation. (C) 2015 International Society for Magnetic Resonance in Medicine