| 基于定量影像组学的肺肿瘤良恶性预测方法 |
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| 引用本文： | 张利文, 刘侠, 汪俊, 董迪, 宋江典, 臧亚丽, 田捷. 基于定量影像组学的肺肿瘤良恶性预测方法. 自动化学报, 2017, 43(12): 2109-2114. doi: 10.16383/j.aas.2017.c160264 |
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| 作者姓名： | 张利文 刘侠 汪俊 董迪 宋江典 臧亚丽 田捷 |
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| 作者单位： | 1.哈尔滨理工大学自动化学院 哈尔滨 150080;;2.中国科学院自动化研究所 北京 100190;;3.东北大学中荷生物医学与信息工程学院 沈阳 110819 |
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| 基金项目： | 黑龙江省然科学基金F201311国家自然科学基金81227901国家自然科学基金81301346黑龙江省然科学基金12541105国家自然科学基金81501616中国科学院科研设备项目YZ201502国家自然科学基金61231004中国科学院科技服务网络计划KFJ-SW-STS-160国家自然科学基金61672197国家自然科学基金81527805 |
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| 摘 要： | 肺癌是世界范围内致死率最高的癌症之一，肺肿瘤的良恶性诊断对于治疗方式选择意义重大.本文借助影像组学（Radiomics）方法利用LIDC（Lung imaging database consortium）肺癌公开数据库中619例病人的肺癌计算机断层（Computed tomography，CT）影像数据，分割出病变区域，并结合肿瘤医学特性和临床认知，提取反映肿瘤形状大小、强度和纹理特性的60个定量影像特征，然后利用支持向量机（Support vector machine，SVM）构建诊断肺肿瘤良恶性的预测模型，筛选出对诊断肺肿瘤良恶性有价值的20个影像组学特征.为肺肿瘤良恶性预测提供了一种非入侵的检测手段.随着CT影像在肺癌临床诊断中的广泛使用，应用样本量的不断增加，本文方法有望成为一种辅助诊断工具，有效提高临床肺肿瘤良恶性诊断准确率.
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| 关 键 词： | 影像组学 肺癌 图像分割 特征提取 支持向量机 |
| 收稿时间： | 2016-03-11 |
Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method |
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| ZHANG Li-Wen, LIU Xia, WANG Jun, DONG Di, SONG Jiang-Dian, ZANG Ya-Li, TIAN Jie. Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method. ACTA AUTOMATICA SINICA, 2017, 43(12): 2109-2114. doi: 10.16383/j.aas.2017.c160264 |
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| Authors: | ZHANG Li-Wen LIU Xia WANG Jun DONG Di SONG Jiang-Dian ZANG Ya-Li TIAN Jie |
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| Affiliation: | 1. School of Automation, Harbin University of Science and Technology, Harbin 150080;;2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190;;3. School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shengyang 110819 |
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| Abstract: | Lung cancer is a leading cause of cancer mortality around the world. Accurate diagnosis of lung cancer is significant for treatment regimen selection. Radiomics refers to comprehensively quantifying the tumor phenotypes by applying a large number of quantitative image features. Here we analyze a computed tomography (CT) data set of 619 patients with lung cancer on the lung image database consortium (LIDC) by radiomic method. Combining with the medical character and clinical recognition of lung tumor, we present a radiomic analysis of 60 features. Then, we use SVM to build a prediction model and find radiomic features which have predictive value for discrimination of malignant and benign lung tumors. Nowadays, as CT imaging is routinely used in lung cancer clinical diagnosis, there is an increase in data set size. We consider that our radiomic prediction model will be developed a valuable medical software and an auxiliary tool which can provide malignant and benign information of lung tumors efficiently. |
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| Keywords: | Radiomics lung cancer image segmentation feature extraction support vector machine (SVM) |
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