自动化学报2017，Vol.43Issue(12)：2109-2114,6.DOI:10.16383/j.aas.2017.c160264
基于定量影像组学的肺肿瘤良恶性预测方法
Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method
摘要
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.关键词
影像组学/肺癌/图像分割/特征提取/支持向量机Key words
Radiomics/lung cancer/image segmentation/feature extraction/support vector machine(SVM)引用本文复制引用
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[J].自动化学报,2017,43(12):2109-2114,6.基金项目
国家自然科学基金(81227901,81527805,61231004,81501616,81301346,61672197),黑龙江省然科学基金(F201311,12541105),中国科学院科技服务网络计划(KFJ-SW-STS-160),中国科学院科研设备项目(YZ201502)资助Supported by National Natural Science Foundation of China(81227901,81527805,61231004,81501616,81301346,61672197),Natural Science Foundation of Heilongjiang Province(F201311,12541105),Chinese Academy of Science Program of Scientific Service Network(KFJ-SW-STS-160),and Chinese Academy of Science Program of Equipment(YZ201502) （81227901,81527805,61231004,81501616,81301346,61672197）
