基于CT征象量化分析的肺结节恶性度分级

陈皓; 段红柏; 郭紫园; 强永乾

doi:10.11999/JEIT200167

基于CT征象量化分析的肺结节恶性度分级

doi: 10.11999/JEIT200167

陈皓^{1, 2, ,},
段红柏¹,
郭紫园¹,
强永乾³

1.
西安邮电大学计算机学院西安 710121
2.
陕西省网络数据分析与智能处理重点实验室西安 710121
3.
西安交通大学第一附属医院西安 710061

基金项目: 国家自然科学基金(61876138, 61203311)，陕西省自然科学基金 (2019JM-365)，陕西省教育厅自然科学专项(17JK0701)，陕西省网络数据分析与智能处理重点实验室开放课题基金(XUPT-KLND(201804))，西安邮电大学创新基金(CXJJLI2018017)

详细信息

作者简介:
陈皓：男，1978年生，博士，副教授，硕士生导师，主要研究方向为医疗大数据

段红柏：男，1993年生，硕士生，研究方向为数据挖掘和模式识别

郭紫园：女，1996年生，硕士生，研究方向为数据挖掘和进化计算

强永乾：男，1965年生，博士，副教授，硕士生导师，研究方向为医学影像学

通讯作者:
陈皓　chenhao@xupt.edu.cn

中图分类号: TN911.73; TP391.41
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出版历程
- 收稿日期: 2020-03-13
- 修回日期: 2020-09-25
- 网络出版日期: 2020-10-16
- 刊出日期: 2021-05-18

Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis

1.
School of Computer, Xi`an University of Posts & Telecommunications, Xi’an 710121, China
2.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Post and Telecommunications, Xi’an 710121, China
3.
First Affiliated Hospital of Xi`an Jiaotong University, Xi’an 710061, China

Funds: The National Natural Science Foundation of China (61876138, 61203311), The Natural Science Basic Research Program of Shaanxi Province (2019JM-365), The Scientific Research Program Funded by Shaanxi Provincial Education Department (17JK0701), The Science Foundation of the Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing (XUPT-KLND (201804)), The Innovation Funds of Xi'an University of Posts and Telecommunications (CXJJLI2018017)

摘要

摘要: 为了提高肺结节恶性度分级的计算精度及可解释性，该文提出一种基于CT征象量化分析的肺结节恶性度分级方法。首先，融合影像组学特征和通过卷积神经网络提取的高阶特征构造分析CT征象所需的特征集; 接着，在混合特征集的基础上利用进化搜索机制优化集成学习分类器，实现对7种肺结节征象的识别和量化打分; 最后，将7种CT征象的量化打分输入到一个利用差分进化算法优化产生的多分类器，实现肺结节恶性度的分级计算。在实验研究中使用LIDC-IDRI数据集中的2000个肺结节样本进行进化集成学习器和恶性度分级器的训练和测试。实验结果显示对7种CT征象的识别准确率可达0.9642以上，肺结节恶性度分级的准确率为0.8618，精确率为0.8678，召回率为0.8617，F1指标为0.8627。与多个典型算法的比较显示，该文方法不但具有较高的准确率，而且可对相关CT征象进行量化分析，使得对恶性度的分级结果更具可解释性。
- 肺恶性度分级 /
- CT征象 /
- 进化集成学习 /
- 量化分析 /
- 可解释性
Abstract: In order to improve the accuracy and interpretability of the grading of malignant nodules in the lung, a method is proposed to achieve grading automatically for lung nodules by using (Computed Tomography, CT) signs. Firstly, features sets are extracted of CT signs by combing the radiomics features with the higher-order features extracted by convolutional neural network. Then, the ensemble classifier is optimized by the evolutionary search mechanism based on the mixed feature sets, and it is used to realize quantitative scores for 7 CT signs. Finally, 7 quantitative scores are input to the optimized multi-classifier to achieve the grading of malignant nodules in the lung. In the experience, 2000 samples of lung nodules in LIDC-IDRI data set are used to train and test the proposed method. The results show that the recognition accuracy of the 7 CT signs can reach more than 0.9642, the grading accuracy reaches 0.8618, the precision reaches 0.8678, the recall reaches 0.8617, and the F1 index reaches 0.8627. With respect to typical algorithms, the proposed method not only has high accuracy, but also can quantitatively analyze the CT signs that make the grade result of malignancy more interpretive.
- Lung malignancy grading /
- Computed Tomography (CT) signs /
- Evolutionary ensemble learning /
- Quantization analysis /
- Interpretability

HTML全文

图 1 肺结节恶性度分级流程

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图 2 多特征融合流程

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图 3 基于进化搜索构造集成学习器的流程

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图 4 进化集成学习器的优化过程

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图 5 不同特征集合的聚类结果可视化对比

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表 1 不同征象的有效特征

特征	阈值	特征总数量	影像组学特征数	CNN特征数
精细度	0.0050	69	28	41
球形度	0.0030	136	23	113
边缘	0.0035	100	28	72
分叶征	0.0009	203	34	169
毛刺征	0.0060	55	23	22
纹理征	0.0070	39	12	27
钙化征	0.0300	68	20	48

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表 2 恶性度分级和CT征象量化的精度

指标	精细度	球形度	边缘	分叶征	毛刺征	纹理征	钙化征	恶性度
ACC	0.9668	0.9764	0.9642	0.9693	0.9792	0.9728	0.9844	0.8618
Pre	0.9648	0.9772	0.9659	0.9522	0.9695	0.9733	0.9683	0.8678
Rec	0.9651	0.9785	0.9346	0.9497	0.9708	0.9736	0.9674	0.8619
F1	0.9649	0.9778	0.9499	0.9509	0.9701	0.9735	0.9678	0.8627

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表 3 恶性度分级模型的权重系数

恶性度等级	精细度	球形度	边缘	分叶征	毛刺征	纹理征	钙化征
1	–0.0703	–0.8560	0.3835	0.2935	0.2751	0.0848	–0.7668
2	–0.0452	0.3054	0.5043	–0.0062	0.0069	–0.6435	–0.4618
3	–0.4187	–0.3507	0.4151	0.3337	0.17592	–0.0367	0.5758
4	0.1984	–0.1948	0.4221	0.0369	0.3217	0.0113	0.2751
5	0.8548	–0.4759	0.2756	–0.2990	0.4641	–0.1176	0.5337

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表 4 不同集成学习器的量化计算结果对比

对比分类器	指标	精细度	球形度	边缘	分叶征	毛刺征	纹理征	钙化征
ET	ACC	0.9638	0.9526	0.9422	0.9603	0.9372	0.9572	0.9512
	Pre	0.9637	0.9558	0.9506	0.9616	0.9372	0.9574	0.9507
	Rec	0.9646	0.9532	0.8994	0.9590	0.9374	0.9572	0.9517
	F1	0.9638	0.9541	0.9197	0.9598	0.9365	0.9573	0.9511
	树个数	112	100	88	108	152	76	76
XGBoost	ACC	0.9621	0.9428	0.9452	0.9560	0.9275	0.9312	0.9498
	Pre	0.9619	0.9442	0.9504	0.9571	0.9276	0.9311	0.9489
	Rec	0.9625	0.9437	0.9266	0.9566	0.9289	0.9311	0.9506
	F1	0.9621	0.9438	0.9377	0.9566	0.9282	0.9311	0.9496
	树个数	188	180	192	188	176	110	80
RF	ACC	0.9585	0.9411	0.9422	0.9491	0.9490	0.9637	0.9471
	Pre	0.9583	0.9449	0.9399	0.9497	0.9492	0.9642	0.9466
	Rec	0.9593	0.9422	0.9166	0.9488	0.9502	0.9637	0.9477
	F1	0.9586	0.9432	0.9270	0.9491	0.9488	0.9639	0.9470
	树个数	128	172	188	136	128	128	72
本文方法	ACC	0.9668	0.9764	0.9642	0.9693	0.9792	0.9728	0.9844
	Pre	0.9648	0.9772	0.9659	0.9522	0.9695	0.9733	0.9683
	Rec	0.9651	0.9785	0.9346	0.9697	0.9708	0.9736	0.9674
	F1	0.9649	0.9778	0.9499	0.9509	0.9701	0.9735	0.9678
	树个数	77	67	78	54	70	60	23

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表 5 相关文献的量化结果对比

文献	精细度	球形度	边缘	分叶征	毛刺征	纹理征	钙化征	恶性度
文献[11]	0.7190	0.5222	0.7250	/	/	0.8340	0.9080	0.8420
文献[22]	0.7163	0.7392	0.7332	0.7248	0.7422	0.7677	0.7390	0.8194
文献[23]	0.7431	0.7622	0.7013	0.8001	0.7828	0.9002	/	0.7556
文献[24]	0.8933	0.8933	0.8933	0.8933	0.8933	0.8933	0.8933	0.8933
本文方法	0.9668	0.9764	0.9642	0.9693	0.9792	0.9728	0.9844	0.8618

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表 6 不同特征集合的聚类结果对比

特征集	均一性	v-measure	互信息
影像组学特征	0.32610	0.3233	0.3179
CNN特征	0.44342	0.4282	0.4118
融合特征	0.60850	0.5934	0.5771

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