Application of Radiomics Based on New Support Vector Machine in the Classification of Hepatic Nodules

doi:10.11938/cjmr20212916

Abstract

Abstract:

Liver cancer is one of the most common malignant tumors. In Asia, liver cancer often develops on a background of cirrhosis caused by chronic hepatitis. The procedure of hepatitis, cirrhotic nodules, dysplastic nodules, and then hepatocellular carcinoma is the most common liver cancer evolutionary process. Judging the stage of hepatic nodules in the evolution process and taking intervention measures are critical for reducing the incidence of liver cancer. In this paper, a more accurate support vector machine (SVM) classification algorithm, LFOA-F-SVM, was proposed for radiomics to classify hepatic nodules from 120 patients into four categories based on dynamic enhanced magnetic resonance images. The algorithm uses radius-margin-based F-SVM, and combines the fruit fly optimization algorithm (FOA) of Levy flight (LF) strategy to optimize the parameters. To verify the effectiveness of the method, five UCI classification data sets (hearts, Parkinson’s disease, iris, wine and zoo) were added and compared with SVM, PSO-SVM, FOA-SVM, F-SVM. The results showed that LFOA-F-SVM has the highest classification accuracy in six data sets compared to the other methods. And in the hepatic nodules data set, the classification precision and recall are relatively high.

Key words: hepatic nodules, classification, radiomics, LFOA-F-SVM

CLC Number:

O482.53

Di LI, Lei HUO, Meng-yun WAN, Ning-yang JIA, Li-jia WANG. Application of Radiomics Based on New Support Vector Machine in the Classification of Hepatic Nodules[J]. Chinese Journal of Magnetic Resonance, 2022, 39(3): 278-290.

Figures/Tables 10

Fig.1

Table 1

Table 2

Extracted radiomics features from hepatic nodules

特征种类（个数）	特征
一阶统计特征（18）	能量、总能量、熵、最小值、像素10%、像素90%、最大值、均值、中位数、四分位间距、像素范围、平均绝对偏差、鲁棒平均绝对偏差、均方根、偏度、峰度、方差、均匀性
形状特征（14）	网格体积、像素体积、表面积、表面积体积比、球形度、最大3D直径、最大2D直径、最大2D直径列、最大2D直径行、长轴、短轴、最小轴、伸长率、平坦度
灰度共生矩阵（GLCM，24）	自相关、聚类突出度、聚类阴影、聚类趋势、对比度、相关性、差异均值、差异熵、差异方差、相关均值、相关能量、相关熵、相关性的非正式度量1、相关性的非正式度量2、逆差分矩、归一化逆差分矩、逆差分、归一化逆差分、逆方差、最大相关系数、最大概率、求和平均值、求和熵、求和方差
灰度游程长度矩阵（GLRLM，16）	灰度不均匀性、归一化灰度不均匀性、灰度方差、强调高灰度运行、强调长期游程、强调长期游程高灰度级别、强调长期游程低灰度级别、强调低灰度游程、强调短期游程、强调短期高灰度级、强调短期低灰度级、游程熵、游程长度非均匀性、标准化游程长度非均匀性、游程百分比、游程方差
灰度大小区域矩阵（GLSZM，16）	强调小区域、强调大区域、灰度非均匀性、归一化灰度非均匀性、尺寸区域非均匀性、归一化尺寸区域非均匀性、区域百分比、灰度方差、区域方差、区域熵、强调低灰度级区域、强调高灰度级、强调小区域低灰度、强调小区域高灰度、强调大区域低灰度、强调大区域高灰度
邻域灰度差分矩阵（NGTDM，5）	繁忙度、复杂度、对比度、粗糙度、强度
灰度依赖矩阵（GLDM，14）	依赖熵、依赖不均匀性、归一化依赖不均匀性、依赖方差、灰度不均匀性、灰度方差、强调大依赖性、强调高灰度级、强调大依赖性高灰度级、强调低灰度级、强调大依赖性低灰度级、强调小依赖性、强调小依赖性高灰度级、强调小依赖性低灰度级
高阶统计特征（744）	小波（LHL、LHH、HLL、LLH、HLH、HHH、HHL、LLL）变换提取特征，指在三个维度中的每一个维度应用高通（H）或低通（L）滤波器的组合变换后，进而提取的一阶统计和纹理特征

Table 2

Fig.2

Fig.3

Fig.4

Table 3

Fig.5

Fig.6

Table 4

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数据集	样本量	类别数	特征数	最高的分类准确率/%	分类器	文献来源^*
心脏	303	2	13	89.35	F-SVM	Wu (2018)^[11]
帕金森疾病	195	2	22	91.98	FOA-F-SVM	Gu (2021)^[23]
虹膜	150	3	4	98.57	SSLSH	Zhang (2014)^[24]
葡萄酒	178	3	13	99.44	FOA-F-SVM	Gu (2021)^[23]
动物园	101	7	16	98.32	F-SVM	Wu (2018)^[11]

数据集	方法
数据集	SVM	PSO-SVM	FOA-SVM	F-SVM	LFOA-F-SVM
心脏	86.77%(0.004 s)	87.74%(0.0035 s)	87.41%(0.0047 s)	88.71%(4.0868 s)	89.35%(6.9537 s)
帕金森疾病	93.27%(0.0021 s)	92.74%(0.0022 s)	93.79%(0.0019 s)	93.68%(8.5848 s)	96.32%(5.215 s)
虹膜	96.00%(0.0006 s)	96.67%(0.0006 s)	96.67%(0.0005 s)	98.00%(0.035 s)	98.67%(0.0237 s)
葡萄酒	96.67%(0.0013 s)	97.78%(0.0011 s)	98.33%(0.0019 s)	98.89%(0.0045 s)	99.44%(0.0057 s)
动物园	93.64%(0.0015 s)	91.82%(0.0012 s)	96.36%(0.0015 s)	97.27%(0.0395 s)	99.09%(0.0092 s)
肝脏结节	74.72%(0.0097 s)	76.11%(0.0092 s)	76.67%(0.0196 s)	76.94%(21.5069 s)	81.00%(14.8110 s)

		SVM	PSO-SVM	FOA-SVM	F-SVM	LFOA-F-SVM
F1	Precision	0.72	0.79	0.82	0.69	0.81
	Recall	0.67	0.70	0.67	0.74	0.78
F2	Precision	0.63	0.72	0.71	0.68	0.77
	Recall	0.59	0.78	0.89	0.70	0.85
F3	Precision	0.80	0.87	0.87	0.78	0.88
	Recall	0.74	0.74	0.74	0.67	0.81
F4	Precision	0.86	0.86	0.83	0.79	0.89
	Recall	0.93	0.93	0.89	0.81	0.89

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