Chinese Journal of Magnetic Resonance ›› 2023, Vol. 40 ›› Issue (1): 52-67.doi: 10.11938/cjmr20222971
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HAN Bing1,XU Jing2,WANG Yuanjun1,*(
),WANG Zhongling2,#()
Received:2022-01-18
Published:2023-03-05
Online:2022-04-04
Contact:
WANG Yuanjun,WANG Zhongling
E-mail:yjusst@126.com;zlwang138136@126.com
CLC Number:
HAN Bing,XU Jing,WANG Yuanjun,WANG Zhongling. Classification of BI-RADS 3-5 Breast Lesions Based on MRI Radiomics[J]. Chinese Journal of Magnetic Resonance, 2023, 40(1): 52-67.
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Fig. 1
Experimental workflow of this research
Fig. 2
Region of interest (ROI) segmentation of DCE magnetic resonance images. (a) Schematic diagram of ROI delineation at the largest layer of the lesion; (b) Schematic diagram of the lesion delineation
Table 1
Radiomics features of breast magnetic resonance images
| 特征类别 | 特征描述 | 特征数 | 特征输入 |
|---|---|---|---|
| shape | 表面积、伸长率、平面度、球形度、最大二维直径等 | 14 | 原始图像 |
| firstorder | 能量、熵、峰值、最大值、平均值、方差、第10百分位数、第90百分位数等 | 342 | 原始图像、派生图像 |
| GLCM | 自相关、联合平均、聚类突出度、聚类阴影、对比、相关、差熵等 | 418 | 原始图像、派生图像 |
| GLRLM | 长游程强调、短游程强调、灰度不均匀性、游程百分比、游程方差、游程熵等 | 304 | 原始图像、派生图像 |
| GLDM | 小依赖度、大依赖度、依赖不均匀性、依赖方差、依赖熵等 | 266 | 原始图像、派生图像 |
| GLSZM | 小区域强调、大区域强调、灰度不均匀性、区域百分比、区域方差、区域熵等 | 304 | 原始图像、派生图像 |
| NGTDM | 粗糙度、对比度、繁忙度、复杂度、强度 | 95 | 原始图像、派生图像 |
Table 2
The optimal radiomics features and corresponding coefficients of T1W images screened by LASSO algorithm
| 影像组学特征 | 系数 | 特征详情 |
|---|---|---|
| 联合熵（original_glcm_JointEntropy） | 0.010878 | 度量邻域强度值的可变性 |
| 平均值（log-sigma-5-0-mm-3D_firstorder_Mean） | 0.120571 | 描述肿瘤区域的平均灰度值 |
| 小区域低灰度级强调 （wavelet-HLH_glszm_SmallAreaLowGrayLevelEmphasis） | -0.110678 | 描述低灰度小尺寸区域体素的分布 |
| 归一化相关不均匀性 （wavelet-HHL_gldm_DependenceNonUniformityNormalized） | -0.089366 | 描述GLDM中体素相关关系的相似程度 |
| 依赖方差（wavelet-HHL_gldm_DependenceVariance） | 0.007990 | 描述GLDM中依赖大小的方差 |
| 相关性信息测度2（wavelet-HHH_glcm_Imc2） | -0.045300 | 量化纹理的复杂性 |
| 游程方差（wavelet-HHH_glrlm_RunVariance） | -0.059722 | 度量游程长度的方差 |
| 依赖熵（gradient_gldm_DependenceEntropy） | 0.019160 | 度量GLDM中依赖大小与灰度级分布的随机性程度 |
| 小依赖性低灰度级强调（gradient_gldm_SmallDependenceLowGrayLevelEmphasis） | -0.021736 | 描述体素小相关性与低阶灰度值的联合分布情况 |
| 相关性信息测度1（squareroot_glcm_Imc1） | 0.030811 | 量化纹理的复杂性 |
| 强度（exponential_ngtdm_Strength） | -0.055385 | 度量肿瘤图像的灰度变化程度 |
Table 3
The optimal radiomics features and corresponding coefficients of DCE images screened by LASSO algorithm
| 影像组学特征 | 系数 | 特征详情 |
|---|---|---|
| 原始图像最小值（original_firstorder_Minimum） | -0.013973 | 描述肿瘤区域的最小灰度值 |
| 大依赖高灰度级强调（original_gldm_LargeDependenceHighGrayLevelEmphasis） | 0.039997 | 测量具有较高灰度值和体素强相关关系的联合分布情况 |
| 偏度（wavelet-LHH_firstorder_Skewness） | 0.001540 | 度量强度平均值的分布不对称性 |
| 相关性信息测度1（wavelet-LHH_glcm_Imc1） | 0.057154 | 量化纹理的复杂性 |
| 小波变换LLL方向联合熵 （wavelet-LLL_glcm_JointEntropy） | 0.197864 | 度量邻域强度值的可变性 |
| 归一化逆差矩（gradient_glcm_Idmn） | 0.034171 | 度量肿瘤图像的局部平均程度 |
| 平方变换最小值（square_firstorder_Minimum） | 0.037978 | 描述肿瘤区域的最小灰度值 |
| 平方变换联合熵（square_glcm_JointEntropy） | 0.064132 | 度量邻域强度值的可变性 |
| 大区域低灰度级强调 （squareroot_glszm_LargeAreaLowGrayLevelEmphasis） | -0.014832 | 测量图像中具有较低灰度值的较大尺寸区域体素的分布 |
| 低灰度区域强调 （logarithm_glszm_LowGrayLevelZoneEmphasis） | -0.055083 | 描述低灰度级区域体素的分布 |
| 低灰度强调（logarithm_gldm_LowGrayLevelEmphasis） | -0.084834 | 描述低阶灰度体素的分布情况 |
Table 4
The optimal fusion features and corresponding coefficients screened by LASSO algorithm
| 影像组学特征 | 系数 | 特征详情 |
|---|---|---|
| 最大2D直径（列） （original_shape_Maximum2DDiameterColumn） | 0.081848 | 冠状平面中肿瘤表面网格顶点之间最大的欧几里得距离 |
| 相关性信息测度1（wavelet-LHL_glcm_Imc1） | 0.018552 | 量化纹理的复杂性 |
| 集群阴影（wavelet-LHH_glcm_ClusterShade） | -0.022909 | 描述图像的褶皱程度 |
| 小波变换HLL方向粗糙度 （wavelet-HLL_ngtdm_Coarseness） | -0.051320 | 度量中心体素与其邻域体素之间平均差异 |
| 小区域低灰度级强调 （wavelet-HLH_glszm_SmallAreaLowGrayLevelEmphasis） | -0.031217 | 描述图像中具有较低灰度值的较小尺寸区域体素分布 |
| 相关性信息测度2（wavelet-HHL_glcm_Imc2） | -0.066184 | 量化纹理的复杂性 |
| 逆方差（wavelet-HHH_glcm_InverseVariance） | 0.010646 | 度量肿瘤图像局部均匀程度 |
| 归一化灰度不均匀度 （wavelet-LLL_glszm_GrayLevelNonUniformityNormalized） | -0.138090 | 测量图像中灰度强度值的可变性 |
| 小波变换LLL方向粗糙度 （wavelet-LLL_ngtdm_Coarseness） | -0.086469 | 度量中心体素与其邻域体素之间平均差异 |
| 归一化区域大小不均匀度 （square_glszm_SizeZoneNonUniformityNormalized） | -0.022937 | 测量整个图像中大小区域体积的可变性 |
| 最小值（exponential_firstorder_Minimum） | -0.038634 | 描述肿瘤区域的最小灰度值 |
| 对比度（logarithm_ngtdm_Contrast） | -0.019143 | 度量体素灰度的空间变化率 |
Fig. 3
The LASSO algorithm is used to screen the optimal T1W features, DCE features and fusion features, respectively. (a) The optimal λ is determined by adjusting λ to minimize the mean square error (MSE) of the model; (b) Convergence diagram of optimal characteristic coefficients
Fig. 4
ROC curves of four models based on different features (FPR: False positive rate, TPR: True positive rate)
Table 5
Diagnostic efficacy of different models based on various features for classification of BI-RADS 3-5 breast lesions
| 特征 | 模型 | 准确率 | Kappa系数 | 海明损失 | Micro AUC | Macro AUC |
|---|---|---|---|---|---|---|
| 平扫特征 | SVM | 64.86% | 0.419 | 0.297 | 0.848 | 0.820 |
| RF | 70.27% | 0.421 | 0.333 | 0.878 | 0.846 | |
| KNN | 70.27% | 0.421 | 0.333 | 0.830 | 0.769 | |
| LR | 71.43% | 0.421 | 0.333 | 0.858 | 0.882 | |
| 增强特征 | SVM | 70.27% | 0.428 | 0.297 | 0.895 | 0.847 |
| RF | 86.49% | 0.428 | 0.297 | 0.928 | 0.862 | |
| KNN | 76.19% | 0.445 | 0.297 | 0.875 | 0.828 | |
| LR | 78.57% | 0.428 | 0.297 | 0.948 | 0.929 | |
| 融合特征 | SVM | 81.25% | 0.606 | 0.224 | 0.881 | 0.855 |
| RF | 87.50% | 0.676 | 0.188 | 0.951 | 0.988 | |
| KNN | 78.38% | 0.606 | 0.224 | 0.886 | 0.867 | |
| LR | 81.25% | 0.606 | 0.224 | 0.961 | 0.989 |
Table 6
The optimal radiomics features and corresponding coefficients of T1W images screened by LASSO algorithm
| 影像组学特征 | 系数 | 特征详情 |
|---|---|---|
| 联合能量（original_glcm_JointEnergy） | -0.074162 | 度量图像纹理中相邻灰度变换稳定程度 |
| 最大概率（original_glcm_MaximumProbability） | -0.049634 | 描述图像中出现次数最多的纹理特征 |
| 大依赖低灰度级强调（log-sigma-2-0-mm-3D_gldm_LargeDependenceLowGrayLevelEmphasis） | -0.029816 | 描述体素强相关关系与低阶灰度的联合分布情况 |
| 大区域低灰度级强调 （wavelet-LLH_glszm_LargeAreaLowGrayLevelEmphasis） | 0.001242 | 描述低灰度大尺寸区域体素的分布 |
| 小依赖低灰度级强调 （wavelet-HHL_gldm_SmallDependenceLowGrayLevelEmphasis） | -0.008748 | 描述体素小相关关系与低阶灰度的联合分布情况 |
| 小波变换HHL方向归一化区域大小不均匀度 （wavelet-HHL_glszm_SizeZoneNonUniformityNormalized） | -0.020799 | 测量整个图像中大小区域体积的可变性 |
| 强度（wavelet-HHL_ngtdm_Strength） | -0.083993 | 度量肿瘤图像的灰度变化程度 |
| 短游程低灰度级强调（square_glrlm_ShortRunLowGrayLevelEmphasis） | -0.088759 | 度量低阶灰度值与短游程长度的联合分布情况 |
| 小区域低灰度级强调（exponential_glszm_SmallAreaLowGrayLevelEmphasis） | 0.037705 | 描述低灰度小尺寸区域体素的分布 |
| 对数变换归一化区域大小不均匀度（logarithm_glszm_SizeZoneNonUniformityNormalized） | -0.004205 | 测量整个图像中大小区域体积的可变性 |
| 小依赖强调（logarithm_gldm_SmallDependenceEmphasis） | -0.011085 | 描述与领域内体素相关性较小的体素分布情况 |
Table 7
The optimum radiomics features and corresponding coefficients of DCE images screened by LASSO algorithm
| 影像组学特征 | 系数 | 特征详情 |
|---|---|---|
| 长轴长度（original_shape_MajorAxisLength） | 0.053568 | 度量肿瘤最长轴的长度 |
| 小区域强调（wavelet-LLH_glszm_SmallAreaEmphasis） | 0.007506 | 度量小尺寸区域的分布情况 |
| 大依赖低灰度级强调 （wavelet-LLH_gldm_LargeDependenceLowGrayLevelEmphasis） | -0.022168 | 描述体素强相关关系与低阶灰度的联合分布情况 |
| 强度（wavelet-LHL_ngtdm_Strength） | -0.002489 | 度量肿瘤图像的灰度变化程度 |
| 峰度（wavelet-LHH_firstorder_Kurtosis） | -0.038882 | 度量图像ROI中值分布的峰值 |
| 小依赖低灰度级强调 （wavelet-HLL_gldm_SmallDependenceLowGrayLevelEmphasis） | -0.003671 | 描述体素小相关关系与低阶灰度的联合分布情况 |
| 归一化灰度不均匀度 （wavelet-HLH_glszm_GrayLevelNonUniformityNormalized） | -0.117303 | 测量图像中灰度强度值的可变性 |
| 梯度变换联合熵（gradient_glcm_JointEntropy） | 0.001880 | 度量邻域强度值的可变性 |
| 平方变换联合熵（square_glcm_JointEntropy） | 0.058258 | 度量邻域强度值的可变性 |
| 强度（square_ngtdm_Strength） | -0.046019 | 度量肿瘤图像的灰度变化程度 |
| 归一化依赖不均匀性（exponential_gldm_DependenceNonUniformityNormalized） | -0.071656 | 测量整个图像中体素相关关系的相似程度 |
Table 8
The optimal fusion features and corresponding coefficients screened by LASSO algorithm
| 影像组学特征 | 系数 | 特征详情 |
|---|---|---|
| 长轴长度（original_shape_MajorAxisLength） | 0.050888 | 度量肿瘤最长轴的长度 |
| 联合能量（original_glcm_JointEnergy） | -0.016239 | 度量图像纹理中相邻灰度变换稳定程度 |
| 最大概率（original_glcm_MaximumProbability） | -0.071362 | 描述图像中出现次数最多的纹理特征 |
| 小波变换LLH方向-小区域强调 （wavelet-LLH_glszm_SmallAreaEmphasis） | 0.009840 | 度量小尺寸区域的分布情况 |
| 大依赖低灰度级强调 （wavelet-LLH_gldm_LargeDependenceLowGrayLevelEmphasis） | -0.014709 | 描述体素强相关关系与低阶灰度的联合分布情况 |
| 低灰度区域强调 （wavelet-LHH_glszm_LowGrayLevelZoneEmphasis） | -0.002699 | 描述低灰度级区域体素的分布 |
| 区域熵（wavelet-LHH_glszm_ZoneEntropy） | 0.090181 | 度量灰度区域大小与灰度级分布的不稳定性 |
| 相关性信息测度2（wavelet-HHL_glcm_Imc2） | -0.086920 | 量化纹理的复杂性 |
| 小波变换HHL方向-小区域强调 （wavelet-HHL_glszm_SmallAreaEmphasis） | -0.051711 | 度量小区域尺寸的分布情况 |
| 小依赖低灰度级强调 （wavelet-HHL_gldm_SmallDependenceLowGrayLevelEmphasis） | -0.012360 | 描述体素小相关关系与低阶灰度的联合分布情况 |
| 中位数（wavelet-HHH_firstorder_Median） | -0.023237 | 描述肿瘤区域的灰度中位数 |
| 逆方差（wavelet-HHH_glcm_InverseVariance） | 0.013141 | 度量肿瘤图像局部均匀程度 |
| 粗糙度（wavelet-LLL_ngtdm_Coarseness） | -0.014267 | 度量中心体素与其邻域体素灰度值的平均差异 |
| 短游程低灰度级强调（square_glrlm_ShortRunLowGrayLevelEmphasis） | -0.048257 | 度量低阶灰度值与短游程长度的联合分布情况 |
| 强度（square_ngtdm_Strength） | -0.002079 | 度量肿瘤图像的灰度变化程度 |
| 第10%分位值（squareroot_firstorder_10Percentile） | -0.003926 | 指肿瘤区域10%分位数的灰度值 |
| 小区域低灰度级强调（exponential_glszm_SmallAreaLowGrayLevelEmphasis） | 0.003075 | 描述低灰度小尺寸区域体素的分布 |
| 小依赖强调（logarithm_gldm_SmallDependenceEmphasis） | -0.002973 | 描述与领域内体素相关性较小的体素分布情况 |
Fig. 5
The LASSO algorithm is used to screen the optimal T1W features, DCE features and fusion features, respectively. (a) The optimal λ is determined by adjusting λ to minimize the mean square error (MSE) of the model; (b) Convergence diagram of optimal characteristic coefficients
Fig. 6
ROC curves of four models based on different features
Table 9
Diagnostic efficacy of different models based on various features for benign and malignant breast lesions
| 特征 | 模型 | AUC | 准确率 | 灵敏度 | 特异度 |
|---|---|---|---|---|---|
| 平扫特征 | SVM | 0.889 | 80.65% | 66.67% | 86.67% |
| RF | 0.934 | 83.87% | 66.67% | 90.91% | |
| KNN | 0.838 | 80.65% | 77.78% | 81.82% | |
| LR | 0.955 | 83.87% | 77.78% | 86.36% | |
| 增强特征 | SVM | 0.927 | 85.45% | 92.31% | 83.33% |
| RF | 0.960 | 89.09% | 84.62% | 90.48% | |
| KNN | 0.958 | 85.45% | 84.62% | 85.71% | |
| LR | 0.965 | 89.09% | 84.62% | 90.48% | |
| 融合特征 | SVM | 0.973 | 90.91% | 78.57% | 95.19% |
| RF | 0.958 | 93.55% | 90.00% | 95.24% | |
| KNN | 0.971 | 92.73% | 92.86% | 92.68% | |
| LR | 0.974 | 94.55% | 85.71% | 97.56% |
Table 10
p values of Delong test results based on diagnostic efficiency of different models
| AUC | RF与SVM | RF与KNN | RF与LR | LR与SVM | LR与KNN | SVM与KNN | |
|---|---|---|---|---|---|---|---|
| BI-RADS分类 | Micro AUC | 0.041 | 0.058 | 0.134 | 0.069 | 0.071 | 0.317 |
| Macro AUC | 0.037 | 0.039 | 0.481 | 0.037 | 0.039 | 0.093 | |
| 良恶性分类 | AUC | 0.052 | 0.067 | 0.034 | 0.067 | 0.078 | 0.078 |
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