CT影像组学特征预测甲状腺乳头状癌颈部淋巴结转移的价值研究

doi:10.3969/j.issn.1674-8115.2021.09.015

摘要/Abstract

摘要：

目的·探讨甲状腺乳头状癌（papillary thyroid carcinoma，PTC）CT增强检查的平扫期、动脉期和静脉期图像的影像组学特征对术前预测颈部淋巴结转移（cervical lymph node metastasis，CLNM）的价值。方法·收集2017年1月—2020年6月在上海市金山区亭林医院行甲状腺外科治疗的197例PTC患者的CT图像，筛选出满足要求的512帧（包括193帧平扫期、131帧动脉期、188帧静脉期），选择图像中显示病灶最大长径的层面进行影像组学研究。根据CLNM的状态，用全部512帧CT图像优选RandomForestClassifier的最佳参数；用具有全部3期CT图像的124例患者资料分别构建3期的随机森林（random forest，RF）分类模型，模型的评价标准为受试者操作特征曲线（receiver operator characteristic curve，ROC curve）的最大平均曲线下面积（area under the curve，AUC）和准确度。结果·RF分类模型显示平扫期、动脉期和静脉期的ROC曲线最大平均AUC分别为0.843、0.775、0.783，相应准确度分别为0.767、0.695、0.726，平扫期的最大平均AUC值明显高于动脉期及静脉期（均P=0.000）。结论·PTC的CT 3期影像组学特征可以较准确地预测CLNM，其中平扫期图像特征预测性能较高。

关键词: 影像组学, CT增强, 甲状腺乳头状癌, 颈部淋巴结, 转移

Abstract:

Objective·To explore the value of radiomic features of pre-contrast phase,arterial phase and venous phase in predicting preoperatively cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) in multi-phase images of contrast-enhanced CT scan.

Methods·CT images of 197 PTC patients who underwent thyroid surgery in Tinglin Hospital of Jinshan District of Shanghai from January 2017 to June 2020 were collected. 512 frames that meet the research requirement, consisting of 193 pre-contrast phases,131 arterial phases and 188 venous phases, were selected from the 3 phases of CT images 197 patients, and the CT images showing the largest length to diameter of PTC lesion were chosen for the radiomic study in each one of the 512 frames. The optimal parameters of RandomForestClassifier were selected with all the 512 frames of CT images and random forest (RF) classification model for the prediction of CLNM was established based on CT images with all the 3 phases of 124 patients who had concurrent CT images of pre-contrast phase, arterial phase and venous phase. The predictive performance of the models was estimated by area under the curve (AUC) of receiver operator characteristic curve (ROC curve) analysis.

Results·The RF classification models showed that the maximal average AUC of ROCs of pre-contrast phase, arterial phase and venous phase were 0.843, 0.775 and 0.783, and the corresponding predictive accuracy were 0.767, 0.695 and 0.726, respectively. Compared with the arterial phase and venous phase, the radiomic features extracted from pre-contrast phase of CT images show better performance to predict CLNM (both P=0.000).

Conclusion·Radiomic features extracted from pre-contrast phase, arterial phase and venous phase of CT images can all feasibly be used to predict CLNM in patients with PTC, and radiomic features from pre-contrast phase of CT images show better performance.

Key words: radiomics, contrast-enhanced CT, papillary thyroid carcinoma (PTC), cervical lymph node, metastasis

中图分类号:

R331

何俊林, 路青, 徐昕, 胡曙东. CT影像组学特征预测甲状腺乳头状癌颈部淋巴结转移的价值研究[J]. 上海交通大学学报(医学版), 2021, 41(9): 1233-1239.

Jun-lin HE, Qing LU, Xin XU, Shu-dong HU. Value of CT radiomic features in preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(9): 1233-1239.

图/表 7

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Item	CLNM group （n=55）	Non-CLNM group （n=69）	t value	P value	Item	CLNM group （n=55）	Non-CLNM group （n=69）	t value	P value
Age/year	52.8±12.8	48.3±13.5	0.139	0.063	Isthmus	3 （5.5）	0 （0）
Age group/n（%）			4.051	0.044	Right lobe	22 （40.0）	38 （55.1）
≥50	37 （67.3）	34 （49.3）			Calcification/n（%）			0.116	0.733
<50	18 （32.7）	35 （50.7）			Negative	28 （50.9）	33 （47.8）
Gender/n（%）			0.304	0.581	Positive	27 （49.1）	36 （52.2）
Male	12 （21.8）	18 （26.1）			Boundary^①/n（%）			1.125	0.289
Female	43 （78.2）	51 （73.9）			Distinct	12 （21.8）	10 （14.5）
Shape/n（%）			1.360	0.713	Indistinct	43 （78.2）	59 （85.5）
Irregular	43 （78.2）	52 （75.4）			Capsule invasion/n（%）			5.731	0.025
Regular	12 （21.8）	17 （24.6）			Negative	4 （7.3）	16 （23.2）
Length to diameter/n（%）			2.285	0.131	Positive	51 （92.7）	53 （76.8）
≤1.0	26 （47.3）	42 （60.9）			ETE/n（%）			8.801	0.004
>1.0	29 （52.7）	27 （39.1）			Negative	27 （49.1）	51 （73.9）
Location/n（%）			5.776	0.056	Positive	28 （50.9）	18 （26.1）
Left lobe	30 （54.5）	31 （44.9）

Item	CLNM group （n=55）	Non-CLNM group （n=69）	t value	P value	Item	CLNM group （n=55）	Non-CLNM group （n=69）	t value	P value
Age/year	52.8±12.8	48.3±13.5	0.139	0.063	Isthmus	3 （5.5）	0 （0）
Age group/n（%）			4.051	0.044	Right lobe	22 （40.0）	38 （55.1）
≥50	37 （67.3）	34 （49.3）			Calcification/n（%）			0.116	0.733
<50	18 （32.7）	35 （50.7）			Negative	28 （50.9）	33 （47.8）
Gender/n（%）			0.304	0.581	Positive	27 （49.1）	36 （52.2）
Male	12 （21.8）	18 （26.1）			Boundary^①/n（%）			1.125	0.289
Female	43 （78.2）	51 （73.9）			Distinct	12 （21.8）	10 （14.5）
Shape/n（%）			1.360	0.713	Indistinct	43 （78.2）	59 （85.5）
Irregular	43 （78.2）	52 （75.4）			Capsule invasion/n（%）			5.731	0.025
Regular	12 （21.8）	17 （24.6）			Negative	4 （7.3）	16 （23.2）
Length to diameter/n（%）			2.285	0.131	Positive	51 （92.7）	53 （76.8）
≤1.0	26 （47.3）	42 （60.9）			ETE/n（%）			8.801	0.004
>1.0	29 （52.7）	27 （39.1）			Negative	27 （49.1）	51 （73.9）
Location/n（%）			5.776	0.056	Positive	28 （50.9）	18 （26.1）
Left lobe	30 （54.5）	31 （44.9）

Iteration	Training set	Validation set	Pre-contrast phase ［top（k）=56］		Arterial phase ［top（k）=94］		Venous phase ［top（k）=47］
Iteration	Training set	Validation set	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC
1	111	13	0.846	0.810	0.615	0.690	0.846	0.857
2	111	13	0.846	0.833	0.692	0.800	0.692	0.817
3	111	13	0.538	0.762	0.615	0.476	0.615	0.595
4	111	13	0.769	0.925	0.692	0.800	0.692	0.950
5	111	13	0.917	0.972	0.750	0.861	1.000	1.000
6	111	13	0.583	0.812	0.583	0.969	0.500	0.469
7	111	13	0.833	0.815	0.833	0.889	0.750	0.852
8	111	13	0.750	0.829	0.667	0.714	0.750	0.771
9	111	13	0.833	0.914	0.750	0.771	0.667	0.743
10	111	13	0.750	0.704	0.750	0.778	0.750	0.778
Average AUC	‒	‒	0.767	0.843	0.695	0.775	0.726	0.783

Iteration	Training set	Validation set	Pre-contrast phase ［top（k）=56］		Arterial phase ［top（k）=94］		Venous phase ［top（k）=47］
Iteration	Training set	Validation set	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC
1	111	13	0.846	0.810	0.615	0.690	0.846	0.857
2	111	13	0.846	0.833	0.692	0.800	0.692	0.817
3	111	13	0.538	0.762	0.615	0.476	0.615	0.595
4	111	13	0.769	0.925	0.692	0.800	0.692	0.950
5	111	13	0.917	0.972	0.750	0.861	1.000	1.000
6	111	13	0.583	0.812	0.583	0.969	0.500	0.469
7	111	13	0.833	0.815	0.833	0.889	0.750	0.852
8	111	13	0.750	0.829	0.667	0.714	0.750	0.771
9	111	13	0.833	0.914	0.750	0.771	0.667	0.743
10	111	13	0.750	0.704	0.750	0.778	0.750	0.778
Average AUC	‒	‒	0.767	0.843	0.695	0.775	0.726	0.783

Order	Pre-contrast phase	Arterial phase	Venous phase
1	Original_glcm_DifferenceAverage	Original_glcm_DifferenceVariance	Original_glcm_DifferenceVariance
2	Original_firstorder_Variance	Original_glcm_ClusterProminence	Original_glcm_Contrast
3	Original_glrlm_RunPercentage	Original_glcm_Contrast	Original_glrlm_ShortRunEmphasis
4	Original_glszm_GrayLevelNonUniformityNormalized	Original_firstorder_Kurtosis	Original_glszm_SmallAreaLowGrayLevelEmphasis
5	Original_glcm_SumSquares	Original_glcm_ClusterShade	Original_glcm_ClusterProminence
6	Original_glcm_JointEntropy	Original_shape_Maximum3DDiameter	Original_glrlm_RunVariance
7	Original_glcm_SumAverage	Original_glrlm_GrayLevelVariance	Original_firstorder_Skewness
8	Original_glcm_Contrast	Original_gldm_DependenceVariance	Original_glcm_MaximumProbability
9	Original_ngtdm_Strength	Original_glcm_SumSquares	Original_glcm_DifferenceAverage
10	Original_gldm_DependenceNonUniformityNormalized	Original_glcm_DifferenceEntropy	Original_glcm_ClusterShade