Value of CT radiomic features in preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma

doi:10.3969/j.issn.1674-8115.2021.09.015

Abstract

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

CLC Number:

R331

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.

Figures/Tables 7

TrendMD

References 26

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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