The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study

Hui Zhu; Hongxia Luo; Yanyan Li; Yuhua Zhang; Zhijing Wu; Yan Yang

doi:10.2478/raon-2024-0040

Figures & Tables

The flowchart for this retrospective study.AUC = area under the curve; ETE = extrathyroidal extension; PTC = papillary thyroid carcinoma; US = ultrasonic; xgboost = the extreme gradient boosting

The ROC curves of sonographic method and US radiomics method for ETE predicting. (A) The ROC curve of sonographic method in all nodules. (B) The ROC curve of US radiomics method in validation group.ETE = extrathyroidal extension; ROC = the receiver operating characteristic; US = ultrasonic

The image of feature importance for xgboost model. (A-D) the top 1–20, top 21–40, top 41–60 and top 61–80 feature importance of xgboost model.xgboost=the extreme gradient boosting; GLSZM=Gray-level size-zone matrix; GLCM=gray-level co-occurrence matrix; GLDM=gray-level dependence matrix; GLRLM=gray-level runlength matrix; LH=low-pass/high-pass; LL=low-pass/low-pass; HH=high-pass/high-pass; HL=high-pass/low-pass; ANTITPOAB=anti-thyroid peroxidase antibody; AST=asparate aminotransferase; RBC=red blood cell; FT3=free triiodothyronine 3; Ca=calcium ion; TT4=total triiodothyronine 4; TG=thyroglobulin; TSH=thyroid stimulating hormone; NEUT=neutrophil; PLT=platelets.

Figure 4.

Partial dependence profile of the top-6 important features in xgboost model. (A-F) Partial dependence profile of logsigma6.0mm3D-GLCM-MCC, logsigma5.0mm3D-GLCM-Imc1, square-firstorder-RootMeanSquared, waveletLL-GLSZM-SmallAreaLowGrayLevelEmphasis, logarithm-GLSZM-SmallAreaHighGrayLevelEmphasis and logsigma6.0mm3D-firstorder-90Percentile in xgboost model in training group. xgboost, the extreme gradient boosting; GLSZM, Gray-level size-zone matrix; GLCM, gray-level co-occurrence matrix; LL, low-pass/low-pass.

Comparison of predictive performance for models and sonographic method for extrathyroidal extension predicting

Model	Accuracy (95% CI)	Sensitivity	Specificity	PPV	NPV	AUC	p-value
xgboost	0.77(0.6751–0.8483)	0.6774	0.8116	0.6176	0.8485	0.813	-
RF	0.73(0.6320–0.8139)	0.3548	0.8986	0.6111	0.7561	0.741	0.000006
GBM	0.75(0.6534–0.8312)	0.5164	0.8551	0.6154	0.7973	0.737	0.000012
binary LR	0.74(0.6427–0.8226)	0.6774	0.7681	0.5676	0.8413	0.730	0.000237
NB	0.55(0.4473–0.6497)	0.9355	0.3768	0.4028	0.9286	0.656	0.000000
DT	0.68(0.5792–0.7698)	0.3871	0.8116	0.4800	0.7467	0.634	0.000000
adaboost	0.71(0.6107–0.7964)	0.3548	0.8696	0.5500	0.7500	0.612	0.000000
SVM	0.70(0.6002–0.7876)	0.2903	0.8841	0.5294	0.7349	0.567	0.000000
KNN	0.69(0.5897–0.7787)	0.1935	0.9130	0.5000	0.7159	0.553	< 2.2x10^-16
Sonographic method	0.70(0.6514–0.7515)	0.5349	0.7279	0.2233	0.9145	0.569	< 2.2x10^-16

Characteristics of nodules in training and validation groups

Characteristics	Training group (n = 237)	Validation group (n = 100)	p-value
Sex			0.708
Male	64 (27.00)	29 (29.00)
Female	173 (73.00)	71 (71.00)
Age (years)^a	45.97 ± 11.92	46.70 ± 11.64	0.606
Size (mm)	9.69 ± 6.08	10.62 ± 7.60	0.550
WBC (×10^9/L)	5.96 ± 1.52	6.12 ± 1.45	0.241
NEUT (×10^9/L)	3.65 ± 1.26	3.75 ± 1.18	0.326
LYM (×10^9/L)	1.89 ± 0.56	1.91 ± 0.62	0.716
HB (g/L)	138.39 ± 16.01	141.70 ± 15.13	0.094
RBC (×10^12/L)	4.69 ± 0.46	4.70 ± 0.45	0.752
PLT (×10^9/L)	252.85 ± 58.65	255.70 ± 64.90	0.656
ALT (U/L)	23.50 ± 19.98	24.15 ± 18.41	0.280
AST (U/L)	21.53 ± 7.86	21.48 ± 7.68	0.696
ALB (g/L)	45.15 ± 3.19	44.93 ± 3.01	0.473
BUN (mmol/L)	4.83 ± 1.27	4.93 ± 1.29	0.391
CREA (umol/L)	58.14 ± 13.36	57.54 ± 12.82	0.489
UA (umol/L)	315.27 ± 82.01	325.80 ± 86.12	0.261
Ca (mmol/L)	2.40 ± 0.11	2.41 ± 0.10	0.460
TT3 (ng/ml)	1.10 ± 0.31	1.09 ± 0.18	0.828
TT4 (μg/dl)	8.32 ± 1.72	8.09 ± 1.62	0.329
FT3 (pg/ml)	3.36 ± 1.16	3.33 ± 0.41	0.346
FT4 (ng/dl)	1.30 ± 0.26	1.29 ± 0.20	0.829
TSH (μIU/ml)	1.66 ± 0.99	1.96 ± 1.38	0.090
ANTITGAB (IU/ml)	122.12 ± 325.06	114.13 ± 349.18	0.335
ANTITPOAB (IU/ml)	41.11 ± 101.64	52.08 ± 124.91	0.912
TG (ng/ml)	36.10 ± 72.10	42.00 ± 84.61	0.886
Urinary leukocyte^b			0.412
Negative	188 (79.32)	87 (87.00)
Positive 1+	19 (8.02)	5 (5.00)
Positive 2+	17 (7.17)	3 (3.00)
Positive 3+	10 (4.22)	3 (3.00)
Positive 4+	10 (4.22)	2 (2.00)
URBC^b			0.144
Negative	208 (87.76)	89 (89.00)
Positive 1+	21 (8.86)	6 (6.00)
Positive 2+	6 (2.53)	1 (1.00)
Positive 3+	0 (0)	2 (2.00)
Positive 4+	2 (0.84)	2 (2.00)
Urinary protein^b			0.524
Negative	145 (61.18)	64 (64.00)
Positive 1+	63 (26.58)	28 (28.00)
Positive 2+	29 (12.24)	8 (8.00)
Composition^b			1.000
Predominately cystic	2 (0.84)	0 (0)
Predominately solid	235 (99.16)	100 (100.00)
Solid	0 (0)	0 (0)
Echogenicity^b			0.966
Hyperechoic or isoechoic	8 (3.38)	3 (3.00)
Hypoechoic	191 (80.59)	82 (82.00)
Markedly hypoechoic	38 (16.03)	15 (15.00)
Shape^b			0.974
Wider-than-tall	100 (42.19)	42 (42.00)
Taller-than-wide	137 (57.81)	58 (58.00)
Margin^b			0.083
Smooth or ill-defined	143 (60.34)	54 (54.00)
Lobulated or irregular	70 (29.54)	27 (27.00)
Extrathyroidal extension	24 (10.13)	19 (19.00)
Echogenic foci^b			0.465
No calcification	68 (28.69)	20 (20.00)
Macrocalcifications	63 (26.58)	23 (23.00)
Peripheral calcifications	6 (2.53)	2 (2.00)
Microcalcifications	147 (62.03)	72 (72.00)
TI-RADS classification^b			1.000
III	1 (0.42)	0 (0)
IV	19 (8.02)	8 (8.00)
V	216 (91.14)	92 (92.00)
ETE			0.910
Negative	165 (69.62)	69 (69.00)
Positive	72 (30.38)	31 (31.00)

The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Comparison of predictive performance for models and sonographic method for extrathyroidal extension predicting

Characteristics of nodules in training and validation groups

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