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

Hui Zhu

Hui Zhu

Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical UniversityWenzhou, China
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Zhu, Hui
, 
Hongxia Luo

Hongxia Luo

Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical UniversityWenzhou, China
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Luo, Hongxia
, 
Yanyan Li

Yanyan Li

Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical UniversityWenzhou, China
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Li, Yanyan
, 
Yuhua Zhang

Yuhua Zhang

Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical UniversityWenzhou, China
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Zhang, Yuhua
, 
Zhijing Wu

Zhijing Wu

Department of Statistical Science, University College LondonLondon, United Kingdom
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Wu, Zhijing
 e 
Yan Yang

Yan Yang

Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical UniversityWenzhou, China
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Yang, Yan
  
15 set 2024
The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study's Cover Image
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Categoria dell'articolo: Research Article
Pubblicato online: 15 set 2024
Pagine: 386 - 396
Ricevuto: 04 apr 2024
Accettato: 01 lug 2024
DOI: https://doi.org/10.2478/raon-2024-0040
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© 2024 Hui Zhu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

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

Figure 2.

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 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

Figure 3.

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.
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.
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 leukocyteb 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)
URBCb 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 proteinb 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)
Compositionb 1.000
  Predominately cystic 2 (0.84) 0 (0)
  Predominately solid 235 (99.16) 100 (100.00)
  Solid 0 (0) 0 (0)
Echogenicityb 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)
Shapeb 0.974
  Wider-than-tall 100 (42.19) 42 (42.00)
  Taller-than-wide 137 (57.81) 58 (58.00)
Marginb 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 focib 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 classificationb 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)
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Medicina, Medicina clinica, Medicina interna, Ematologia, Oncologia, Radiologia
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