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Comparison of 2D and 3D radiomics features with conventional features based on contrast-enhanced CT images for preoperative prediction the risk of thymic epithelial tumors

Yu-Hang Yuan

Yu-Hang Yuan

Department of Radiology, The First Hospital of Jilin UniversityJilin, China
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Yuan, Yu-Hang
, 
Hui Zhang

Hui Zhang

Department of Radiology, The First Hospital of Jilin UniversityJilin, China
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Zhang, Hui
, 
Wei-Ling Xu

Wei-Ling Xu

Department of Radiology, The First Hospital of Jilin UniversityJilin, China
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Xu, Wei-Ling
, 
Dong Dong

Dong Dong

Department of Radiology, The First Hospital of Jilin UniversityJilin, China
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Dong, Dong
, 
Pei-Hong Gao

Pei-Hong Gao

Department of Radiology, The First Hospital of Jilin UniversityJilin, China
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Gao, Pei-Hong
, 
Cai-Juan Zhang

Cai-Juan Zhang

Department of Radiology, The First Hospital of Jilin UniversityJilin, China
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Zhang, Cai-Juan
, 
Yan Guo

Yan Guo

GE HealthcareChina
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Guo, Yan
, 
Ling-Ling Tong

Ling-Ling Tong

Department of Pathology, The First Hospital of Jilin UniversityJilin, China
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Tong, Ling-Ling
 and 
Fang-Chao Gong

Fang-Chao Gong

Department of Thoracic Surgery, The First Hospital of Jilin UniversityJilin, China
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Gong, Fang-Chao
  
Feb 27, 2025
Comparison of 2D and 3D radiomics features with conventional features based on contrast-enhanced CT images for preoperative prediction the risk of thymic epithelial tumors's Cover Image
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Article Category: research article
Published Online: Feb 27, 2025
Page range: 69 - 78
Received: Jul 31, 2024
Accepted: Jan 27, 2025
DOI: https://doi.org/10.2478/raon-2025-0016
Keywords
© 2025 Yu-Hang Yuan et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

FIGURE 1.

The flow chart of the case selection process
The flow chart of the case selection process

Figure 2.

The flow chart of the CT imaging analysis. (A) shows the workflow of conventional analysis and 14 conventional features were recorded. (B) shows the workflow of radiomics analysis. 2D and 3D segmentation were performed on the CT images and 396 radiomics features were extracted respectively. The most predictive feature variables were selected, and the multivariate logistic regression analysis was applied to build the prediction models. The predicting abilities of the conventional and radiomics models were demonstrated by receiver operating characteristic (ROC) curves. The goodness of fit was assessed using calibration curve of the Hosmer-Lemeshow test. Additionally, decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models.
The flow chart of the CT imaging analysis. (A) shows the workflow of conventional analysis and 14 conventional features were recorded. (B) shows the workflow of radiomics analysis. 2D and 3D segmentation were performed on the CT images and 396 radiomics features were extracted respectively. The most predictive feature variables were selected, and the multivariate logistic regression analysis was applied to build the prediction models. The predicting abilities of the conventional and radiomics models were demonstrated by receiver operating characteristic (ROC) curves. The goodness of fit was assessed using calibration curve of the Hosmer-Lemeshow test. Additionally, decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models.

Figure 3.

Receiver operating characteristic (ROC) curve analysis of the conventional, 2D and 3D radiomics models: (A) the training set; (B) the testing set.
Receiver operating characteristic (ROC) curve analysis of the conventional, 2D and 3D radiomics models: (A) the training set; (B) the testing set.

Figure 4.

The calibration curves of the conventional, 2D and 3D radiomics models:(A) the training set; (B) the testing set.
The calibration curves of the conventional, 2D and 3D radiomics models:(A) the training set; (B) the testing set.

Figure 5.

Decision curve analysis (DCA) to determine the clinical usefulness of the models by quantifying the net benefits under different threshold probabilities: (A) the training set; (B) the testing set.
Decision curve analysis (DCA) to determine the clinical usefulness of the models by quantifying the net benefits under different threshold probabilities: (A) the training set; (B) the testing set.

Distribution of conventional CT features in training and testing dataset

Testing set Training set
Low-risk (n=32) High-risk (n= 72) P Low-risk (n=14) High-risk (n=31) P
Mean CT value (HU) 79.5 (68.4, 91.6) 62.0 (51.0, 78.8) <0.001 88.5 (76.0, 95.1) 67.0 (59.2, 74.0) <0.001
Standard deviation 18.0 (16.0, 22.6) 16.5 (14.0, 19.0) 0.050 18.0 (15.9, 26.2) 17.0 (14.2, 21.6) 0.548
Minimum CT value (HU) -6.10±30.2 -8.5±25.0 0.678 -0.7±31.6 -7.3±17.9 0.477
Maximum CT value (HU) 148.5 (131.0, 172.1) 118.0 (105.5, 138.6) <0.001 162.0 (148.9, 166.1) 129.0 (105.4, 146.8) 0.002
Long diameter (mm) 50.6±17.0 44.1±19.4 0.106 47.8 (40.9, 57.8) 38.0 (27.7, 61.3) 0.198
Short diameter (mm) 34.7 (23.9, 41.7) 23.2 (17.7, 34.6) 0.009 36.5 (26.0, 45.4) 25.6 (19.0, 39.3) 0.073
Vertical diameter (mm) 48.6 (44.1, 60.2) 40.4 (29.1, 55.2) 0.204 50.5 (44.4, 63.6) 38.9 (33.1, 55.1) 0.059
Area (mm2) 1321.5 (692.0, 1889.8) 628.5 (397.4, 1409.7) 0.008 1024.0 (747.7, 1623.3) 651.0 (346.0, 1362.8) 0.315
Perimeter (mm) 143.0 (110.8, 167.7) 112.5 (78.6, 153.8) 0.021 143.5 (118.6, 255.4) 100.0 (84.1, 194.8) 0.098
Location 0.373 0.790
Right mediastinum 10 (31.3%) 33 (45.8%) 7 (50.0%) 11 (35.5%)
Middle 8 (25.0%) 15 (20.8%) 1 (7.1%) 3 (9.7%)
Left mediastinum 14 (43.8%) 24 (33.3%) 6 (42.9%) 17 (54.8%)
Morphology 0.010 <0.001
Lobular 5 (15.6%) 10 (13.9%) 7 (50.0%) 2 (6.5%)
Shallowly-lobulated 15 (46.9%) 14 (19.4%) 7 (50.0%) 15 (48.4%)
Non-lobular 12 (37.5%) 48 (66.7%) 0 (0.0%) 14 (45.2%)
Demarcation 0.023 0.010
Clear 15 (46.9%) 17 (23.6%) 10 (71.4%) 8 (25.8%)
Unclear 16 (50.0%) 43 (59.7%) 4 (28.6%) 17 (54.8%)
Infiltration 1 (3.1%) 12 (16.7%) 0 (0%) 6 (19.4%)
Internal calcification 8 (25.0%) 13 (18.1%) 0.416 4 (28.6%) 9 (29.0%) 0.746
Necrosis 12 (37.5%) 20 (27.8%) 0.321 9 (64.3%) 12 (38.7%) 0.111

Baseline characteristics of the patients in training and testing dataset

Training set Testing set
Low-risk (n=32) High-risk (n= 72) P Low-risk (n=14) High-risk (n=31) P
Age, (Mean ± SD) years 53.6±11.2 52.5±11.4 0.656 54.0±10.7 56.4±8.9 0.446
Sex (male, No. (%)) 14 (43.8) 37 (51.4) 0.472 7 (50.0) 18 (58.1) 0.614
Myasthenia gravis, No. (%) 7 (21.9) 24 (33.3) 0.238 0 (0.0) 8 (25.8) 0.094
Thoracalgia, No. (%) 3 (9.4) 18 (25.0) 0.067 1 (7.1) 11 (35.5) 0.104

Diagnostic performance of the three models

Model Training dataset Testing dataset
Sensitivity Specificity AUC (95%CI) Sensitivity Specificity AUC (95%CI)
Conventional models 77.8% 87.5% 0.863(0.786-0.940) 54.8% 100.0% 0.853(0.740-0.965)
2D radiomics model 86.1% 71.9% 0.854(0.777-0.931) 77.4% 85.7% 0.834(0.714-0.984)
3D radiomics model 75.0% 93.8% 0.902(0.842-0.963) 67.7% 100.0% 0.906(0.820-0.991)
Language:
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Journal Subjects:
Medicine, Clinical Medicine, Internal Medicine, Haematology, Oncology, Radiology
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