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Comparison of radiomics tools in differentiation between anterior mediastinal cystic lesion and solid mass
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 선, 주성 | - |
| dc.contributor.author | 유, 슬기 | - |
| dc.date.accessioned | 2023-11-16T05:43:58Z | - |
| dc.date.available | 2023-11-16T05:43:58Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/26859 | - |
| dc.language.iso | en | - |
| dc.title | Comparison of radiomics tools in differentiation between anterior mediastinal cystic lesion and solid mass | - |
| dc.type | Thesis | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000032487 | - |
| dc.subject.keyword | Radiomics | - |
| dc.subject.keyword | computed tomography | - |
| dc.subject.keyword | anterior mediastinum | - |
| dc.subject.keyword | thymic epithelial tumor | - |
| dc.subject.keyword | thymic cyst | - |
| dc.subject.keyword | reproducibility | - |
| dc.subject.keyword | PyRadiomics | - |
| dc.subject.keyword | TexRAD | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | 대학원 의학과 | - |
| dc.contributor.affiliatedAuthor | 유, 슬기 | - |
| dc.date.awarded | 2023 | - |
| dc.type.local | Theses | - |
| dc.citation.date | 2023 | - |
| dc.embargo.liftdate | 9999-12-31 | - |
| dc.embargo.terms | 9999-12-31 | - |
| dc.description.tableOfContents | I. Introduction 1
II. Materials and Methods 4 A. Study cohort 4 B. CT image acquisition 4 C. CT image analysis 5 D. Radiomics analysis 5 1. Volumetric segmentation and radiomic feature extraction 5 2. Radiomics model building 6 E. Diagnostic performance of CT features, radiomics model and integrated model 6 F. Reproducibility of common radiomic features between different platforms 7 G. Statistical analysis 7 III. Results 9 A. CT image analysis 9 B. Construction of radiomics-based predictive model 9 C. Diagnostic performance of CT model, Radiomics model and integrated model 10 D. Reproducibility of radiomic features between two different platforms 11 IV. Discussion 12 V. Conclusion 16 VI. List of Figures 17 A. Figure 1. Study diagram for patient inclusion 17 B. Figure 2. Segmentation in radiomics analysis using PyRadiomics 18 C. Figure 3. Segmentation in radiomics analysis using TexRAD 19 D. Figure 4. ROC analysis of the CT model, Radiomics models and integrated model 20 E. Figure 5. Representative case of true positive prediction for solid mass of both CT model and integrated model 21 F. Figure 6. Representative case of false positive prediction of the CT model and true negative prediction of the integrated model 22 G. Figure 7. Representative case of false negative prediction of both CT model and integrated model 23 H. Figure 8. ROC analysis of the CT model, Radiomics models and integrated model in subgroup consisting of thymic cysts and thymic epithelial tumors. 24 VII. List of Tables 25 A. Table 1. Histopathological diagnoses of the anterior mediastinal lesions 25 B. Table 2. Characteristics of patients and CT features of anterior mediastinal lesions 26 C. Table 3. Predictors for solid mass by multivariable logistic regression analysis using CT features. 28 D. Table 4. Diagnostic performance of conventional CT model, radiomics model and integrated model 29 E. Table 5. Multivariable logistic regression analysis using CT model and radiomics models in differentiation of solid mass from cyst. 30 F. Table 6. Diagnostic performance between thymic cyst and TETs of conventional CT model, radiomics model and integrated model 31 G. Table 7. CCC results of common radiomic features. 32 VIII. References 33 | - |
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