188 research outputs found
CT ์์ ๊ธ์ ํ์๋ฌผ ์ ๊ฑฐ๋ฅผ ์ํ ํจ์จ์ ์ธ ๋น ๊ฒฝํ ๊ต์ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2021. 2. ์ ์๊ธธ.๋น๊ฒฝํ๋ ๋ค์ X์ ์ ์ฌ์ฉํ๊ณ ์๋์ง ์์กด์ ์ธ ๋ฌผ์ง ๊ฐ์ ๊ณ์๋ฅผ ์ด์ฉํ๋ CT ์์คํ
์ ํน์ฑ์ ๋ถ๊ฐํผํ ํ์์ด๋ฉฐ, ์ด๋ ํนํ ๊ธ์ ์์ญ์ ํฌํจํ๋ ํ๋ก์ ์
์์ ๊ฐ์ ์ค์ธก์ ํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก CT ์์์ ํ์๋ฌผ์ ์ ๋ฐํ๋ค. ๊ธ์ ํ์๋ฌผ ์ ๊ฐํ๋ CT ์์์ ์กด์ฌํ๋ ์ด๋ฌํ ํ์๋ฌผ์ ์ ๊ฑฐํ๊ณ ๊ฐ๋ ค์ง ์ค์ ์ ๋ณด๋ฅผ ๋ณต์ํ๋ ๊ณผ์ ์ด๋ค. ์์์ ํตํ ์ง๋จ๊ณผ ๋ฐฉ์ฌ์ ์น๋ฃ๋ฅผ ์ํ ๊ณํ ์๋ฆฝ์ ์์ด์ ์ ํํ CT ์์์ ํ๋ํ๊ธฐ ์ํด ๊ธ์ ํ์๋ฌผ์ ์ ๊ฑฐ๋ ํ์์ ์ด๋ค. ๋ฐ๋ณต์ ์ธ ์ฌ๊ตฌ์ฑ์ ์ํ ์์น์ ๋ฐฉ๋ฒ์ ๊ธฐ๋ฐ์ ๋ ํจ๊ณผ์ ์ธ ๊ธ์ ํ์๋ฌผ ์ ๊ฑฐ์ ๊ดํ ์ต์ ์ฐ๊ตฌ๋ค์ด ๋ฐํ๋์์ผ๋ ๋ฌด๊ฑฐ์ด ๊ณ์ฐ๋์ผ๋ก ์ธํด ์์ ์ค์ต์ ์ ์ฉ์ด ์ด๋ ค์ด ์ํฉ์ด๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์ด๋ฌํ ๊ณ์ฐ์ ์ธ ์ด์๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํ ํจ์จ์ ์ธ ๋น ๊ฒฝํ ์ถ์ ๋ชจ๋ธ๊ณผ ์ด๋ฅผ ์ด์ฉํ ๊ธ์ ํ์๋ฌผ ์ ๊ฐํ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ ์ํ ๋ชจ๋ธ์ ๊ธ์ ๋ฌผ์ฒด์ ๊ธฐํ์ ๋ณด์ ๋ค์ X์ ์ด ๋ฌผ์ฒด๋ฅผ ํต๊ณผํ๋ฉด์ ๋ฐ์ํ๋ ๋น๊ฒฝํ์ ๋ฌผ๋ฆฌ์ ์ธ ํน์ฑ์ ๋ฐ์ํ๋ค. ๋ชจ๋ธ์ ํ์ํ ๋๋ถ๋ถ์ ๋งค๊ฐ๋ณ์๋ค์ ์์นํ์ ์ธ ๋ฐฉ๋ฒ์ผ๋ก ๊ต์ ์ ์ CT ์์๊ณผ CT ์์คํ
์ผ๋ก๋ถํฐ ์ถ๊ฐ์ ์ธ ์ต์ ํ ๊ณผ์ ์์ด ํ๋ํ๋ค. ๋น๊ฒฝํ ํ์๋ฌผ๊ณผ ๊ด๋ จ๋ ๋งค๊ฐ ๋ณ์ ์ค ๋จ ํ๋๋ง ์ฌ๊ตฌ์ฑ ์ดํ์ ์์ ๋จ๊ณ์์ ์ ํ ์ต์ ํ๋ฅผ ํตํด ํ์๋๋ค. ๋ํ ์ ์ํ ๋ฐฉ๋ฒ์ผ๋ก ๊ต์ ๋ ๊ฒฐ๊ณผ ์์์ ์์กดํ๋ ํ์๋ฌผ๋ค์ ์ ๊ฑฐํ๊ธฐ ์ํ ์ถ๊ฐ์ ์ธ ๋๊ฐ์ง ๊ฐ์ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค.
๋ค์์ ์๋ฎฌ๋ ์ด์
๋ฐ์ดํฐ์ ์ค์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ์ฌ ์ ์ฑ์ ๋ฐ ์ ๋์ ๋น๊ต๋ฅผ ํตํด ์ ์ ๊ธฐ๋ฒ์ ์ ํจ์ฑ์ด ์ฒด๊ณ์ ์ผ๋ก ํ๊ฐ๋์๋ค. ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ์ ํ์ฑ ๋ฐ ๊ฒฌ๊ณ ์ฑ ์ธก๋ฉด์์ ์ ์๋ฏธํ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ์๊ณ , ๊ธฐ์กด์ ๊ธฐ๋ฒ๋ค์ ๋นํด ํฅ์๋ ๊ฒฐ๊ณผ ์์์ ํ์ง ๋ฟ๋ง ์๋๋ผ ์์์ ์ผ๋ก ์ ์ฉํ ๋งํ ๋น ๋ฅธ ์ํ ์๊ฐ์ ๋ณด์ฌ์ฃผ์๋ค. ์ด ์ฐ๊ตฌ๋ CT ์์์ ํตํ ์ง๋จ๊ณผ ๋ฐฉ์ฌ๋ฅ ์น๋ฃ์ ๊ณํ ์๋ฆฝ์ ์ํ ์ ํ์ฑ ํฅ์์ ์ ์๋ฏธํ ์๋ฏธ๋ฅผ ๊ฐ๋๋ค.Beam hardening in X-ray computed tomography (CT) is an inevitable problem due to the characteristics of CT system that uses polychromatic X-rays and energy-dependent attenuation coefficients of materials. It causes artifacts in CT images as the result of underestimation on the projection data, especially on metal regions. Metal artifact reduction is the process of reducing the artifacts in CT and restoring the actual information hidden by the artifacts. In order to obtain exact CT images for more accurate diagnosis and treatment planning on radiotherapy in clinical fields, it is essential to reduce metal artifacts. State-of-the-art approaches on effectively reducing metal artifact based on numerical methods by iterative reconstruction have been presented. However, it is difficult to be applied in clinical practice due to a heavy computational burden.
In this dissertation, we proposes an efficient beam-hardening estimation model and a metal artifact reduction method using this model to address this computational issue. The proposed model reflects the geometric information of metal objects and physical characteristics of beam hardening during the transmission of polychromatic X-ray through a material. Most of the associated parameters are numerically obtained from an initial uncorrected CT image and CT system without additional optimization. Only the unknown parameter related to beam-hardening artifact is fine-tuned by linear optimization, which is performed only in the reconstruction image domain. Two additional refinement methods are presented to reduce residual artifacts in the result image corrected by the proposed metal artifact reduction method.
The effectiveness of the proposed method was systematically assessed through qualitative and quantitative comparisons using numerical simulations and real data. The proposed algorithm showed significant results in the aspects of accuracy and robustness. Compared to existing methods, it showed improved image quality as well as fast execution time that is clinically applicable. This work may have significant implications in improving the accuracy of diagnosis and treatment planning for radiotheraphy through CT imaging.Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Scope and aim 5
1.3 Main contribution 6
1.4 Contents organization 8
Chapter 2 Related Works 9
2.1 CT physics 9
2.1.1 Fundamentals of X-ray 10
2.1.2 CT reconstruction algorithms 13
2.2 CT artifacts 18
2.2.1 Physics-based artifacts 19
2.2.2 Patient-based artifacts 21
2.3 Metal artifact reduction 22
2.3.1 Sinogram-completion based MAR 24
2.3.2 Sinogram-correction based MAR 27
2.3.3 Deep-learning based MAR 29
2.4 Summary 31
Chapter 3 Constrained Beam-hardening Estimator for Polychromatic X-ray 33
3.1 Characteristics of polychromatic X-ray 34
3.2 Constrained beam-hardening estimator 35
3.3 Summary 41
Chapter 4 Metal Artifact Reduction with Constrained Beam-hardening Estimator 43
4.1 Metal segmentation 44
4.2 X-ray transmission length 46
4.3 Artifact reduction with CBHE 48
4.3.1 Artifact estimation for a single type of metal 48
4.3.2 Artifact estimation for multiple types of metal 51
4.4 Refinement methods 54
4.4.1 Collaboration with ADN 54
4.4.2 Application of CBHE to bone 57
4.5 Summary 59
Chapter 5 Experimental Results 61
5.1 Data preparation and quantitative measures 62
5.2 Verification on constrained beam-hardening estimator 67
5.2.1 Accuracy 67
5.2.2 Robustness 72
5.3 Performance evaluations 81
5.3.1 Evaluation with simulated phantoms 81
5.3.2 Evaluation with hardware phantoms 86
5.3.3 Evaluation on refinement methods 91
Chapter 6 Conclusion 95
Bibliography 101
์ด๋ก 115
Acknowledgements 117Docto
C57BL/6 ๋ง์ฐ์ค์์์ ์ ์์ด์ ์ญํ์ฑ ๊ฐ์์ ์ ๋ชจ๋ธ ๊ตฌ์ถ ๋ฐ ์ข ์ ๋ฉด์ญ ๋ฏธ์ธํ๊ฒฝ ๋ถ์
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์๊ณผ๋ํ ์ํ๊ณผ, 2023. 8. ์ด๊ท์ธ.์๋ก : ๋ฉด์ญ์ข
์ํ์์ ๋ง์ฐ์ค ๋ชจ๋ธ์ ํ๋ณดํ๋ ๊ฒ์ ์๋ก์ด ์น๋ฃ ๋์์ ์๋ณํ๊ณ ๊ฒ์ฆํ๋๋ฐ ์ค์ํ๋ค. C57BL/6 ๋ง์ฐ์ค๋ ๋๋ฌผ์ ๋ฉด์ญ์ฒด๊ณ ์ค ๊ฐ์ฅ ํน์ฑํ๋ ๊ฒ ์ค ํ๋๋ก, ๋ฉด์ญ์ข
์ํ์์ ์๋ก์ด ๋ฐ๊ฒฌ์ ์ํ ๊ฐ์ฅ ํจ๊ณผ์ ์ธ ํ๋ซํผ์ ์ ๊ณตํ๋ค. TBP3743(๋ง์ฐ์ค ๊ฐ์์ ์ญํ์ฑ์) ์ธํฌ๋ฅผ B6129SF1 ํ์ด๋ธ๋ฆฌ๋ ๋ง์ฐ์ค์ ์ฌ์ฉํ์ฌ ์ ์์ด์ ๋ชจ๋ธ์ด ๊ตฌ์ถ๋์์ผ๋ฉฐ, ์ด ๋ชจ๋ธ์ C57BL/6 ๊ณํต ๋ง์ฐ์ค์ ๋นํด ์ข
์ ๋ฉด์ญํ ์ฐ๊ตฌ์ ์์ด์ ์ ํ ๋๋ค. ๋ณธ ์ฐ๊ตฌ๋ C57BL/6 ๋ง์ฐ์ค์์ ์๋ก์ด ์ ์์ด์ ATC ๋ชจ๋ธ์ ๊ตฌ์ถํ๊ณ , ํด๋น ๋ชจ๋ธ์์ ์ข
์ ๋ฏธ์ธํ๊ฒฝ์ ๋ฉด์ญํน์ฑ์ ์ค์ฌ์ผ๋ก ์ฐ๊ตฌ๋ฅผ ์งํํ์๋ค.
๋ฐฉ๋ฒ: Adapted TBP3743 ์ธํฌ๋ C57BL/6 ๋ง์ฐ์ค์์ in vivo serial passaging์ ํตํ์ฌ ๋ง๋ค์ด์ก๋ค. ๊ทธ๋ฆฌ๊ณ ๋ค์๊ณผ ๊ฐ์ด ๋ง์ฐ์ค ๊ฐ์์ ์ ์ ์์ด์ ํ์๋ค: original TBP3743 ์ธํฌ๋ฅผ ์ฃผ์
ํ B6129SF1 ๋ง์ฐ์ค (original/129), adapted TBP3743 ์ธํฌ๋ฅผ ์ฃผ์
ํ B6129SF1 ๋ง์ฐ์ค (adapted/129) ๋ฐ adapted TBP3743 ์ธํฌ๋ฅผ ์ฃผ์
ํ C57BL/6 ๋ง์ฐ์ค (adapted/B6).
๊ฒฐ๊ณผ: Adapted TBP3743 ์ธํฌ๋ ์ฒด์ธ์์ ์ธํฌ ๋ชจ์, ์์กด์จ, ์ด๋/์นจ์ต ๋ฅ๋ ฅ์ original TBP3743 ์ธํฌ์ ๋น๊ตํ์์ ๋ ๋น์ทํ์๋ค. Ki-67+ ์ธํฌ ๋ถํ์ original/129์ ๋นํด adapted/129์์ ๋ ๋์ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์๋ค. ์ ์์ด์ ์ข
์ RNA sequencing ๋ฐ์ดํฐ ๋ถ์์ ํตํ์ฌ adapted/129๋ original/129์ ๋นํ์ฌ ๋ ๊ฐํ oncogenic properties๋ฅผ ๋ณด์ฌ์ฃผ์๋ค. ๋ฐ๋ฉด, adapted/B6์์ ์ฑ์ฅํ ์ ์์ด์ ์ข
์์ adapted/129๋ณด๋ค ํฌ๊ธฐ๊ฐ ์๊ณ ๋ฎ์ Ki-67+ ์ธํฌ ๋ถํ์ ๋ณด์๋ค. ๊ทธ๋ฌ๋ adapted/B6์ adapted/129์์์ oncogenic properties๋ ์ ์ฌํ๋ค. Immune-related pathways๋ adapted/B6์์ adapted/129์ ๋น๊ตํ์ฌ ํ๋ถํ๊ฒ ๋ฐํ๋์๋ค. ์ ์์ด์ ์ข
์์ ์ ์ธํฌ๋ถ์ ๊ฒฐ๊ณผ, adapted/B6์์ ์ธํฌ๋
์ฑ CD8+ T์ธํฌ์ monocytic-myeloid-derived suppressor ์ธํฌ ๋ถํ์ด ๋ชจ๋ adapted/129๋ณด๋ค ๋์๋ค. adapted/B6์์ ์ถ์ ๋ CD8+ ๋ฐ CD4+ ์ธํฌ ๋ถํ์ ์ธ๊ฐ ATC์์์ ์ ์ฌํ์ง๋ง original/B6์์๋ ๋ฌด์ํ ์ ์์๋ค.
๊ฒฐ๋ก : ์ ์ฐ๊ตฌ๋ฅผ ํตํด C57BL/6 ๋ง์ฐ์ค์์ ์ญํ์ฑ ๊ฐ์์ ์์ ์๋ก์ด ์ ์์ด์ ๋ชจ๋ธ์ ์ฑ๊ณต์ ์ผ๋ก ์๋ฆฝํ์๋ค. ๊ธฐ์กด์ B6129SF1 ๋ง์ฐ์ค ๋ชจ๋ธ๊ณผ ๋น๊ตํ๋ฉด, ์ด ๋ชจ๋ธ์ ๋ ๊ณต๊ฒฉ์ ์ธ ์ข
์ํน์ฑ๊ณผ ๊ฐ๋ ฅํ ๋ฉด์ญ๋ฐ์์ ๋ํ๋ธ๋ค. ๋ํ ์ด ๋ชจ๋ธ์ ์ข
์์ ๋ฏธ์ธํ๊ฒฝ์ ๋ ๊น์ด ์ฐ๊ตฌํ๋๋ฐ ํ์ฉ๋๊ณ , ์๋ก์ด ํญ์์ ๊ฐ๋ฐ์ ์ํ ํ๋ซํผ์ผ๋ก์ ์์ฉํ ๊ฒ์ด๋ค.Background: Securing a well-established mouse model is important in identifying and validating new therapeutic targets for immuno-oncology. The C57BL/6 mouse is one of the most fully characterised immune system of any animal and provides powerful platform for immuno-oncology discovery. An orthotopic tumor model has been established using TBP3743 (murine anaplastic thyroid cancer [ATC]) cells in B6129SF1 hybrid mice, this model has limited data on tumor immunology than C57BL/6 inbred mice. This study aimed to establish a novel orthotopic ATC model in C57BL/6 mice and characterize the tumor microenvironment focusing immunity in the model.
Methods: Adapted TBP3743 cells were generated via in vivo serial passaging in C57BL/6 mice. Subsequently, the following orthotopic tumor models were established via intrathyroidal injection: B6129SF1 mice injected with original TBP3743 cells (original/129), B6129SF1 mice injected with adapted cells (adapted/129), and C57BL/6 mice injected with adapted cells (adapted/B6).
Results: The adapted TBP3743 cells de-differentiated but exhibited cell morphology, viability, and migration/invasion potential comparable with those of original cells in vitro. The adapted/129 contained a higher Ki-67+ cell fraction than the original/129. RNA sequencing data of orthotopic tumors revealed enhanced oncogenic properties in the adapted/129 compared with those in the original/129. In contrast, the orthotopic tumors grown in the adapted/B6 were smaller, with a lower Ki-67+ cell fraction than those in the adapted/129. However, the oncogenic properties of the tumors within the adapted/B6 and adapted/129 were similar. Immune-related pathways were enriched in the adapted/B6 compared with those in the adapted/129. Flow cytometric analysis of the orthotopic tumors revealed higher cytotoxic CD8+ T cell and monocytic-myeloid-derived suppressor cell fractions in the adapted/B6 compared with the adapted/129. The estimated CD8+ and CD4+ cell fractions in the adapted/B6 were similar to those in human ATCs but negligible in the original/B6.
Conclusions: A novel orthotopic tumor model of ATC was established in C57BL/6 mice. Compared with the original B6129SF1 murine model, the novel model exhibited more aggressive tumor cell behaviours and strong immune responses. Expect that this novel model contributes to the understanding tumor microenvironment and provides the platform for drug development.Introduction 1
Methods 4
Results 15
Discussion 69
References 76
Abstract in Korean 83๋ฐ
์ 21๋ ์ด์ ์ ์ค์ฌ์ผ๋ก
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ํ์ ๋ํ์ ํ์ ํ๊ณผ(ํ์ ํ์ ๊ณต), 2020. 8. ๊ธํ์ญ.๋ณธ ์ฐ๊ตฌ๋ ๊ตญํ์์ ์ด์ ๊ฑฐ์ ๋น์ ๊ฒฐ์ ์์ธ์ ๋ฐํ๋ ค๋ ์๋์์ ์์๋๋ค. ๊ทธ ์ค์์๋ 2020๋
4์ 15์ผ ์ค์๋ 21๋ ์ด์ ์ด ์ฃผ์ํ ์ฐ๊ตฌ ๋์์ด๋ฉฐ, ๋๋ถ์ด๋ฏผ์ฃผ๋น(253๋ช
)๊ณผ ๋ฏธ๋ํตํฉ๋น(236๋ช
)์ ์ง์ญ๊ตฌ ํ๋ณด์ 489๋ช
์ ๋์์ผ๋ก ๋ถ์ํ๋ค.
๊ทธ๋์ ํ๊ตญ ์ ์น์์๋ ์ ๋น ๋ด ์์์ ์ค๋ ฅ์๊ฐ ๋น๋ฏผ์ฃผ์ ์ด๊ณ ๋ถํฌ๋ช
ํ ๋ฐฉ์์ผ๋ก ๊ตญํ์์ ์ ๊ฑฐ์ ๋์ค ํ๋ณด์๋ฅผ ๊ณต์ฒํ๋ ๊ฒ ๋ฌธ์ ๋ก ์ง์ ๋๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๊ทธ๋ฌํ ๋ฐฉ์๋ณด๋ค ๊ฒฝ์ ์ ํตํ ๋ฐฉ์์ด ๊ณผ์ ์ ๋ฏผ์ฃผ์ฑ๋ฟ ์๋๋ผ ๊ฒฐ๊ณผ์ ํจ๊ณผ์ฑ๋ ๋ด๋ณดํ ์ ์์ ๊ฒ์ผ๋ก ๊ฐ์ ํ๋ค. ์ค์ 253๊ฐ ์ง์ญ๊ตฌ์ ์ ๊ฑฐ ๊ฒฐ๊ณผ๋ฅผ ๋ถ์ํด ๊ฒฝ์ ์ ํตํ ํ๋ณด์ ๊ณต์ฒ์ ์ ๋ต(์ฐ์ )๊ณต์ฒ, ๋จ์๊ณต์ฒ ๋ฑ ๋น๊ฒฝ์ ๋ฐฉ์๋ณด๋ค ๋น์ ๊ฐ๋ฅ์ฑ์ด๋ ๋ํ์จ ์ ๊ณ ์ธก๋ฉด์์ ์ ๋ฆฌํ๋ค๋ ๊ฒฐ๋ก ์ ์ป์๋ค. ๊ฒฝ์ ์ ๋ณด๋์ค ํจ๊ณผ(primary bonus)๊ฐ ์กด์ฌํ๋ ๊ฒ์ด๋ค.
๋ํ 21๋ ์ด์ ์์๋ ์ฌ๋น ํ๋ณด์์ธ์ง ์๋์ง๊ฐ ์ ๊ฑฐ ๊ฒฐ๊ณผ์ ์๋นํ ํฐ ์ํฅ์ ๋ผ์ณค๋ค. ์ฝ๋ก๋๋ฐ์ด๋ฌ์ค๊ฐ์ผ์ฆ-19(COVID-19) ์ฌํ ์์์ ์ค์๋ ์ด๋ฒ ์ ๊ฑฐ๋ ์ ๊ถ ์์ ๋ก ์ด ์ ๊ถ ์ฌํ๋ก ์ ์๋ํด ์ฌ๋น ํ๋ฆฌ๋ฏธ์์ด ํฌ๊ฒ ์์ฉํ๋ค. ๋ฐ๋ฉด ํ์ง ๊ตญํ์์์ด ํ์ฌ ์์ ์ ์ง์ญ๊ตฌ์ ์ถ๋งํ ๋ ์ป๋ ์ด๋ฅธ๋ฐ ํ์ง ํจ๊ณผ(incumbency advantage)๋ ๋ํ๋์ง ์์๋ค. ๊ทธ๋ฆฌ๊ณ ํ๋ณด์ ์์ ๋น์ ๊ฐ๋ฅ์ฑ์ ํฐ ์๊ด์ด ์๋ ๊ฒ์ผ๋ก ๋ถ์๋๋ค. ์ฌ์ค์ ๋๋ถ์ด๋ฏผ์ฃผ๋น๊ณผ ๋ฏธ๋ํตํฉ๋น์ ์์ ๋๊ฒฐ ๊ตฌ๋๋ก ์ด์ ์ด ์น๋ฌ์ก๊ธฐ ๋๋ฌธ์ ๋ค๋ฅธ ์ ๋น ํ๋ณด์์ ์ซ์๋ ํฐ ์๋ฏธ๊ฐ ์์๋ ๊ฒ์ด๋ค.This study investigates the process and outcome of party nominations in National Assembly election in the Republic of Korea. Especially this study reviews how the two main parties - the ruling Democratic Party (DP) and the main opposition United Future Party (UFP) - nominated their candidates running for the 21st general election. And identifies the factors to the electoral victories of individual candidates.
This study assumes that the primary effect and ruling party premium and incumbency advantage are factors affecting election. Most of all, focus on the primary effect. In other words, this study analyzes intensively on bottom-up style candidate selection. And estimates the relationship between types of candidate selections and their political outcomes.
The findings of this study can be summarized into a few consequences. First, the bonus effect of the primary exist. That is to say, bottom-up style of candidate selection system helps candidates earn more votes and their winning chances. Second, ruling party premium is strong. Many DP candidates won election for the reason that they are member of the ruling party. Third, incumbency advantage was not existing.
There are limits to this study. There was no third party threatening the two main parties. And the election was held in the midst of Coronavirus disease 2019 (COVID-19). For that reason, there were other factors as well, but they were not analyzed.์ 1 ์ฅ ์๋ก 1
์ 1 ์ ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์ 1
์ 2 ์ ์ฐ๊ตฌ์ ๋์๊ณผ ๋ฐฉ๋ฒ 5
1. ์ฐ๊ตฌ์ ๋์ 5
2. ์ฐ๊ตฌ์ ๋ฐฉ๋ฒ 7
์ 2 ์ฅ ์ด๋ก ์ ๋
ผ์ ๋ฐ ์ ํ์ฐ๊ตฌ์ ๊ฒํ 9
์ 1 ์ ๋ฏผ์ฃผ์ฑ๊ณผ ํจ์จ์ฑ 9
์ 2 ์ ๊ท๋ฒ์ ์ฐ๊ตฌ 10
1. ๊ณต์ฒ์ ๋ฏผ์ฃผํ 10
2. ์ํฅ์ ๊ณต์ฒ์ ํ์์ฑ๊ณผ ๋ฌธ์ ์ 13
3. ๊ท๋ฒ์ ์ฐ๊ตฌ์ ํ๊ณ 19
์ 3 ์ ๊ฒฝํ์ ์ฐ๊ตฌ 19
1. ๊ณต์ฒ ๊ณผ์ ์ ๋ํ ๋ถ์ 20
2. ๊ณต์ฒ ๊ฒฐ๊ณผ์ ๋ํ ๋ถ์ 22
3. ๊ณต์ฒ ์ ํ์ ๋ฐ๋ฅธ ์์ ํ๋ ๋ถ์ 26
์ 3 ์ฅ ์ฐ๊ตฌ์ ์ค๊ณ 29
์ 1 ์ ์ฐ๊ตฌ ๋ฌธ์ 29
1. ์ ๋น ๋ด๋ถ์ ์์ธ 29
2. ์ ๋น ์ธ๋ถ์ ์์ธ 32
3. ํ๋ณด์ ๊ฐ์ธ์ ์์ธ 33
์ 2 ์ ์ฐ๊ตฌ์ ๋ณ์ 34
1. ์ข
์๋ณ์ 34
2. ๋
๋ฆฝ๋ณ์ 35
3. ํต์ ๋ณ์ 35
์ 3 ์ ์ฐ๊ตฌ์ ๋ชจํ 37
์ 4 ์ฅ ๊ธฐ์ด ๋ถ์ 38
์ 1 ์ ๊ณต์ฒ ๊ธฐ์ค 38
์ 2 ์ ๊ณต์ฒ ์ ํ 40
์ 3 ์ ์ ๊ฑฐ ๊ฒฐ๊ณผ 46
์ 5 ์ฅ ์ฌ์ธต ๋ถ์ 55
์ 1 ์ ๊ธฐ์ ํต๊ณ 55
์ 2 ์ ๊ฐ์ค์ ๊ฒ์ฆ 60
1. ์ ๋น ๋ด๋ถ์ ์์ธ 61
2. ์ ๋น ์ธ๋ถ์ ์์ธ 76
3. ํ๋ณด์ ๊ฐ์ธ์ ์์ธ 78
4. ํต์ ๋ณ์ 80
5. ์ฐ๊ตฌ ๊ฒฐ๊ณผ์ ์์ฝ 81
์ 6 ์ฅ ๊ฒฐ๋ก 83
์ 1 ์ ์ฐ๊ตฌ์ ์์ 83
์ 2 ์ ์ฐ๊ตฌ์ ํ๊ณ ๋ฐ ํฅํ ๊ณผ์ 85Maste
Diagnostic Accuracy of Percutaneous Transthoracic Needle Lung Biopsies: A Multicenter Study
OBJECTIVE: To measure the diagnostic accuracy of percutaneous transthoracic needle lung biopsies (PTNBs) on the basis of the intention-to-diagnose principle and identify risk factors for diagnostic failure of PTNBs in a multi-institutional setting.
MATERIALS AND METHODS: A total of 9384 initial PTNBs performed in 9239 patients (mean patient age, 65 years [range, 20-99 years]) from January 2010 to December 2014 were included. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PTNBs for diagnosis of malignancy were measured. The proportion of diagnostic failures was measured, and their risk factors were identified.
RESULTS: The overall accuracy, sensitivity, specificity, PPV, and NPV were 91.1% (95% confidence interval [CI], 90.6-91.7%), 92.5% (95% CI, 91.9-93.1%), 86.5% (95% CI, 85.0-87.9%), 99.2% (95% CI, 99.0-99.4%), and 84.3% (95% CI, 82.7-85.8%), respectively. The proportion of diagnostic failures was 8.9% (831 of 9384; 95% CI, 8.3-9.4%). The independent risk factors for diagnostic failures were lesions โค 1 cm in size (adjusted odds ratio [AOR], 1.86; 95% CI, 1.23-2.81), lesion size 1.1-2 cm (1.75; 1.45-2.11), subsolid lesions (1.81; 1.32-2.49), use of fine needle aspiration only (2.43; 1.80-3.28), final diagnosis of benign lesions (2.18; 1.84-2.58), and final diagnosis of lymphomas (10.66; 6.21-18.30). Use of cone-beam CT (AOR, 0.31; 95% CI, 0.13-0.75) and conventional CT-guidance (0.55; 0.32-0.94) reduced diagnostic failures.
CONCLUSION: The accuracy of PTNB for diagnosis of malignancy was fairly high in our large-scale multi-institutional cohort. The identified risk factors for diagnostic failure may help reduce diagnostic failure and interpret the biopsy results.ope
Regional Amyloid Burden Differences Evaluated Using Quantitative Cardiac MRI in Patients with Cardiac Amyloidosis
Objective: This study aimed to investigate the regional amyloid burden and myocardial deformation using T1 mapping and strain values in patients with cardiac amyloidosis (CA) according to late gadolinium enhancement (LGE) patterns.
Materials and methods: Forty patients with CA were divided into 2 groups per LGE pattern, and 15 healthy subjects were enrolled. Global and regional native T1 and T2 mapping, extracellular volume (ECV), and cardiac magnetic resonance (CMR)-feature tracking strain values were compared in an intergroup and interregional manner.
Results: Of the patients with CA, 32 had diffuse global LGE (group 2), and 8 had focal patchy or no LGE (group 1). Global native T1, T2, and ECV were significantly higher in groups 1 and 2 than in the control group (native T1: 1384.4 ms vs. 1466.8 ms vs. 1230.5 ms; T2: 53.8 ms vs. 54.2 ms vs. 48.9 ms; and ECV: 36.9% vs. 51.4% vs. 26.0%, respectively; all, p < 0.001). Basal ECV (53.7%) was significantly higher than the mid and apical ECVs (50.1% and 50.0%, respectively; p < 0.001) in group 2. Basal and mid peak radial strains (PRSs) and peak circumferential strains (PCSs) were significantly lower than the apical PRS and PCS, respectively (PRS, 15.6% vs. 16.7% vs. 26.9%; and PCS, -9.7% vs. -10.9% vs. -15.0%; all, p < 0.001). Basal ECV and basal strain (2-dimensional PRS) in group 2 showed a significant negative correlation (r = -0.623, p < 0.001). Group 1 showed no regional ECV differences (basal, 37.0%; mid, 35.9%; and apical, 38.3%; p = 0.184).
Conclusion: Quantitative T1 mapping parameters such as native T1 and ECV may help diagnose early CA. ECV, in particular, can reflect regional differences in the amyloid deposition in patients with advanced CA, and increased basal ECV is related to decreased basal strain. Therefore, quantitative CMR parameters may help diagnose CA and determine its severity in patients with or without LGE.ope
Predictive factors of recurrence after resection of subsolid clinical stage IA lung adenocarcinoma
Background: Ongoing studies are currently investigating the extent of surgical resection required for subsolid cancers. This study aimed to investigate the predictive factors related to recurrence in patients with clinical stage IA subsolid cancer who underwent either lobectomy or sublobar resection.
Methods: This was a prospective multicenter observational study conducted in eight qualifying university teaching hospitals between April 2014 and December 2016. A total of 173 patients with subsolid nodules pathologically confirmed to have primary lung adenocarcinoma and stage IA disease were included in the final analysis. All patients underwent lobectomy, segmentectomy, or wedge resection performed by experienced thoracoscopic surgeons at each site. The surgical procedure was chosen based on the decision of the surgeons involved. The primary endpoint was time to recurrence (TTR).
Results: The study population was 43.9% (76 of 173) male with a mean age of 60.7 years. During the median follow-up period of 5.01 years, nine patients (5%) experienced disease recurrence. In the multivariable analysis, tumor size (size โฅ2 cm) (hazard ratio: 73.717, 95% confidence interval [CI]: 3.635-895.036; p < 0.001) and stage IA3 (hazard ratio: 62.010, 95% CI: 2.837-855.185; p < 0.001) were independent predictors of tumor recurrence. When analyzing the recurrence outcome in patients according to surgical procedure, no significant difference was found in TTR among the three groups (i.e., lobectomy, segmentectomy, and wedge resection; p = 0.99).
Conclusions: Patients with radiologically subsolid lung adenocarcinoma measuring <3 cm could be candidates for sublobar resection instead of lobectomy.ope
LOGIS (LOcalization of Ground-glass-opacity and pulmonary lesions for mInimal Surgery) registry: Design and Rationale
Background and purpose: An optimal pulmonary localization technique for video-assisted thoracic surgery (VATS) of small lung nodules has not yet been established. The LOcalization of Ground-glass-opacity and pulmonary lesions for mInimal Surgery (LOGIS) registry aims to establish a multicenter database and investigate the usefulness and safety of localization techniques for small pulmonary lesions in individuals undergoing VATS. Methods/Design: The LOGIS registry is a large-scale, multicenter cohort study, aiming to enroll 825 patients at 10 institutions. Based on the inclusion and exclusion criteria, all study participants with pulmonary lesions indicated for VATS will be screened and enrolled at each site. All study participants will undergo preoperative lesion localization by the hook-wire or lipiodol localization methods according to site-specific methods. Within a few hours of marking, thoracoscopic surgery will be done under general anesthesia by experienced thoracoscopic surgeons. The primary endpoints are the success and complication rates of the two localization techniques. Secondary endpoints include procedure duration, recurrence rate, and all-cause mortality. Study participant enrollment will be completed within 2 years. Procedure success rates and incidence of complications will be analyzed based on computed tomography findings. Procedure duration, recurrence rate, and all-cause mortality will be compared between the two techniques. The study will require 5 years for completion, including 6 months of preparation, 3.5 years for recruitment, and 1 year of follow-up endpoint assessment. Discussion: The LOGIS registry, once complete, will provide objective comparative results regarding the usefulness and safety of the lipiodol and hook-wire localization techniques.ope
Dual-Energy CT for Pulmonary Embolism: Current and Evolving Clinical Applications
Pulmonary embolism (PE) is a potentially fatal disease if the diagnosis or treatment is delayed. Currently, multidetector computed tomography (MDCT) is considered the standard imaging method for diagnosing PE. Dual-energy CT (DECT) has the advantages of MDCT and can provide functional information for patients with PE. The aim of this review is to present the potential clinical applications of DECT in PE, focusing on the diagnosis and risk stratification of PE.ope
Analysis of Complications of Percutaneous Transthoracic Needle Biopsy Using CT-Guidance Modalities In a Multicenter Cohort of 10568 Biopsies
OBJECTIVE: To analyze the complications of percutaneous transthoracic needle biopsy using CT-based imaging modalities for needle guidance in comparison with fluoroscopy in a large retrospective cohort.
MATERIALS AND METHODS: This study was approved by multiple Institutional Review Boards and the requirement for informed consent was waived. We retrospectively included 10568 biopsies from eight referral hospitals from 2010 through 2014. In univariate and multivariate logistic analyses, 3 CT-based guidance modalities (CT, CT fluoroscopy, and cone-beam CT) were compared with fluoroscopy in terms of the risk of pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis, with adjustment for other risk factors.
RESULTS: Pneumothorax occurred in 2298 of the 10568 biopsies (21.7%). Tube insertion was required after 316 biopsies (3.0%), and hemoptysis occurred in 550 cases (5.2%). In the multivariate analysis, pneumothorax was more frequently detected with CT {odds ratio (OR), 2.752 (95% confidence interval [CI], 2.325-3.258), p < 0.001}, CT fluoroscopy (OR, 1.440 [95% CI, 1.176-1.762], p < 0.001), and cone-beam CT (OR, 2.906 [95% CI, 2.235-3.779], p < 0.001), but no significant relationship was found for pneumothorax requiring chest tube insertion (p = 0.497, p = 0.222, and p = 0.216, respectively). The incidence of hemoptysis was significantly lower under CT (OR, 0.348 [95% CI, 0.247-0.491], p < 0.001), CT fluoroscopy (OR, 0.594 [95% CI, 0.419-0.843], p = 0.004), and cone-beam CT (OR, 0.479 [95% CI, 0.317-0.724], p < 0.001) guidance.
CONCLUSION: Hemoptysis occurred less frequently with CT-based guidance modalities in comparison with fluoroscopy. Although pneumothorax requiring chest tube insertion showed a similar incidence, pneumothorax was more frequently detected using CT-based guidance modalities.ope
Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: a phantom study
Background: The aim of this study was to compare the dose reduction potential and image quality of deep learning-based image reconstruction (DLIR) with those of filtered back-projection (FBP) and iterative reconstruction (IR) and to determine the clinically usable dose of DLIR for low-dose chest computed tomography (LDCT) scans. Methods: Multi-slice computed tomography (CT) scans of a chest phantom were performed with various tube voltages and tube currents, and the images were reconstructed using seven methods to control the amount of noise reduction: FBP, three stages of IR, and three stages of DLIR. For subjective image analysis, four radiologists compared 48 image data sets with reference images and rated on a 5-point scale. For quantitative image analysis, the signal to noise ratio (SNR), contrast to noise ratio (CNR), nodule volume, and nodule diameter were measured. Results: In the subjective analysis, DLIR-Low (0.46 mGy), DLIR-Medium (0.31 mGy), and DLIR-High (0.18 mGy) images showed similar quality to the FBP (2.47 mGy) image. Under the same dose conditions, the SNR and CNR were higher with DLIR-High than with FBP and all the IR methods (all P<0.05). The nodule volume and size with DLIR-High were significantly closer to the real volume than with FBP and all the IR methods (all P<0.001). Conclusions: DLIR can improve the image quality of LDCT compared to FBP and IR. In addition, the appropriate effective dose for LDCT would be 0.24 mGy with DLIR-High. ยฉ Quantitative Imaging in Medicine and Surgery. All rights reserved.ope
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