64 research outputs found

    ์ดˆ๋ฐœ ์ •์‹ ์ฆ ํ™˜์ž๊ตฐ์˜ ๋งˆ์Œ ์ด๋ก  ๋Šฅ๋ ฅ ์†์ƒ๊ณผ ๋ ๋‹ค๋ฐœ๊ณผ ์œ„์„ธ๋กœ๋‹ค๋ฐœ๊ณผ์˜ ์—ฐ๊ด€์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋‡Œ์ธ์ง€๊ณผํ•™๊ณผ,2019. 8. ๊ถŒ์ค€์ˆ˜.Deficit in Theory of Mind (ToM), the ability to infer others mental state, is considered as a core feature of schizophrenia (SCZ) evident since the prodromal stage of psychosis. Previous functional magnetic resonance imaging (fMRI) studies have suggested that abnormal activities among the regions comprising the mentalizing network are related to the observed ToM deficits. However, the structural connectivity underlying the functional network of ToM in SCZ remain unclear. To investigate the relation between white matter integrity and ToM deficits, diffusion tensor imaging (DTI) data of 35 patients with first-episode psychosis (FEP) and 29 matched healthy controls (HC) were analyzed via tract-based spatial statistics (TBSS). The acquired fractional anisotropy (FA) values of the two regions of interest (ROI) - cingulum and superior longitudinal fasciculus (SLF) - and ToM task scores of FEP went through correlation analysis and compared with that of HC. A positive correlation was found between the integrity of the left cingulum and ToM strange story score in patients with FEP. Also, the integrity of the left SLF was positively correlated with ToM strange story score in FEP. These results suggest the crucial roles of the cingulum and SLF in ToM deficits of SCZ. Our study is the first to demonstrate white matter connectivity underlying mentalizing network, as well as its relation to the behavioral outcome of social cognition in the early phase of SCZ.๋‹ค๋ฅธ ์‚ฌ๋žŒ๋“ค์˜ ์ •์‹  ์ƒํƒœ๋ฅผ ์ถ”๋ก ํ•˜๋Š” ๋Šฅ๋ ฅ์ธ ๋งˆ์Œ ์ด๋ก ์˜ ์†์ƒ์€ ์กฐํ˜„๋ณ‘ ํ™˜์ž์˜ ํ•ต์‹ฌ์  ํŠน์ง•์ด๋ฉฐ ์ด๋Š” ์ฆ์ƒ ๋ฐœ๋ณ‘ ์ด์ „ ๋‹จ๊ณ„์ธ ์ „๊ตฌ๊ธฐ๋ถ€ํ„ฐ ๊พธ์ค€ํžˆ ๊ด€์ฐฐ๋˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ด์ „์˜ ๊ธฐ๋Šฅ์  ์ž๊ธฐ๊ณต๋ช…์˜์ƒ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด mentalizing network์— ํ•ด๋‹นํ•˜๋Š” ์˜์—ญ๋“ค์˜ ํ™œ๋™ ์ด์ƒ์ด ๋งˆ์Œ ์ด๋ก  ๋Šฅ๋ ฅ ์†์ƒ์— ๊ด€๋ จ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ  ๋˜์—ˆ์œผ๋‚˜ ๋งˆ์Œ ์ด๋ก  ๋Šฅ๋ ฅ์„ ๋‹ด๋‹นํ•˜๋Š” ๊ธฐ๋Šฅ์  ๋„คํŠธ์›Œํฌ์˜ ๊ธฐ์ €๊ฐ€ ๋˜๋Š” ๋ฐฑ์งˆ ๊ตฌ์กฐ์˜ ์—ญํ• ์€ ์•„์ง ์กฐํ˜„๋ณ‘ ํ™˜์ž๊ตฐ์—์„œ ๋ฐํ˜€์ง„ ๋ฐ”๊ฐ€ ์—†๋‹ค. ์กฐํ˜„๋ณ‘ ํ™˜์ž์˜ ๋งˆ์Œ ์ด๋ก  ๋Šฅ๋ ฅ์— ๊ด€๋ จ๋œ ๋‡Œ ๋ฐฑ์งˆ ๊ตฌ์กฐ๋ฅผ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ์ดˆ๋ฐœ ์ •์‹ ์ฆ ํ™˜์ž 35๋ช…๊ณผ ์ •์ƒ ๋Œ€์กฐ๊ตฐ 29๋ช…์˜ ํ™•์‚ฐํ…์„œ์˜์ƒ์„ tract based spatial statistics (TBSS) ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜๊ณ  ๋‘๊ฐ€์ง€ ๊ด€์‹ฌ์˜์—ญ, ์ฆ‰ ๋ ๋‹ค๋ฐœ๊ณผ ์œ„์„ธ๋กœ๋‹ค๋ฐœ์˜ FA ๊ฐ’๊ณผ ๋งˆ์Œ ์ด๋ก  ๊ณผ์ œ ์ ์ˆ˜์™€์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ดˆ๋ฐœ ์ •์‹ ์ฆ ํ™˜์ž์˜ ์™ผ์ชฝ ๋ ๋‹ค๋ฐœ๊ณผ ๋งˆ์Œ ์ด๋ก  ๊ณผ์ œ ์ค‘ strange story์˜ ์ ์ˆ˜๊ฐ€ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ดˆ๋ฐœ ์ •์‹ ์ฆ ํ™˜์ž์˜ ์™ผ์ชฝ ์œ„์„ธ๋กœ๋‹ค๋ฐœ๋„ strange story ์ ์ˆ˜์™€ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์กฐํ˜„๋ณ‘ ํ™˜์ž์˜ ๋งˆ์Œ ์ด๋ก  ๋Šฅ๋ ฅ ์†์ƒ์— ๋ ๋‹ค๋ฐœ๊ณผ ์œ„์„ธ๋กœ๋‹ค๋ฐœ์ด ์ฃผ์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์ด ์ฆ๋ช…๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ดˆ๋ฐœ ์ •์‹ ์ฆ ํ™˜์ž๊ตฐ์—์„œ ๋ฐฑ์งˆ ๊ตฌ์กฐ์˜ ์™„์ „์„ฑ๊ณผ ๋งˆ์Œ ์ด๋ก  ์†์ƒ์˜ ๊ด€๋ จ์„ ๋ฐํžŒ ์ฒซ๋ฒˆ์งธ ์‹œ๋„์ด๋ฉฐ ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ ์‚ฌํšŒ์ธ์ง€์˜ ์†์ƒ์„ ๋ฐฑ์งˆ์„ ํ†ตํ•ด ์‚ดํŽด๋ณด๋Š” ์ ‘๊ทผ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ์ฆ๊ฑฐ๋ฅผ ์ œ๊ณตํ•œ๋‹ค.1. Introduction 1 2. Methods 5 3. Results 10 4. Discussion 12 References 20 Tables 36 Figures 38 Abstract in Korean 41Maste

    ์—ฐ๋ น์ฃผ์˜ ๊ด€์ ์— ๊ทผ๊ฑฐํ•œ ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ง€๋ฆฌํ•™๊ณผ, 2013. 8. ์†์ •๋ ฌ.โ”ƒ๊ตญ๋ฌธ ์ดˆ๋กโ”ƒ ๊ณ ๋ นํ™” ์‚ฌํšŒ(Aging Society)์— ์ ‘์–ด๋“  ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๋…ธ์ธ๋“ค์€ ์‹œ์žฅ๊ฒฝ์ œ ์›๋ฆฌ์— ๋”ฐ๋ฅธ ๋…ธ์ธ ์ฐจ๋ณ„์ ์ธ ๋ฐฐํƒ€์  ์—ฐ๋ น์ฃผ์˜(ageism against the aged) ์ธ์‹์ด ๋งŒ์—ฐํ•œ ์‚ฌํšŒ ์†์—์„œ ์‚ด์•„๊ฐ€๊ณ  ์žˆ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๋…ธ์ธ๋“ค์€ ์‹ ์ฒด์ ยท์ •์‹ ์  ๊ธฐ๋Šฅ ์‡ ์•ฝ๊ณผ ์‚ฌํšŒํ™œ๋™ ์ค‘๋‹จ์œผ๋กœ ์ธํ•ด ์ฃผ๊ฑฐ๊ณต๊ฐ„์—์„œ ๋ณด๋‚ด๋Š” ์‹œ๊ฐ„์ด ๋งŽ์•„์ง€๊ธฐ ๋•Œ๋ฌธ์— ๋…ธ์ธ ๊ฐ€๊ตฌ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•œ ๋…ธ์ธ๋ณต์ง€์ฃผํƒ์—์„œ์˜ ์ฃผ๊ฑฐ์ƒํ™œ์€ ์„ฑ๊ณต์ ์ธ ๋…ธํ›„์™€ ๋…ธ์ธ๋ณต์ง€์— ์ค‘์š”ํ•œ ๋ณ€์ˆ˜๋กœ ์ž‘์šฉํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๊ณ ๊ฐ€์˜ ๋ถ„์–‘๊ฐ€์™€ ์ƒํ™œ๋น„๊ฐ€ ์š”๊ตฌ๋˜๋Š” ๋…ธ์ธ๋ณต์ง€์ฃผํƒ์€ ๊ณ ์†Œ๋“ยท๊ณ ํ•™๋ ฅ์˜ ๋…ธ์ธ๋“ค์˜ ์ „์œ  ๊ณต๊ฐ„์œผ๋กœ ์กด์žฌํ•˜๊ณ  ์žˆ๊ณ , ๊ฑฐ์ฃผ๋ฏผ๋“ค์€ ๋ฐฐํƒ€์  ์—ฐ๋ น์ฃผ์˜์™€๋Š” ๋ฌด๊ด€ํ•œ ๋…ธ์ธ์ธ๊ตฌ ์ง‘๋‹จ์œผ๋กœ ๊ฐ„์ฃผ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ์ •๋œ ์ง‘๋‹จ๋งŒ์ด ์ ์œ  ๊ฐ€๋Šฅํ•œ ๋…ธ์ธ๋ณต์ง€์ฃผํƒ์˜ ๊ณต๊ฐ„ ์†์„ฑ์„ ์•Œ์•„๋ณด๊ณ , ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ์˜ ์ฃผ๊ฑฐ์ด๋™ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ์—ฐ๋ น์ฃผ์˜ ๊ด€์ ์„ ์ ์šฉํ•˜์—ฌ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹์— ๋ฏธ์นœ ์—ฐ๋ น์ฃผ์˜์˜ ์˜ํ–ฅ์„ ๊ทœ๋ช… ํ•ด ๋ณด๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ์„ ์—ฐ๊ตฌ ๋Œ€์ƒ์œผ๋กœ ์‚ผ๊ณ , ์„ค๋ฌธ์กฐ์‚ฌ์™€ ์ฃผ๊ด€์ ์ธ ๊ฑฐ์ฃผ๋ฏผ๋“ค์˜ ์ธ์‹ ๋„์ถœ์„ ์œ„ํ•ด Q ๋ฐฉ๋ฒ•๋ก ์„ ๋ถ„์„ ๋ฐฉ๋ฒ•์œผ๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ๋“ค์˜ ์ „๋ฐ˜์ ์ธ ์ฃผ๊ฑฐ์ด๋™ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ํƒ์ƒ‰ ๊ฒฐ๊ณผ 50% ์ด์ƒ์˜ ๋…ธ์ธ๋“ค์ด ์€ํ‡ด์ดํ›„ ๊ฒฝ์ œ์  ์—ฌ๊ฑด์ด ์•„๋‹Œ ํŽธ์˜์‹œ์„ค์˜ ๋‹ค์–‘์„ฑ๊ณผ ๊ฑด๊ฐ• ๋“ฑ์„ ์ฃผ๊ฑฐ ์†Œ๋น„ ๊ธฐ์ค€์œผ๋กœ ์šฐ์„ ์‹œ ํ•˜๊ณ  ์žˆ์—ˆ๊ณ , ์ผ์ƒ์ƒํ™œ ํ™œ๋™ ์˜์—ญ ๋˜ํ•œ ์ฃผ๊ฑฐ ๋‹จ์ง€ ๋‚ด๋กœ ํ™•์žฅ๋˜๋ฉด์„œ ์™ธ๋ถ€์™€์˜ ์†Œํ†ต์€ ์ƒ๋Œ€์ ์œผ๋กœ ์ค„์–ด๋“œ๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ์ด๋Š” ์ผ์ƒ์ƒํ™œ ์„œ๋น„์Šคยท์—ฌ๊ฐ€ ์„œ๋น„์Šคยท์˜๋ฃŒ ์„œ๋น„์Šค ๋“ฑ์„ ์ œ๊ณตํ•˜๋Š” ์ฃผ๊ฑฐ๊ณต๊ฐ„์˜ ๊ตฌ์„ฑ์— ๋”ฐ๋ฅธ ๊ฒฐ๊ณผ๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๊ณ , ์ด๋Ÿฌํ•œ ๊ณต๊ฐ„ ์†Œ๋น„๊ฐ€ ๊ฐ€๋Šฅํ•œ ์ง‘๋‹จ์€ ์‘๋‹ต์ž์˜ 90%๊ฐ€ ๋Œ€์กธ ์ด์ƒ์˜ ํ•™๋ ฅ๊ณผ ์•ฝ 75%์˜ ์‘๋‹ต์ž๊ฐ€ ์ƒ์ธต๏ฝž์ค‘์ƒ์ธต์˜ ์ƒํ™œ์ˆ˜์ค€์„ ์œ ์ง€ํ•˜๊ณ  ์žˆ๋‹ค๊ณ  ์‘๋‹ตํ•จ์œผ๋กœ์จ ๊ณ ํ•™๋ ฅยท๊ณ ์†Œ๋“์— ํ•ด๋‹นํ•˜๋Š” ์‚ฌํšŒยท๊ฒฝ์ œ์  ์ง€์œ„๊ฐ€ ๋†’์€ ๋…ธ์ธ๋“ค๋กœ ํ•œ์ •๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๋…ธ์ธ์ธ๊ตฌ ์ง‘๋‹จ ๋‚ด์—์„œ ์ƒ์œ„์˜ ์ง‘๋‹จ์œผ๋กœ ๊ฐ„์ฃผํ•  ์ˆ˜ ์žˆ๋Š” ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ์˜ ์ฃผ๊ฑฐ์ด๋™ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ์—ฐ๋ น์ฃผ์˜ ๊ด€์ ์„ ์ ์šฉํ•˜์—ฌ Q ๋ถ„์„์„ ์‹คํ–‰ํ•œ ๊ฒฐ๊ณผ ์‘๋‹ต์ž๋“ค์€ ๋™์ผํ•œ ์ฃผ๊ฑฐ๊ณต๊ฐ„์„ ์ ์œ ํ•˜์ง€๋งŒ ๊ฐ๊ธฐ ๋‹ค๋ฅธ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹์˜ ์ฐจ์ด๋Š” ์ฃผ๊ฑฐ์ด๋™ ์ด์ „๊ณผ ์ดํ›„์— ์ธ์ง€ํ•œ ์‘๋‹ต์ž๋“ค์˜ ์—ฐ๋ น์ฃผ์˜ ์ธ์‹์— ์˜ํ•ด ๋ฐœ์ƒํ•˜์˜€๋‹ค. ์‘๋‹ต์ž๋“ค์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹์€ ํฌ๊ฒŒ ๋„ค ๊ฐ€์ง€๋กœ ๊ตฌ๋ถ„ ๋˜์—ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ง‘๋‹จ์€ ๋…ธํ™”ํ˜„์ƒ์— ์‹ ์ฒด์ ยท์ •์‹ ์  ๊ธฐ๋Šฅ์ €ํ•˜์™€ ์‹ฌ๋ฆฌ์  ๋ฐ•ํƒˆ๊ฐ์„ ์Šค์Šค๋กœ ์กฐ์ ˆํ•˜๊ธฐ ์œ„ํ•ด ๋…ธ์ธ๋ณต์ง€์ฃผํƒ์œผ๋กœ์˜ ์ฃผ๊ฑฐ์ด๋™์„ ๊ฐํ–‰ํ•œ ๋…ธ์ธ๋“ค๋กœ์จ, ์ฃผ๊ฑฐ๊ณต๊ฐ„์„ ๋…ธํ™”ํ˜„์ƒ์— ๋Œ€์ฒ˜ํ•˜๊ธฐ ์œ„ํ•œ ๊ณต๊ฐ„์œผ๋กœ ์ธ์‹ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ง‘๋‹จ์€ ๋…ธ์ธ์„ ๋ฐ”๋ผ๋ณด๋Š” ๋ฐฐํƒ€์  ์—ฐ๋ น์ฃผ์˜์˜ ๋Œ€ํ•˜์—ฌ ์ถฉ๋ถ„ํžˆ ์ธ์ง€ํ•˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋…ธํ™”๊ฐ€ ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ์„œ ์—ฐ๋ นยท์ž์‚ฐยท๊ฐ€์น˜๊ด€ ๋“ฑ์ด ์œ ์‚ฌํ•œ ์‚ฌ๋žŒ๋“ค๊ณผ ๋ชจ์ž„์œผ๋กœ์จ ๋†’์€ ์‚ถ์˜ ์งˆ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์ฃผ์žฅํ•˜์˜€๋‹ค. ๋•Œ๋ฌธ์— ์ด๋“ค์€ ์ฃผ๊ฑฐ์ด๋™ ์ดํ›„์—๋„ ํ˜ผํ•ฉ์—ฐ๋ น(mix-age)ํ™œ๋™์„ ์ ๊ทน์ ์œผ๋กœ ๊ฑฐ๋ถ€ํ•˜๋ฉด์„œ ์™ธ๋ถ€์— ๋Œ€ํ•œ ๋ฐฐํƒ€์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ์—ฐ๋ น์ฃผ์˜๋กœ๋ถ€ํ„ฐ ๋ถ„๋ฆฌ๋œ ๊ณต๊ฐ„์œผ๋กœ ์ฃผ๊ฑฐ๊ณต๊ฐ„์„ ์ธ์‹ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์„ธ ๋ฒˆ์งธ ์ง‘๋‹จ์€ ๋…ธ์ธ๋ณต์ง€์ฃผํƒ์„ ์—ฐ๋ น์ฃผ์˜๋ฅผ ์ง€์†์‹œํ‚ค๋Š” ๊ณต๊ฐ„์œผ๋กœ ์ธ์‹ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์ด๋“ค์˜ ์ฃผ๊ฑฐ์ด๋™ ์ด‰๋ฐœ์€ ์ด์ „ ๊ฑฐ์ฃผ์ง€์—์„œ์˜ ๋ฐฐํƒ€์  ์—ฐ๋ น์ฃผ์˜ ๊ฒฝํ—˜์— ์˜ํ•ด ์ด๋ฃจ์–ด์กŒ์œผ๋‚˜, ์ฃผ๊ฑฐ๊ณต๊ฐ„ ๋ณ€ํ™”๊ฐ€ ๋…ธ์ธ๋“ค์—๊ฒŒ ํ˜•์„ฑ๋œ ๋…ธ์ธ ์ฐจ๋ณ„์  ์—ฐ๋ น์ฃผ์˜๋ฅผ ์—†์• ๋Š” ์ž‘์šฉ์„ ํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๋…ธ์ธ๋“ค์€ ์ฃผ๊ฑฐ์ด๋™ ์ดํ›„์—๋„ ์—ฌ์ „ํžˆ ๋ถˆ์•ˆ๊ฐ๊ณผ ์†Œ์™ธ๊ฐ์—์„œ ๋ฒ—์–ด๋‚˜์ง€ ๋ชปํ•˜๊ณ , ๋˜ ๋‹ค์‹œ ๋‹ค๋ฅธ ์žฅ์†Œ์—์„œ ๋‹ค์–‘ํ•œ ์ง‘๋‹จ๊ณผ์˜ ์†Œํ†ต์„ ํ†ตํ•ด ๋‚˜์€ ์ƒํ™œ์„ ์˜์œ„ํ•  ๊ฒƒ์„ ๊ธฐ๋Œ€ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰ ์ง‘๋‹จ์€ ์ Š์Œ๊ณผ ๋…ธํ™”์— ๋Œ€ํ•œ ์‹ ์ฒดยท์‹ฌ๋ฆฌ์  ๊ฒฉ์ฐจ๋ฅผ ์ธ์ •ํ•˜๋Š” ๋™์‹œ์— ๋‹ค์–‘ํ•œ ์‚ฌ๋žŒ๋“ค๊ณผ์˜ ๊ต๋ฅ˜๋ฅผ ํ†ตํ•ด์„œ ์‚ถ์˜ ์งˆ์„ ์œ ์ง€ยทํ–ฅ์ƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋Š” ๋…ธ์ธ๋“ค์ด์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋น„์ž๋ฐœ์  ์ฃผ๊ฑฐ์ด๋™์— ์˜ํ•ด ์ฃผ๊ฑฐ์ด๋™ ์ดํ›„, ๋…ธ์ธ๋ณต์ง€์ฃผํƒ์˜ ์ƒˆ๋กœ์šด ์ผ์ƒ์ƒํ™œ ๊ทœ๋ฒ”๋“ค์— ์˜ํ•ด ์—ฐ๋ น์ฃผ์˜๋ฅผ ๊ฒฝํ—˜ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋“ค์€ ์ฃผ๊ฑฐ๊ณต๊ฐ„์„ ์—ฐ๋ น์ฃผ์˜๋ฅผ ์œ ๋ฐœํ•˜๋Š” ๊ณต๊ฐ„์œผ๋กœ ์ธ์‹ํ•œ ์ง‘๋‹จ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ๋…ธ์ธ์ธ๊ตฌ์ง‘๋‹จ ๋‚ด ํ‰๊ท  ์ด์ƒ์˜ ์†Œ๋“๊ณผ ํ•™๋ ฅ์„ ๊ฐ€์ง„ ๋…ธ์ธ๋“ค์„ ๋Œ€๋ณ€ํ•˜๋Š” ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ๋“ค์˜ ์ฃผ๊ฑฐ์ด๋™ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ์—ฐ๋ น์ฃผ์˜ ๊ด€์ ์„ ์ ์šฉํ•˜์—ฌ ๊ทธ ์˜ํ–ฅ์„ ์ž…์ฆ ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ๊ฐœ๊ฐœ์ธ์˜ ์ฃผ๊ฑฐ์ด๋™ ์ด‰๋ฐœ์š”์ธ๊ณผ ๊ฐ€์น˜๊ด€์— ๋”ฐ๋ผ์„œ ์ฃผ๊ฑฐ๊ณต๊ฐ„์— ์—ฐ๋ น์ฃผ์˜์— ๋Œ€ํ•œ ์ธ์‹์„ ๊ฐ๊ธฐ ๋‹ค๋ฅธ ๋ฐฉ์‹์œผ๋กœ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์ฃผ๊ฑฐ๊ณต๊ฐ„์„ ๋น„๋กฏํ•œ ๋…ธ์ธ๋“ค์„ ์œ„ํ•œ ๊ณต๊ฐ„ ํ™˜๊ฒฝ ๊ตฌ์„ฑ ์‹œ ๋…ธ์ธ๋“ค์ด ๊ธฐ์กด์— ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ณต๊ฐ„์ธ์‹์— ๋Œ€ํ•œ ์‹ฌ์ธต์  ํƒ์ƒ‰ ์—†์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋Š” ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ์ •์ฑ…์— ๊ฒฝ์ข…์„ ์šธ๋ฆฌ๋Š” ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค๋Š” ๋ฐ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ฃผ์š”์–ด: ์—ฐ๋ น์ฃผ์˜, ๋…ธ์ธ๋ณต์ง€์ฃผํƒ, ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹, ์ƒ์œ„ ๋…ธ์ธ์ธ๊ตฌ ์ง‘๋‹จ ํ•™๋ฒˆ: 2011-23143โ”ƒABSTRACTโ”ƒ Residential space perception of Elderly welfare housing dwellers based on Ageism Nari Kim Department of Geography Graduate School Seoul National University In an increasingly aging South Korean society, the elderly are living in a society dominated by differentiating and exclusive ageism against the aged according to the principles of market economy. They spend more time in residential space due to their weakened physical and mental functions and suspension of social activities. So, Elderly welfare housing reflecting the characteristics of elderly households becomes an important variable for successful old age and elderly welfare. However, elderly welfare housing, which asks for high prices and living expenses, is exclusive to the elderly of high incomes and educational backgrounds. Its residents are considered to be an elderly group that has nothing to do with exclusive ageism. This study thus set out to investigate the spatial attributes of elderly welfare housing currently occupied only by a restricted group, apply the perspective of ageism to the residential move mechanism of elderly welfare housing dwellers, and examine the effects of ageism on the residential space perception. For those purposes, the study selected a group of subjects among the dwellers of elderly welfare housing and used the Q methodology as an analysis method for subjective resident perceptions. The research findings were as follows: First, the examination results of the overall residential move mechanism among the dwellers of elderly welfare housing show that more than 50% of the elderly change from "economic conditions" to "diversity of convenience facilities & health" after retirement as the criteria of residence consumption. The areas of their daily life activities expanded within the housing complex with their communication with the outside world decreasing relatively, which is attributed to the organization of residential space providing daily life, leisure, and medical service. Since 90% of the respondents graduated from college or higher educational institution and approximately 75% respondents said that they maintained an upper to upper middle level of living standard, the consumption of such residential space was restricted to the elderly of high educational backgrounds and incomes and high social and economic status. Secondly, Q analysis was conducted by applying the perspective of ageism to the residential move mechanism of elderly welfare housing dwellers that were regarded as the upper group in the elderly population. The analysis results reveal that the respondents occupied the same residential space but had different perceptions of residential space. Those differences were attributed to their perceptions of ageism before and after a residential move. Their residential space perceptions were categorized into four groups: the first group moved to the elderly welfare housing to regulate their deteriorated physical and mental functions and psychological deprivation according to the aging phenomenon and regarded residential space as "space to deal with the aging phenomenon." The second group insisted that they could maintain a high quality of life by gathering with people of similar age, assets, and values with age, because they were already fully aware of exclusive ageism against the old. So, they regarded residential space as "space separated from ageism" for them to actively refuse mixed-age activities and keep their exclusivity against the outside world after a residential move. The third group regarded elderly welfare housing as "space to maintain ageism." Their residential move was triggered by their experiences of exclusive ageism in their old residence, but the change of residential space did not eliminate differentiating ageism against what had formed in their consciousness. As a result, they still had anxiety and sense of alienation even after a residential move and anticipated that they would lead a better life by communicating with various groups at another place. The final group recognized physical and mental gaps between youth and aging and believed that they could maintain or enhance quality of life by exchanging with various people. They, however, experienced ageism due to the new daily life rules of elderly welfare housing after a residential move and accordingly regarded residential space as "space to cause ageism." The study applied the perspective of ageism to the residential move mechanism of elderly welfare housing dwellers, who represented the elderly population of higher incomes and educational backgrounds rather than the average and demonstrated its effects. The study found that they reflected their perceptions of ageism onto their residential space in different ways according to their causes of residential moves and values. The study is significant in that its results sound an alarm to the elderly welfare housing policies that organize a spatial environment for the elderly including residential space without any in-depth inquiry into the existing space perceptions of the elderly. Keywords: ageism, elderly welfare housing, residential space perception , the upper elderly group Student number: 2011-23143โ”ƒ๋ชฉ ์ฐจโ”ƒ โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ๋ฒ”์œ„์™€ ๋ฐฉ๋ฒ• 4 1) ์—ฐ๊ตฌ์ง€์—ญ๊ณผ ์—ฐ๊ตฌ๋Œ€์ƒ 5 2) ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 10 3. ์—ฐ๊ตฌ์˜ ํ๋ฆ„๊ณผ ๊ตฌ์„ฑ 13 โ…ก. ์—ฐ๊ตฌ์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 15 1. ์—ฐ๋ น์ฃผ์˜(Ageism)์˜ ๊ฐœ๋…๊ณผ ๊ธฐ๋Šฅ ๋ฐ ์œ ์šฉ์„ฑ 15 1) ์—ฐ๋ น์ฃผ์˜์˜ ๊ฐœ๋…๊ณผ ๊ธฐ๋Šฅ 15 2) ์—ฐ๋ น์ฃผ์˜ ์ธก์ •๋ฐฉ๋ฒ• 20 3) ์—ฐ๋ น์ฃผ์˜์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 25 2. ๋…ธ์ธ์ธ๊ตฌ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹๊ณผ ์ฃผ๊ฑฐํ–‰ํƒœ 28 1) ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹๊ณผ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹ ๋ณ€ํ™” 28 2) ๋…ธ์ธ์ธ๊ตฌ์ง‘๋‹จ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„๊ณผ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹ 30 3) ๋…ธ์ธ์ธ๊ตฌ์˜ ์ฃผ๊ฑฐํ–‰ํƒœ ํŠน์„ฑ 33 4) ๋…ธ์ธ์˜ ์ฃผ๊ฑฐ์ด๋™๊ณผ ๋…ธ์ธ์ฃผ๊ฑฐ์„ ํƒ ํŠน์„ฑ 40 3. ์ง€๋ฆฌํ•™์—์„œ Q ๋ฐฉ๋ฒ•๋ก  ํ™œ์šฉ 42 โ…ข. ๋…ธ์ธ๊ณต๋™์ƒํ™œ์ฃผํƒ์˜ ์ œ๋„์  ํŠน์„ฑ 44 1. ๋…ธ์ธ๊ณต๋™์ƒํ™œ์ฃผํƒ ๋“ฑ์žฅ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ 44 2. ๋…ธ์ธ ์ฃผ๊ฑฐ๋ณต์ง€์˜ ๊ตญ๊ฐ€๋ณ„ ์ •์ฑ… ๋ณ€์ฒœ 46 1) ์„ ์ง„๊ตญ์˜ ๋…ธ์ธ๊ณต๋™์ƒํ™œ์ฃผํƒ ์œ ํ˜•๊ณผ ์ •์ฑ… ๋ณ€ํ™” 46 2) ๊ตญ๋‚ด ๋…ธ์ธ์ฃผ๊ฑฐ๋ณต์ง€์‹œ์„ค์˜ ์œ ํ˜•๊ณผ ์ •์ฑ… ๋ณ€ํ™” 53 โ…ฃ. ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ์˜ ์ฃผ๊ฑฐํ–‰ํƒœ ๋ถ„์„ 58 1. ์ธ๊ตฌํ•™์  ํŠน์„ฑ 58 2. ์ฃผ๊ฑฐ์ด๋™(residential mobility) ํ–‰ํƒœ 62 3. ์ฃผ๊ฑฐํ–‰ํƒœ(residential behavior) ๋ณ€ํ™” 68 1) ์ฃผ๊ฑฐ์†Œ๋น„ ํ–‰ํƒœ ๋ณ€ํ™” 68 2) ์ฃผ๊ฑฐ์ƒํ™œ ํ–‰ํƒœ ๋ณ€ํ™” 70 4. ์†Œ๊ฒฐ 78 โ…ค. ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ๊ฑฐ์ฃผ๋ฏผ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹ 80 1. ์—ฐ๋ น์ฃผ์˜ ๊ด€์ ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹ Q ์ง„์ˆ ๋ฌธ 82 2. ์—ฐ๋ น์ฃผ์˜ ๊ด€์ ์˜ ์ฃผ๊ฑฐ๊ณต๊ฐ„์ธ์‹ ์œ ํ˜• ๊ตฌ๋ถ„ 85 3. ๋…ธ์ธ๋ณต์ง€์ฃผํƒ ์ฃผ๊ฑฐ๊ณต๊ฐ„์— ๋Œ€ํ•œ ์œ ํ˜•๋ณ„ ์ธ์‹ ํ•ด์„ 90 1) ์ œ 1์œ ํ˜•: ๋…ธํ™”ํ˜„์ƒ์— ๋Œ€์ฒ˜ํ•˜๊ธฐ ์œ„ํ•œ ๊ณต๊ฐ„ 92 2) ์ œ 2์œ ํ˜•: ์—ฐ๋ น์ฃผ์˜๋กœ๋ถ€ํ„ฐ ๋ถ„๋ฆฌ๋œ ๊ณต๊ฐ„ 96 3) ์ œ 3์œ ํ˜•: ์—ฐ๋ น์ฃผ์˜๋ฅผ ์ง€์†์‹œํ‚ค๋Š” ๊ณต๊ฐ„ 100 4) ์ œ 4์œ ํ˜•: ์—ฐ๋ น์ฃผ์˜๋ฅผ ์œ ๋ฐœํ•˜๋Š” ๊ณต๊ฐ„ 103 4. ์†Œ๊ฒฐ 107 โ…ฅ. ๊ฒฐ๋ก  ๋ฐ ์š”์•ฝ 110 ์ฐธ๊ณ ๋ฌธํ—Œ 113 ๊ตญ๋‚ด ๋ฌธํ—Œ 113 ๊ตญ์™ธ ๋ฌธํ—Œ 115 ๋‹จํ–‰๋ณธ๊ณผ ๋ณด๊ณ ์„œ 120 ๋ถ€๋ก 121 Abstract 128Maste

    ํ•œ๊ตญ์‚ฐ์—…์˜ ๊ฒฝ์ œ์  ๊ทœ์ œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ํ–‰์ •ํ•™๊ณผ(ํ–‰์ •ํ•™์ „๊ณต), 2021. 2. ๊ถŒ์ผ์›….Todayโ€™s world is surrounded by various kinds of regulations. Numerous studies have been conducted investigating the influence of regulations, but there is a lack of researches that empirically analyze the theories on why we have regulations. In addition, there are previous studies that classify regulations by types based on various criteria, but there is limited literature on why such types of regulations are created, and what factors influence the creation of these types of regulations. The main theories that explain the reasons for the existence of regulations are the public interest theory, the capture theory, and the general theory that combines the two. The theory of public interest argues that government interventions in the form of regulations exist in order to amend market failure. On the contrary, capture theory has the view that the government introduces regulations by being โ€œcapturedโ€ by the group of producers. These two theories are representative theories related to regulation. However, it is difficult to find a study that empirically verifies whether these two theories can properly reflect the Korean reality. When looking into the Korean regulation literature, the theory of excessive or cutthroat competition is often mentioned. This theory says that the government introduces regulations to prevent competition from overheating. According to previous studies, the logic behind the introduction of regulation based on the theory of excessive competition seems to be still controversial, and it appears that the negative views on such government intervention are dominant. It is important to understand how specific regulations affect the country and industries. However, if we understand what circumstances create regulations and what influences the creation of specific types of regulations, we can clearly explain the reasons for their existence and the effects we want to achieve through these regulations. In particular, if it is possible to empirically define which characteristics of an industry influence the creation of regulations based on industrial statistical data, it will be easier to identify the types of regulations that are created more easily in the industry simply by looking into the characteristics of the industry. Such an analysis can help governments introduce regulations that may be more effective in a particular industry depending on their specific characteristics. In addition, when analyzing existing and newly developing industries, it is possible to predict in advance what regulations will be required by looking into the features of the industry. This kind of prediction can be useful in policy design. Therefore, this study established a research framework on the relationship between competition and regulation based on the regulation theories, and analyzed it empirically using Korean regulatory and industrial data. First, this paper analyzed how the degree of competition, which is one of the characteristics of the industry, affects the creation of regulations. In addition, it verified whether the employment level of the industry has a controlling effect on this relationship. Second, by further categorizing the degree of competition, the effect of the degree of competition in the industry on the regulation was analyzed in more precise and comprehensive manner. Third, using actual Korean regulatory data, the public interest theory, the capture theory and the excessive competition theory were compared and analyzed on the equal basis. Fourth, an empirical analysis was conducted using the data of the entire Korean industry to have general implications beyond one specific industry. Fifth, to verify each theory in more detail, not only the total number of regulations but the number and percentage of each type of regulation were additionally used. As a result of analyzing three models designed to understand the relationship between the change in the degree of competition and regulations in the industry, no results were obtained in support of the theory of public interest. The derived results were in support of the capture theory 1 and 2 and the theory of excessive competition. Based on these results, it was concluded that the regulations introduced in the Korean industry can be explained by the capture theory and the theory of excessive competition. This suggests that Korean regulations can be introduced both by the companiesโ€™ or governmentโ€™s initiative. But in case of governmentโ€™s initiative, it is peculiar that the excessive competition theory better explains this phenomenon than the public interest theory. While the efforts of companies to capture regulatory authorities and introduce regulations are rather natural, it is somewhat cautious to evaluate the fact that the government is active in regulating the intensification of competition. The introduction of regulations based on the theory of excessive competition follows the logic that by preventing the excessive market entry, the welfare of society can be improved. There are already some previous studies that sees this logic problematic, and there are many negative views on such government intervention. The fact that this empirical result can explain the current state of regulation in Korean industry through the theory of excessive competition shows that the Korean government has a strong tendency to take the lead in controlling the market. This study also shows that the Korean government are more concerned about excessive competition than monopoly. This result has a great importance as it derives a peculiarity in the regulation of Korean industry. It shows that the government has a strong will to prevent excessive competition by introducing regulations. In this study, a regulation database was constructed using a vast amount of Korean regulatory data that was not utilized in previous studies. The regulatory data were additionally categorized by regulation types. Thanks to this, it was possible to verify the existing regulatory theories in detail. In addition, the analysis was conducted by matching regulatory data to each industry. Through this, it contributed to expanding the scope of implications that can be obtained by verifying the existing regulatory theories. Finally, showing that the degree of competition in the industry influences the creation of regulations using overall industry data provides important implications. It is particularly vital that the degree of competition was analyzed by categorizing it into three different stages, so that the concept of competition could be dealt more comprehensively and in more detail. Whereas the previous studies analyzed the effect of regulation on the degree of competition in an industry, this study further expanded research on the relationship between competition and regulation by revealing that the degree of competition has an effect on regulation. In addition, it is also meaningful to find that the level of employment can control the relationship between the degree of competition and regulation. This shows that another characteristic of the industry can strengthen or weaken the relationship between the level of competition and regulation of the industry. Therefore, it implies that various characteristics of an industry must be considered when establishing regulatory policies. Above all, this study is significant that it analyzes which regulatory theory can better explain the relationship between the degree of competition and regulation. Due to this, it is possible to predict what kind of regulations can be created just by grasping the degree of competition in an industry. In addition, it can be helpful for short-term and long-term policy formulation as it is possible to know the background and purpose why the regulation was created. However, these research results have certain limitations because the regulatory data used in the analysis are limited to currently valid regulations. Although methodologically, there was an attempt to overcome the limitations of single-year regulatory data, there is a high possibility that the results obtained using the actual historical data will deviate from the results obtained in this study. Unfortunately, as of now, Korea's past regulatory data are not openly managed or available to the public. As neither the entire nor the types of regulation lists are accessible, there are some limitations in expanding the results from this study. Furthermore, a follow-up study is needed to analyze the impact of each type of regulation on a specific industry to find out more suitable regulatory types for each industry. Hopefully, the results and methodology of this study will serve as the basis for regulatory reform in the future and provide directions for identifying, developing and introducing more appropriate regulations for each industry.๊ทœ์ œ๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ์‹์œผ๋กœ ์šฐ๋ฆฌ์˜ ์ƒํ™œ ์†์— ํ•จ๊ป˜ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ ๋™์•ˆ ๊ทœ์ œ์˜ ์˜ํ–ฅ๋ ฅ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋‹ค์ˆ˜ ์ด๋ฃจ์–ด์กŒ์œผ๋‚˜, ์™œ ๊ทœ์ œ๊ฐ€ ์ƒ๊ธฐ๋Š”์ง€์— ๋Œ€ํ•œ ์ด๋ก ์„ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ๋˜ํ•œ ๊ทœ์ œ๋ฅผ ๋‹ค์–‘ํ•œ ๊ธฐ์ค€์— ์˜ํ•ด ์œ ํ˜•๋ณ„๋กœ ๋ถ„๋ฅ˜ํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ์กด์žฌํ•˜๋‚˜, ์™œ ๊ทธ๋Ÿฌํ•œ ์œ ํ˜•์˜ ๊ทœ์ œ๊ฐ€ ์ƒ๊ธฐ๋Š”์ง€, ๊ทธ๋ฆฌ๊ณ  ์–ด๋– ํ•œ ํ™˜๊ฒฝ์ด๋‚˜ ์š”์ธ์ด ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ๊ทœ์ œ๋ฅผ ๋งŒ๋“œ๋Š” ๋ฐ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š”์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๋ถ€์กฑํ•˜๋‹ค. ๊ทœ์ œ์˜ ์กด์žฌ ์ด์œ ์™€ ๊ด€๋ จ๋œ ์ด๋ก ์€ ํฌ๊ฒŒ ๊ณต์ต์ด๋ก , ํฌํš์ด๋ก  ๊ทธ๋ฆฌ๊ณ  ์ด ๋‘˜์„ ์กฐํ•ฉํ•œ ์ผ๋ฐ˜์ด๋ก ์ด ์žˆ๋‹ค. ๊ณต์ต์ด๋ก ์€ ์‹œ์žฅ์‹คํŒจ๋ฅผ ๊ต์ •ํ•˜๊ธฐ ์œ„ํ•ด ๊ทœ์ œ๋ฅผ ํ™œ์šฉํ•ด์„œ ์ •๋ถ€์˜ ๊ฐœ์ž…์ด ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด์™€ ๋ฐ˜๋Œ€๋กœ, ํฌํš์ด๋ก ์€ ์ƒ์‚ฐ์ž ์ง‘๋‹จ์˜ ์š”๊ตฌ์— ์˜ํ•ด ์ •๋ถ€๊ฐ€ โ€˜ํฌํšโ€™๋˜์–ด ๊ทœ์ œ๋ฅผ ๋„์ž…ํ•œ๋‹ค๋Š” ๊ด€์ ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋‘ ๊ฐ€์ง€ ์ด๋ก ์€ ๊ทœ์ œ์™€ ๊ด€๋ จ๋œ ๋Œ€ํ‘œ์ ์ธ ์ด๋ก ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๋‘ ์ด๋ก ์ด ์šฐ๋ฆฌ๋‚˜๋ผ ํ˜„์‹ค์— ์‹ค์ œ ์ ์šฉ๋˜๋Š”์ง€ ์—ฌ๋ถ€๋ฅผ ๊ตญ๋‚ด ์ž๋ฃŒ๋กœ ์‹ค์ฆ ๊ฒ€์ฆํ•œ ์—ฐ๊ตฌ๋Š” ์ฐพ์•„๋ณด๊ธฐ ์–ด๋ ค์šด ํ˜„์‹ค์ด๋‹ค. ํ•œ๊ตญ์˜ ๊ทœ์ œ์— ๋Œ€ํ•œ ์ด๋ก ๋“ค์„ ์‚ดํŽด๋ณด๋ฉด, ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์ด ๋งŽ์ด ์–ธ๊ธ‰์ด ๋œ๋‹ค. ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์€ ๊ฒฝ์Ÿ์ด ๊ณผ์—ด๋˜๋Š” ๊ฒƒ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ์ •๋ถ€๊ฐ€ ๊ทœ์ œ๋ฅผ ๋„์ž…ํ•œ๋‹ค๊ณ  ๋ณด๋Š” ๊ฒƒ์ด๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ์— ์˜ํ•˜๋ฉด, ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์— ์˜ํ•œ ๊ทœ์ œ๋„์ž…์— ๋Œ€ํ•œ ๋…ผ๋ฆฌ์  ๊ทผ๊ฑฐ์— ๋Œ€ํ•ด์„œ๋Š” ์•„์ง ๋…ผ๋ž€์˜ ์—ฌ์ง€๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ์ •๋ถ€์˜ ๊ฐœ์ž…์— ๋Œ€ํ•œ ๋ถ€์ •์ ์ธ ์‹œ๊ฐ์ด ๋” ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ํŠน์ • ๊ทœ์ œ๊ฐ€ ๊ตญ๊ฐ€์™€ ์‚ฐ์—…์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š”์ง€์— ๋Œ€ํ•ด ์ดํ•ด๋ฅผ ํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์ด์— ์•ž์„œ ์–ด๋– ํ•œ ํ™˜๊ฒฝ์ด ๊ทœ์ œ๋ฅผ ๋งŒ๋“œ๋Š”์ง€, ๊ทธ๋ฆฌ๊ณ  ๋” ๋‚˜์•„๊ฐ€ ์–ด๋– ํ•œ ์ƒํ™ฉ์ด ํŠน์ •ํ•œ ์œ ํ˜•์˜ ๊ทœ์ œ๋ฅผ ๋งŒ๋“œ๋Š” ์ง€์— ๋Œ€ํ•ด ์ดํ•ด๋ฅผ ํ•  ์ˆ˜ ์žˆ๋‹ค๋ฉด ํ•ด๋‹น ๊ทœ์ œ์˜ ์กด์žฌ ์ด์œ ์™€ ํ•ด๋‹น ๊ทœ์ œ๋ฅผ ํ†ตํ•ด ์–ป๊ณ ์ž ํ•˜๋Š” ํšจ๊ณผ๋ฅผ ๋” ๋ช…ํ™•ํ•˜๊ฒŒ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ํŠนํžˆ, ์‚ฐ์—… ํ†ต๊ณ„ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‚ฐ์—…์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ๋“ค์ด ๊ทœ์ œ ์ƒ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๊ฒƒ์„ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋‹ค๋ฉด, ์‚ฐ์—…์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ์„ ์ž˜ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ํ†ตํ•ด ํ•ด๋‹น ์‚ฐ์—…์—์„œ ๋” ์‰ฝ๊ฒŒ ์ƒ์„ฑ๋˜๋Š” ๊ทœ์ œ์˜ ์ข…๋ฅ˜๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ๊ฐ€ ์šฉ์ดํ•˜๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„์€ ์ •๋ถ€๊ฐ€ ํ•ด๋‹น ์‚ฐ์—…์— ๋” ํšจ๊ณผ์ ์ผ ์ˆ˜ ์žˆ๋Š” ๊ทœ์ œ๋ฅผ ๋„์ž…ํ•˜๋Š”๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ธฐ์กด ์‚ฐ์—…๋ฟ๋งŒ์ด ์•„๋‹ˆ๋ผ ์ƒˆ๋กญ๊ฒŒ ๋ฐœ์ „ํ•˜๋Š” ์‚ฐ์—…์„ ๋ถ„์„ํ•  ๋•Œ์—๋„, ๊ทธ ์‚ฐ์—…์˜ ํŠน์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์–ด๋– ํ•œ ๊ทœ์ œ๊ฐ€ ํ•„์š”ํ• ์ง€ ๋ฏธ๋ฆฌ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์–ด์„œ ์ •์ฑ…์ˆ˜๋ฆฝ์— ํ™œ์šฉ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ทœ์ œ์ด๋ก ์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฒฝ์Ÿ๊ณผ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๋ถ„์„ํ‹€์„ ์„ธ์šฐ๊ณ , ์ด๋ฅผ ํ•œ๊ตญ ๊ทœ์ œ ๋ฐ์ดํ„ฐ์™€ ์‚ฐ์—… ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ•ด๋ณด์•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ฒซ์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฐ์—…์˜ ํŠน์„ฑ ์ค‘ ํ•˜๋‚˜์ธ ๊ฒฝ์Ÿ์ •๋„๊ฐ€ ๊ทœ์ œ ์ƒ์„ฑ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ๊ด€๊ณ„๋ฅผ ์‚ฐ์—…์˜ ๊ณ ์šฉ์ˆ˜์ค€์ด ์กฐ์ ˆํ•˜๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ๋Š”์ง€๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๊ฒฝ์Ÿ์ •๋„๋ฅผ ๋” ์„ธ๋ถ„ํ™”ํ•˜์—ฌ ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„๊ฐ€ ๊ทœ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋”์šฑ ๊ตฌ์ฒด์ ์ด๊ณ  ํฌ๊ด„์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์…‹์งธ, ์‹ค์ œ ํ•œ๊ตญ ๊ทœ์ œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณต์ต์ด๋ก , ํฌํš์ด๋ก , ๊ทธ๋ฆฌ๊ณ  ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์„ ๋™์ผํ•œ ๊ธฐ์ค€์œผ๋กœ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค. ๋„ท์งธ, ํŠน์ • ์‚ฐ์—…์ด ์•„๋‹ˆ๋ผ ์ „์ฒด ํ•œ๊ตญ ์‚ฐ์—… ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹ค์ฆ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋‹ค์„ฏ์งธ, ๊ฐ ์ด๋ก ์„ ๋” ์„ธ๋ถ€์ ์œผ๋กœ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์ „์ฒด ๊ทœ์ œ ๊ฐœ์ˆ˜๋ฟ๋งŒ์ด ์•„๋‹ˆ๋ผ ์œ ํ˜•๋ณ„ ๊ทœ์ œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„ ๋ณ€ํ™”์™€ ๊ทœ์ œ์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด 3๊ฐœ์˜ ๋ชจํ˜•์„ ์„ค์ •ํ•˜์—ฌ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๊ณต์ต์ด๋ก ์„ ์ง€์ง€ํ•˜๋Š” ๊ฒฐ๊ณผ๋Š” ์–ป์ง€ ๋ชปํ•˜์˜€์œผ๋ฉฐ, ํฌํš์ด๋ก  1, 2์™€ ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์„ ๋’ท๋ฐ›์นจํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ ์ตœ๊ทผ ํ•œ๊ตญ ์‚ฐ์—…์— ๋„์ž…๋œ ๊ทœ์ œ๋Š” ํฌํš์ด๋ก ๊ณผ ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์— ์˜ํ•ด ์„ค๋ช…ํ•˜๋Š” ๊ฒƒ์ด ์ ์ ˆํ•œ ๊ฒƒ์œผ๋กœ ๊ฒฐ๋ก ์„ ๋‚ด๋ ธ๋Š”๋ฐ, ์ด๊ฒƒ์€ ํ•œ๊ตญ์˜ ๊ทœ์ œ๊ฐ€ ๊ธฐ์—… ๋˜๋Š” ์ •๋ถ€ ์ฃผ๋„๋กœ ๊ทœ์ œ๊ฐ€ ๋„์ž…๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์ •๋ถ€ ์ฃผ๋„๋กœ ๊ทœ์ œ๊ฐ€ ๋„์ž…๋˜๋Š” ๊ฒฝ์šฐ์—๋Š”, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ๊ณต์ต์ด๋ก ๋ณด๋‹ค๋Š” ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์ด ๋” ์ž˜ ์„ค๋ช…ํ•œ๋‹ค๋Š” ์ ์ด ํŠน์ดํ•˜๋‹ค. ๊ธฐ์—…๋“ค์ด ๊ทœ์ œ๋‹น๊ตญ์„ ํฌํšํ•˜์—ฌ ๊ทœ์ œ๋ฅผ ๋„์ž…ํ•˜๋ ค๋Š” ๋…ธ๋ ฅ์€ ์˜คํžˆ๋ ค ์ž์—ฐ์Šค๋Ÿฌ์šด ํ˜„์ƒ์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋Š” ๋ฐ˜๋ฉด, ์ •๋ถ€๊ฐ€ ๊ฒฝ์Ÿ์ด ์‹ฌํ™”๋˜๋Š” ๊ฒƒ์„ ๊ทœ์ œํ•˜๋Š”๋ฐ ์ ๊ทน์ ์ด๋ผ๋Š” ์ ์€ ๋‹ค์†Œ ํ‰๊ฐ€ํ•˜๊ธฐ๊ฐ€ ์กฐ์‹ฌ์Šค๋Ÿฝ๋‹ค. ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์— ์˜ํ•œ ๊ทœ์ œ๋„์ž…์€ ์‹œ์žฅ์œผ๋กœ์˜ ๊ณผ๋‹น์ง„์ž…์„ ๋ฐฉ์ง€ํ•˜์—ฌ ์‚ฌํšŒํ›„์ƒ์„ ์ฆ์ง„์‹œ์ผœ์•ผํ•œ๋‹ค๋Š” ๋…ผ๋ฆฌ๋ฅผ ๋”ฐ๋ฅด๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ๋…ผ๋ฆฌ์˜ ์˜ค๋ฅ˜์— ๋Œ€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ๊ฐ€ ์ด๋ฏธ ๋‹ค์ˆ˜ ์กด์žฌํ•˜๋ฉฐ, ๊ณผ๋‹น๊ฒฝ์Ÿ์„ ๋ฐฉ์ง€ํ•˜๋ ค๋Š” ๋ชฉ์ ์œผ๋กœ ์ •๋ถ€๊ฐ€ ๊ทœ์ œ๋ฅผ ํ†ตํ•ด ์‹œ์žฅ์— ๊ฐœ์ž…ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ๋ถ€์ •์ ์ธ ์‹œ๊ฐ์ด ๋งŽ์ด ์กด์žฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์‹ค์ฆ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ํ•œ๊ตญ ์‚ฐ์—…์˜ ๊ทœ์ œ ํ˜„ํ™ฉ์„ ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก ์œผ๋กœ๋„ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์€ ๊ทธ๋งŒํผ ํ•œ๊ตญ ์ •๋ถ€๋Š” ์‹œ์žฅ์„ ์ฃผ๋„์ ์œผ๋กœ ํ†ต์ œํ•˜๋ ค๋Š” ๊ฒฝํ–ฅ์ด ๊ฐ•ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ํŠนํžˆ ๊ฒฝ์Ÿ์ด ๋‚ฎ์•„์ง€๋Š” ๊ฒƒ๋ณด๋‹ค ๋†’์•„์ง€๋Š” ๊ฒƒ์„ ๋” ์ ๊ทน์ ์œผ๋กœ ๊ทœ์ œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์•„ ๋…์ ๋ณด๋‹ค๋Š” ๊ณผ๋‹น๊ฒฝ์Ÿ์— ๋Œ€ํ•œ ์šฐ๋ ค๊ฐ€ ๋” ํฌ๊ฒŒ ์ž‘์šฉํ•˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๊ทœ์ œ๋„์ž… ๊ณผ์ •์—์„œ ๊ณผ๋‹น๊ฒฝ์Ÿ ๋ฐฉ์ง€์— ๋Œ€ํ•œ ์ •๋ถ€์˜ ๊ฐ•ํ•œ ์˜์ง€๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค๋Š” ์ ์—์„œ ํ•œ๊ตญ์˜ ์‚ฐ์—…์˜ ๊ทœ์ œ์—์„œ ํŠน์ด์ ์„ ๋„์ถœํ•˜์—ฌ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์—์„œ๋Š” ํ™œ์šฉ๋˜์ง€ ๋ชปํ•œ ๋ฐฉ๋Œ€ํ•œ ํ•œ๊ตญ ๊ทœ์ œ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ  ๋ชจ๋“  ๊ทœ์ œ๋ฅผ ์œ ํ˜•๋ณ„๋กœ ๋‚˜๋ˆ„์–ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๊ธฐ์กด ๊ทœ์ œ์ด๋ก ์„ ์„ธ๋ถ€์ ์œผ๋กœ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ•ด๋ณด์•˜๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ๋˜ํ•œ, ์‚ฐ์—…๋ณ„๋กœ ๊ตฌ๋ถ„๋˜์–ด ์žˆ์ง€ ์•Š์€ ๊ทœ์ œ ๋ฐ์ดํ„ฐ๋ฅผ ๊ฐ ์‚ฐ์—…์— ๋งค์นญํ•˜๋Š” ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด์„œ, ๊ธฐ์กด์˜ ๊ทœ์ œ์ด๋ก ์˜ ์ ์šฉ๋ฒ”์œ„๋ฅผ ํ™•์žฅํ•˜๋Š” ๋ฐ ๊ธฐ์—ฌ๋ฅผ ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ „์ฒด ์‚ฐ์—… ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„๊ฐ€ ๊ทœ์ œ ์ƒ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€ ๊ฒƒ์€ ์ค‘์š”ํ•œ ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ํŠนํžˆ ๊ฒฝ์Ÿ์ •๋„๋ฅผ ์„ธ ๊ฐœ์˜ ๋‹จ๊ณ„๋กœ ๋‚˜๋ˆ„์–ด ๋ถ„์„์„ ํ•˜์—ฌ, ๊ฒฝ์Ÿ์„ ๋ณด๋‹ค ์„ธ๋ถ€์ ์œผ๋กœ ๊ทธ๋ฆฌ๊ณ  ๋™์‹œ์— ํฌ๊ด„์ ์œผ๋กœ ๋‹ค๋ฃจ์—ˆ๋‹ค๋Š” ์ ์—์„œ ์ค‘์š”ํ•˜๋‹ค. ๊ธฐ์กด์˜ ์„ ํ–‰์—ฐ๊ตฌ๋Š” ๊ทœ์ œ๊ฐ€ ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ ๊ฒฝ์šฐ๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด์—ˆ๋‹ค๋ฉด, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฒฝ์Ÿ์ •๋„๊ฐ€ ๊ทœ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํž˜์œผ๋กœ์จ, ๊ฒฝ์Ÿ๊ณผ ๊ทœ์ œ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๋”์šฑ ํ™•์žฅํ•˜์˜€๋‹ค. ์ด์™€ ๋”๋ถˆ์–ด ๊ฒฝ์Ÿ์ •๋„์™€ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ์šฉ์ˆ˜์ค€์ด ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•œ ์ ๋„ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ์ด๋Š” ์‚ฐ์—…์˜ ๋˜ ๋‹ค๋ฅธ ํŠน์„ฑ์ด ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์™€ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ฐ•ํ™” ๋˜๋Š” ์•ฝํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋”ฐ๋ผ์„œ ๊ทœ์ œ๊ด€๋ จ ์ •์ฑ…์„ ์ˆ˜๋ฆฝํ•  ๋•Œ ๋ฐ˜๋“œ์‹œ ์‚ฐ์—…์˜ ๋‹ค์–‘ํ•œ ํŠน์„ฑ์„ ๋‹ค๋ฐฉ๋ฉด์œผ๋กœ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ฌด์—‡๋ณด๋‹ค ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฒฝ์Ÿ์ •๋„์™€ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์–ด๋–ค ๊ทœ์ œ์ด๋ก ์œผ๋กœ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ด๋Š” ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„๋งŒ์„ ํŒŒ์•…ํ•˜๋”๋ผ๋„ ์–ด๋– ํ•œ ๊ทœ์ œ๊ฐ€ ์ƒ์„ฑ๋  ์ˆ˜ ์žˆ์„์ง€ ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋˜ํ•œ, ๊ทธ ๊ทœ์ œ ์ƒ์„ฑ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์ ์ด ๋ฌด์—‡์ธ์ง€ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋‹จ๊ธฐ ๋ฐ ์žฅ๊ธฐ์ ์ธ ์ •์ฑ…์ˆ˜๋ฆฝ์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค๋งŒ, ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ํ™œ์šฉํ•œ ๊ทœ์ œ ์ž๋ฃŒ๊ฐ€ ํ˜„์žฌ ์œ ํšจํ•œ ๊ทœ์ œ๋งŒ์œผ๋กœ ์ œํ•œ๋˜์–ด ์žˆ์–ด ํ•œ๊ณ„์ ์ด ์žˆ๋‹ค. ๋‹จ์ผ ์—ฐ๋„ ๊ทœ์ œ ๋ฐ์ดํ„ฐ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋ฐฉ๋ฒ•๋ก ์ ์œผ๋กœ ๋ณด์™„์„ ์‹œ๋„ํ•˜์˜€์ง€๋งŒ, ์‹ค์ œ ๊ณผ๊ฑฐ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ–ˆ์„ ๋•Œ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋ถ„์„์˜ ๊ฒฐ๊ณผ์™€๋Š” ํŽธ์ฐจ๊ฐ€ ํด ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•„์‰ฝ๊ฒŒ๋„ ํ˜„์žฌ ์‹œ์ ์œผ๋กœ ํ•œ๊ตญ์˜ ๊ณผ๊ฑฐ ๊ทœ์ œ ์ž๋ฃŒ๋Š” ์œ ํ˜•๋ณ„๋กœ๋Š” ๋ฌผ๋ก ์ด๊ณ , ์ „์ฒด ๋ชฉ๋ก์กฐ์ฐจ๋„ ๊ด€๋ฆฌ ๋˜๋Š” ๊ตฌ์ถ•๋˜์–ด ์žˆ์ง€ ์•Š๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ๋”์šฑ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฉด์œผ๋กœ ํ™•์žฅํ•˜๊ณ  ์ ์šฉํ•˜๋Š” ๋ฐ ํ•œ๊ณ„๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๋˜ํ•œ, ๋ณธ ๋ถ„์„์—์„œ ๋” ๋‚˜์•„๊ฐ€ ๊ฐ ๊ทœ์ œ์˜ ์œ ํ˜•์ด ํŠน์ • ์‚ฐ์—…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์—ฌ, ์‚ฐ์—… ํŠน์„ฑ๋ณ„ ๋” ์ ํ•ฉํ•œ ๊ทœ์ œ์œ ํ˜•์„ ์•Œ์•„๋ณด๋Š” ํ›„์†์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„๊ฒฐ๊ณผ์™€ ๋ฐฉ๋ฒ•์ด ๊ทผ๊ฐ„์ด ๋˜์–ด ํ–ฅํ›„ ๊ทœ์ œ๊ฐœํ˜์— ๋„์›€์ด ๋˜๊ณ , ๊ฐ ์‚ฐ์—…๋ณ„ ๋” ์ ํ•ฉํ•œ ๊ทœ์ œ๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ๋„์ž…ํ•˜๋Š”๋ฐ ๋ฐฉํ–ฅ์„ ์ œ์‹œํ•˜๊ฒŒ ๋˜๊ธธ ๊ธฐ๋Œ€ํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 2 ์žฅ ๊ทœ์ œ์ด๋ก ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ 4 ์ œ 1 ์ ˆ ๊ทœ์ œ์ด๋ก  4 1. ๊ณต์ต์ด๋ก  4 2. ํฌํš์ด๋ก  5 3. ๊ฒฝ์ œ๊ทœ์ œ์ด๋ก ๊ณผ ์ผ๋ฐ˜์ด๋ก  6 4. ๊ณผ๋‹น๊ฒฝ์Ÿ์ด๋ก  7 ์ œ 2 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ 9 1. ๊ทœ์ œ์™€ ๊ฒฝ์ œ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 9 2. ๊ทœ์ œ์™„ํ™”์˜ ๊ฒฝ์ œ์  ํšจ๊ณผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 11 3. ๊ทœ์ œ์™€ ๊ธฐ์ˆ ํ˜์‹ ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 15 4. ํ•œ๊ตญ์˜ ์‚ฐ์—…๋ณ„ ๊ทœ์ œ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 17 5. ๊ทœ์ œ์ด๋ก ์„ ๋น„๊ตํ•˜๋Š” ์—ฐ๊ตฌ 28 ์ œ 3 ์žฅ ๊ทœ์ œ์˜ ์ •์˜์™€ ํ˜„ํ™ฉ 20 ์ œ 1 ์ ˆ ๊ทœ์ œ์˜ ์ •์˜์™€ ๋ถ„๋ฅ˜ 20 ์ œ 2 ์ ˆ ๊ทœ์ œ ํ˜„ํ™ฉ 24 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 26 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ 26 1. ๊ฒฝ์Ÿ๊ณผ ๊ทœ์ œ์˜ ๊ด€๊ณ„ 27 2. ์‹œ์žฅ๊ตฌ์กฐ์™€ ๊ฒฝ์Ÿ 28 3. ๊ฒฝ์Ÿ๊ณผ ๊ทœ์ œ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ๊ทœ์ œ์ด๋ก  30 ์ œ 2 ์ ˆ ๋ถ„์„๋ฐฉ๋ฒ• 42 1. ๋ชจํ˜•-1 ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ๊ณผ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„ ๊ฒ€์ฆ 45 2. ๋ชจํ˜•-2 ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ๊ณผ ์œ ํ˜•๋ณ„ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„ ๊ฒ€์ฆ 46 3. ๋ชจํ˜•-3 ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์™€ ๊ทœ์ œ ๊ฐ„์˜ ๊ด€๊ณ„์—์„œ ๊ณ ์šฉ ์ˆ˜์ค€ ๋ณ€ํ™”์˜ ์กฐ์ ˆํšจ๊ณผ ๊ฒ€์ฆ 50 ์ œ 5 ์žฅ ์—ฐ๊ตฌ์ž๋ฃŒ ๋ฐ ๋ณ€์ˆ˜์„ค๋ช… 54 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์ž๋ฃŒ 54 1. ๊ทœ์ œ ๋ฐ์ดํ„ฐ 55 2. ๊ทœ์ œ์œ ํ˜•๋ณ„ ๋ถ„๋ฅ˜ 56 3. ์‚ฐ์—…๋ณ„ ๊ทœ์ œ๋ถ„๋ฅ˜ 56 ์ œ 2 ์ ˆ ๋ณ€์ˆ˜์„ค๋ช… 59 1. ์ข…์†๋ณ€์ˆ˜ 59 2. ๋…๋ฆฝ๋ณ€์ˆ˜ 60 3. ์กฐ์ ˆ๋ณ€์ˆ˜์™€ ์ƒํ˜ธ์ž‘์šฉ๋ณ€์ˆ˜ 62 4. ํ†ต์ œ๋ณ€์ˆ˜ 63 ์ œ 6 ์žฅ ๋ถ„์„๊ฒฐ๊ณผ 66 ์ œ 1 ์ ˆ ๊ธฐ์ดˆ๋ถ„์„ 66 1. ๊ทœ์ œ ๋ณ€์ˆ˜์˜ ๋ถ„ํฌ ๋ถ„์„ 66 ์ œ 2 ์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ 72 1. ๋ณ€์ˆ˜ ๊ธฐ์ˆ ํ†ต๊ณ„ 72 ์ œ 3 ์ ˆ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 73 1. ๋ณ€์ˆ˜ ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 73 ์ œ 4 ์ ˆ ์‹ค์ฆ๋ถ„์„ ๊ฒฐ๊ณผ 80 1. ๋ชจํ˜•-1 ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„ ๋ณ€ํ™”์™€ ์‚ฐ์—… ๋‚ด ์ „์ฒด ๊ทœ์ œ ๋ณ€ํ™”์™€์˜ ๊ด€๊ณ„ ๊ฒ€์ฆ ๊ฒฐ๊ณผ 80 2. ๋ชจํ˜•-2 ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„ ๋ณ€ํ™”์™€ ์‚ฐ์—… ๋‚ด ์œ ํ˜•๋ณ„ ๊ทœ์ œ ๋ณ€ํ™”์™€์˜ ๊ด€๊ณ„ ๊ฒ€์ฆ ๊ฒฐ๊ณผ 82 3. ๋ชจํ˜•-3 ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„ ๋ณ€ํ™”์™€ ์ „์ฒด ๊ทœ์ œ ๋ณ€ํ™”์™€์˜ ๊ด€๊ณ„์—์„œ ๊ณ ์šฉ์ˆ˜์ค€์˜ ์กฐ์ ˆํšจ๊ณผ ๊ฒ€์ฆ ๊ฒฐ๊ณผ 89 ์ œ 7 ์žฅ ๊ฒฐ๋ก  95 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์š”์•ฝ 95 1. ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์™€ ๊ทœ์ œ์˜ ๊ด€๊ณ„ 95 2. ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์™€ ๊ฐ€๊ฒฉ ๋ฐ ๊ฑฐ๋ž˜๊ทœ์ œ์˜ ๊ด€๊ณ„ 95 3. ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์™€ ์ง„์ž…๊ทœ์ œ์˜ ๊ด€๊ณ„ 96 4. ์‚ฐ์—…์˜ ๊ฒฝ์Ÿ์ •๋„์™€ ์ง„์ž…๊ทœ์ œ์˜ ๊ด€๊ณ„์—์„œ ๊ณ ์šฉ์ˆ˜์ค€์˜ ์กฐ์ ˆํšจ๊ณผ 97 5. ์ด๋ก  97 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•จ์˜ ๋ฐ ํ•œ๊ณ„์  99 ์ฐธ๊ณ ๋ฌธํ—Œ 101 ๋ถ€๋ก 107 Abstract 145Docto

    20์„ธ๊ธฐ ๋ฏธ์ˆ ์‚ฌ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ์„œ์˜ ์–‘์‹๋ก  ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฏธํ•™๊ณผ, 2015. 8. ๊น€์ง„์—ฝ.๋ณธ ๋…ผ๋ฌธ์€ ์˜ค๋Š˜๋‚  ๋ฏธ์ˆ ์‚ฌ๋ฅผ ์—ฐ๊ตฌํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ์„œ ์–‘์‹๋ก ์˜ ์œ ํšจ์„ฑ์„ ๊ฒ€ํ† ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ์ด๋Š” 20์„ธ๊ธฐ ์ดˆ๋ฐ˜์— ์–‘์‹๋ก ์„ ์œ ๋ ฅํ•œ ๋ฏธ์ˆ ์‚ฌ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ์ •์ดˆํ–ˆ๋˜ ํ•˜์ธ๋ฆฌํžˆ ๋ตํ”Œ๋ฆฐ์˜ ๋ฏธ์ˆ ์‚ฌ๋ก ์„ ์—ฐ๊ตฌํ•˜๊ณ , ๊ทธ์˜ ๋…ผ์˜๊ฐ€ 20์„ธ๊ธฐ์˜ ๋ฏธ์ˆ ์‚ฌ, ๊ทธ ์ค‘์—์„œ๋„ ํŠนํžˆ ์ถ”์ƒ๋ฏธ์ˆ ์ด๋ผ๋Š” ๊ฐ€์žฅ ํ˜„๋Œ€์ ์ธ ๋ฏธ์ˆ  ์–‘์‹์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์—๋„ ์œ ํšจํ•œ์ง€๋ฅผ ๊ฒ€ํ† ํ•˜๋Š” ์ž‘์—…์ด๋‹ค. ๋ตํ”Œ๋ฆฐ์— ์žˆ์–ด์„œ ์–‘์‹์€ ๋‹จ์ˆœํžˆ ์‹œ๋Œ€, ๋ฏผ์กฑ, ๊ฐœ์ธ์˜ ์ •์„œ๊ฐ€ ์ฆ‰๊ฐ์ ์œผ๋กœ ํ‘œ์ถœ๋œ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ํ‘œ์ƒํ˜•์‹์ด๋ผ๋Š” ๋” ์‹ฌ์ธต์ ์ธ ๋ฐœ์ƒ๊ทผ์›์„ ๊ฐ–๋Š”๋‹ค. ์ด๋ ‡๋“ฏ ๊ทธ์˜ ๋…ผ์˜์˜ ์ค‘์‹ฌ๋ถ€์— ํ˜•์‹ ๊ฐœ๋…์ด ๋†“์ธ ํƒ“์— ๊ทธ๋Š” ํ˜•์‹์ฃผ์˜ ๋ฏธ์ˆ ์‚ฌํ•™์ž๋กœ ๋‚™์ธ์ฐํžˆ๊ฒŒ ๋˜์—ˆ๊ณ , ๊ทธ์˜ ์–‘์‹๋ก ์€ ํ˜•์‹์ฃผ์˜ ์ด๋ก ์— ๊ฐ€ํ•ด์กŒ๋˜ ์ˆ˜๋งŽ์€ ๋น„๋‚œ๋“ค์„ ๊ณ ์Šค๋ž€ํžˆ ๊ฐ๋‹นํ•ด์•ผ ํ–ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฒฐ๊ตญ ์–‘์‹๋ก ์€ ํŒŒ๋…ธํ”„์Šคํ‚ค์˜ ๋„์ƒํ•ด์„ํ•™์ด๋ผ๋Š” ๋˜ ๋น„-ํ˜•์‹์ฃผ์˜์ ์ธ ๋ฐฉ๋ฒ•๋ก ์— ๊ทธ ์ฃผ๋„์ ์ธ ์œ„์น˜๋ฅผ ๋„˜๊ฒจ์ฃผ๋ฉด์„œ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ์„œ์˜ ์œ„๋ ฅ์„ ๊ฑฐ์˜ ์ƒ์‹คํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ๊ธฐ์‹ค ํŒŒ๋…ธํ”„์Šคํ‚ค์˜ ๋„์ƒํ•ด์„ํ•™์€ ๋ตํ”Œ๋ฆฐ์˜ ์–‘์‹๋ก ์— ๋Œ€ํ•œ ๋น„ํŒ์  ๊ฒ€ํ† ๋ฅผ ๊ณ„๊ธฐ๋กœ ๋ฐœ์ƒํ•œ ๊ฒƒ์ด์—ˆ๋‹ค. ํŒŒ๋…ธํ”„์Šคํ‚ค์— ๋”ฐ๋ฅด๋ฉด, ์–‘์‹์€ ํ˜•์‹์ด๋ผ๋Š” ํ˜„์ƒ์œผ๋กœ์„œ ๊ฐ€์‹œํ™”๋˜์ง€๋งŒ ๊ทธ๋Ÿฌํ•œ ํ˜„์ƒ์ด ๋ฐœ์ƒํ•œ ์›์ธ ์ž์ฒด๋Š” ์ •์‹ ์  ์ฐจ์›์— ๋†“์—ฌ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฐ”๋กœ ๊ทธ ์ •์‹ ์  ์ฐจ์›์— ๋Œ€ํ•ด ํƒ๊ตฌํ•˜๋Š” ๊ฒƒ์ด ๋„์ƒํ•ด์„ํ•™์  ๋ฏธ์ˆ ์‚ฌ ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ณผ์ œ๋กœ ์„ค์ •๋˜์—ˆ๋‹ค. ๋งํ•˜์ž๋ฉด ํŒŒ๋…ธํ”„์Šคํ‚ค๊ฐ€ ์–‘์‹๋ก ์— ๋Œ€ํ•œ ๋Œ€์•ˆ์œผ๋กœ ๋„์ƒํ•ด์„ํ•™์„ ์ œ์‹œํ•œ ๊ฒƒ์€, ์–‘์‹๋ก ์„ ๋ฌด์ž‘์ • ๊ธฐ๊ฐํ•˜๊ธฐ๋ณด๋‹ค๋Š” ์˜คํžˆ๋ ค ์–‘์‹๋ก ์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•จ์ด์—ˆ๋˜ ๊ฒƒ์ด๋‹ค. ์ด๋ ‡๋“ฏ ์–‘์‹๋ก ์ด ๋„์ƒํ•ด์„ํ•™์ด๋ผ๋Š” ๋น„ํ˜•์‹์ฃผ์˜์ ์ธ ๋ฐฉ๋ฒ•๋ก ์˜ ๋ฐœ์ƒ์— ๊ฒฐ์ •์ ์ธ ๋‹จ์ดˆ๋ฅผ ์ œ๊ณตํ–ˆ๋‹ค๋Š” ์‚ฌ์‹ค๋กœ ๋ฏธ๋ฃจ์–ด๋ณผ ๋•Œ, ์–‘์‹๋ก ์€ ๋‹จ์ˆœํžˆ ํ˜•์‹์ฃผ์˜ ์ด๋ก ์œผ๋กœ ๊ทœ์ •๋  ์ˆ˜ ์—†๋Š” ์ธก๋ฉด์ด ์žˆ๋‹ค. ํ˜•์‹์ฃผ์˜ ์ด๋ก ์€ ๋ฏธํ•™๊ณผ ๋ฏธ์ˆ ๋น„ํ‰์˜ ํ•œ ์ž…์žฅ์œผ๋กœ์„œ ์˜ˆ์ˆ ์ž‘ํ’ˆ์˜ ์˜์˜๊ฐ€ ์ž‘ํ’ˆ์˜ ๋‚ด์šฉ๋ณด๋‹ค ํ˜•์‹์— ์žˆ๋‹ค๊ณ  ๋ณด๋ฉฐ, ์™ธ๋ถ€ ์„ธ๊ณ„์— ์˜์กดํ•˜์ง€ ์•Š๋Š” ๊ณ ์œ ํ•œ ๋Œ€์ƒ์  ์ง€์œ„, ๊ทธ๋ฆฌ๊ณ  ๊ณ ์œ ํ•œ ์ •์˜์™€ ๊ฐ€์น˜๋ฅผ ๊ฐ–๋Š” ํšŒํ™”์˜ ์ž์œจ์„ฑ์ด๋ผ๋Š” ๊ด€๋…์„ ์ง€์ง€ํ–ˆ๋‹ค. ์ฆ‰, ์—ฌ๊ธฐ์—๋Š” ์˜ˆ์ˆ ์ž‘ํ’ˆ ์ž์ฒด๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ์‚ฌํšŒ, ์—ญ์‚ฌ์  ๋งฅ๋ฝ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์™„์ „ํžˆ ๋ˆ„๋ฝ๋˜์–ด ์žˆ์—ˆ๋˜ ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ตํ”Œ๋ฆฐ์˜ ์–‘์‹๋ก ์„ ์ง€ํƒฑํ•˜๋Š” ์ฃผ์š” ๊ฐœ๋…์ธ ํ‘œ์ƒํ˜•์‹์€ ์ธ๊ฐ„์˜ ์‹œ๊ฐ์ด๋ผ๋Š” ๊ฐ๊ฐ์— ์—ฐ๋ฃจ๋œ ์‹ฌ๋ฆฌ์ , ์ •์„œ์  ์ฐจ์›์„ ํฌ๊ด„ํ•˜๋Š” ์ผ์ข…์˜ ์‹œ๊ฐ์  ํƒœ๋„๋ฅผ ๊ฐ€๋ฆฌํ‚จ๋‹ค. ๋”ฐ๋ผ์„œ ์–‘์‹๋ก ์€ ํ˜•์‹ ์ด์™ธ์˜ ๋งฅ๋ฝ์„ ์ „ํ˜€ ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š” ํ˜•์‹์ฃผ์˜ ์ด๋ก ๊ณผ๋Š” ํ™•์—ฐํžˆ ๋‹ค๋ฅธ ๋ฐฉํ–ฅ์˜ ์ด๋ก ์  ์„ฑํ–ฅ์„ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์–‘์‹๋ก ์˜ ์ด์™€ ๊ฐ™์€ ๋น„-ํ˜•์‹์ฃผ์˜์  ๋ฉด๋ชจ๋Š” 20์„ธ๊ธฐ ์ถ”์ƒ๋ฏธ์ˆ ์— ๋Œ€ํ•œ ๋ฏธ์ˆ ์‚ฌํ•™์ž ๋งˆ์ด์–ด ์ƒคํ”ผ๋กœ์˜ ์–‘์‹๋ก ์  ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋”์šฑ ๋ถ„๋ช…ํ•˜๊ฒŒ ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ํ˜•์‹์ฃผ์˜ ์ด๋ก ์ด ๋ฏธ์ˆ ์ž‘ํ’ˆ์— ๋Œ€ํ•œ ์ ์ ˆํ•œ ์ดํ•ด๋ฅผ ์ค„ ์ˆ˜ ์—†๋‹ค๋Š” ๋งˆ๋ฅดํฌ์Šค์ฃผ์˜ ๋ฏธ์ˆ ์‚ฌ ๋ฐฉ๋ฒ•๋ก ์ด ํ™•์‚ฐ๋˜๋Š” ๊ฒฝํ–ฅ ์†์—์„œ, ๋งˆ์ด์–ด ์ƒคํ”ผ๋กœ๋Š” ํ˜„๋Œ€ ์ถ”์ƒ๋ฏธ์ˆ ์— ๋Œ€ํ•œ ๋น„-ํ˜•์‹์ฃผ์˜์  ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ํŠน์œ ์˜ ์–‘์‹๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ์ƒคํ”ผ๋กœ์˜ ์–‘์‹๋ก ์€ ๋ตํ”Œ๋ฆฐ์˜ ์–‘์‹๋ก ์— ์ž ์žฌ๋˜์–ด ์žˆ๋˜ ์ธ๊ฐ„์˜ ๊ฐ๊ฐ์  ๊ฒฝํ—˜๊ณผ ์‹ฌ๋ฆฌ์  ์ธต์œ„์— ๋Œ€ํ•œ ๋…ผ์˜๋ฅผ ํ™•์žฅํ•œ ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ถ”์ƒ๋ฏธ์ˆ ์ด ๋‹จ์ˆœ ์˜๋ฏธ๋ฅผ ๋ฐฐ์ œํ•œ ์ˆœ์ˆ˜ํ•œ ํ˜•์‹์˜ ํ–ฅ์—ฐ์œผ๋กœ ๊ฐ„์ฃผํ•˜๋Š” ํ˜•์‹์ฃผ์˜์  ๊ด€์ ์„ ๋น„ํŒํ•œ๋‹ค. ๊ทธ์— ๋”ฐ๋ฅด๋ฉด ์ถ”์ƒ๋ฏธ์ˆ ์˜ ํ˜•์‹์€ ์ธ๊ฐ„์˜ ๋น„๊ฐ€์‹œ์ ์ธ ์ •์„œ์˜ ์ž์ทจ๋“ค๋กœ์„œ ์—ฌ์ „ํžˆ ๋‹ค์–‘ํ•œ ์˜๋ฏธ๋“ค์„ ๋ฐœ์ƒ์‹œํ‚จ๋‹ค. ๋‹ค์‹œ ๋งํ•ด, ์ถ”์ƒ๋ฏธ์ˆ ์€ ์˜ˆ์ˆ ๊ฐ€์˜ ์ •์„œ์  ํƒœ๋„์— ๋”ฐ๋ผ ์ƒ‰์ฑ„, ํ‘œ๋ฉด, ์œค๊ณฝ ๋“ฑ์˜ ํ˜•์‹์— ํŠน์ˆ˜ํ•˜๊ณ  ์ผ์‹œ์ ์ธ ์ค‘์š”์„ฑ์ด ๋ถ€์—ฌ๋œ ์ผ์ข…์˜ ์–‘์‹์œผ๋กœ ์ดํ•ด๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ ‡๋“ฏ ์ƒคํ”ผ๋กœ๋Š” ์ถ”์ƒ๋ฏธ์ˆ ์ด ๊ทธ๊ฒƒ์„ ๋‘˜๋Ÿฌ์‹ผ ์‚ฌํšŒ, ์—ญ์‚ฌ์  ๋งฅ๋ฝ๊ณผ ๊ฒฐ์ฝ” ๋ถ„๋ฆฌ๋˜์–ด ๋…ผ์˜๋  ์ˆ˜ ์—†๋Š” ์–‘์‹์ž„์„ ์—ญ์„คํ•˜๊ธฐ ์œ„ํ•ด ์–‘์‹๋ก ์„ ๋ณ€์šฉํ•˜์˜€๋‹ค. ์ด๋กœ์จ ์–‘์‹๋ก ์€ ์˜ˆ์ˆ ์„ ๋‘˜๋Ÿฌ์‹ผ ์ธ๊ฐ„์˜ ๊ฐ๊ฐ์  ๊ฒฝํ—˜์— ๋Œ€ํ•œ ๋…ผ์˜๋กœ ํ™•์žฅ๋˜์—ˆ๋˜ ๊ฒƒ์ด๋‹ค. 20์„ธ๊ธฐ์— ๋“ค์–ด์„œ ๋ฏธ์ˆ ์‚ฌ ์—ฐ๊ตฌ๋Š” ์˜ˆ์ˆ ์ž‘ํ’ˆ ์ž์ฒด์— ๋Œ€ํ•œ ๋…ผ์˜์— ๋จธ๋ฌผ์ง€ ์•Š๊ณ  ์˜ˆ์ˆ ์ž‘ํ’ˆ์„ ๋‘˜๋Ÿฌ์‹ผ ๋‹ค์–‘ํ•œ ์™ธ์  ๋งฅ๋ฝ์— ๋Œ€ํ•œ ๋…ผ์˜๋กœ ๋‚˜์•„๊ฐˆ ๊ฒƒ์„ ์š”์ฒญ๋ฐ›์•˜๊ณ , ์–‘์‹๋ก ์€ ๊ทธ๋Ÿฌํ•œ ์š”์ฒญ์— ์„ฑ์‹คํžˆ ๋ฐ˜์‘ํ•ด ์™”๋‹ค. ๊ทธ๊ฒƒ์€ ์˜ˆ์ˆ ์ž‘ํ’ˆ์„ ๋‘˜๋Ÿฌ์‹ผ ์—ฌํƒ€์˜ ๋งฅ๋ฝ๋“ค์„ ์ „ํ˜€ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ์ฑ„ ํ˜•์‹์ด๋ผ๋Š” ์˜ˆ์ˆ  ๊ณ ์œ ์˜ ์˜์—ญ์—๋งŒ ์ง‘์ค‘ํ–ˆ๋˜ ํ˜•์‹์ฃผ์˜ ์ด๋ก ๊ณผ๋Š” ํ™•์—ฐํžˆ ๊ตฌ๋ถ„๋˜์–ด์•ผ ํ•œ๋‹ค. ์˜คํžˆ๋ ค ์–‘์‹๋ก ์€ ์ธ๊ฐ„์˜ ๊ฐ๊ฐ์  ์ธต์œ„์— ๋Œ€ํ•œ ๊ด€์‹ฌ์„ ๋ฌต๋ฌตํžˆ ๊ฒฌ์ง€ํ•ด์™”์œผ๋ฉฐ, ์ด๋Ÿฐ ์ ์—์„œ ๋ณธ๊ณ ๋Š” ๊ทธ๊ฒƒ์ด ์—„์—ฐํžˆ ์ธ๋ฌธํ•™์˜ ์ง€ํ‰์— ๋†“์—ฌ์•ผ ๋งˆ๋•…ํ•˜๋‹ค๊ณ  ๋ณธ๋‹ค. ๊ทธ๋Ÿผ์œผ๋กœ์จ ์–‘์‹๋ก ์ด ํ˜„๋Œ€ ๋ฏธ์ˆ ์˜ ๋‹ค์–‘ํ•œ ํ˜•์‹์  ์‹คํ—˜๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์—๋„ ์œ ํšจํ•œ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ์„œ์˜ ์œ„์ƒ์„ ํšŒ๋ณตํ•˜๊ฒŒ ๋˜๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.๊ตญ ๋ฌธ ์ดˆ ๋ก โ…ฐ ์„œ ๋ก  1 โ… . ์–‘์‹๋ก ์˜ ํ™•๋ฆฝ: ๋ตํ”Œ๋ฆฐ์˜ ๋ฏธ์ˆ ์‚ฌ๋ก ์„ ์ค‘์‹ฌ์œผ๋กœ 8 1.๋ฏธ์ˆ ์‚ฌ์™€ ์–‘์‹ 9 1) ์–‘์‹์˜ ์—ญ์‚ฌ์„ฑ 9 2) ๋ฏธ์ˆ ์˜ ์–‘์‹์„ฑ 16 2.์–‘์‹์˜ ์ด์ค‘๊ทผ์› 22 1) ํ‘œ์ถœ 22 2) ํ‘œ์ƒํ˜•์‹ 27 3.๋ฏธ์ˆ ์‚ฌ์˜ ๊ธฐ์ดˆ๊ฐœ๋…์œผ๋กœ์„œ์˜ 5๊ฐ€์ง€ ๊ฐœ๋…์Œ 32 โ…ก. ์–‘์‹๋ก ์˜ ํ•œ๊ณ„์™€ ๋Œ€์•ˆ: ํŒŒ๋…ธํ”„์Šคํ‚ค์˜ ๋ฏธ์ˆ ์‚ฌ๋ก ์„ ์ค‘์‹ฌ์œผ๋กœ 38 1.์–‘์‹๊ณผ ์˜๋ฏธ 39 1) ๋ตํ”Œ๋ฆฐ์˜ ํ˜•์‹์ฃผ์˜ ์–‘์‹๋ก ์— ๋Œ€ํ•œ ๋น„ํŒ 39 2) ์–‘์‹์˜ ๋ณธ์งˆ๋กœ์„œ์˜ ์˜๋ฏธ 44 2.๋„์ƒํ•ด์„ํ•™์˜ ์ด๋ก  49 1) ๋ณธ์งˆ์  ์˜๋ฏธ 49 2) ํ•ด์„ํ•™์  ์ ‘๊ทผ 54 3.๋„์ƒํ•ด์„ํ•™์˜ ์‹ค์ฒœ 60 1) ์‹ค์ฒœ์˜ 3 ๋‹จ๊ณ„ 60 2) ์‹ค์ฒœ์˜ ํ•œ๊ณ„ 64 โ…ข. ์–‘์‹๋ก ์˜ ํ˜„๋Œ€์  ๋ณ€์šฉ: ๋งˆ์ด์–ด ์ƒคํ”ผ๋กœ์˜ ๋ฏธ์ˆ ์‚ฌ๋ก ์„ ์ค‘์‹ฌ์œผ๋กœ 70 1.์ƒคํ”ผ๋กœ์˜ ๋ฏธ์ˆ ์‚ฌ๋ก  72 1) ์˜ˆ์ˆ ๊ณผ ์‚ฌํšŒ 72 2) ํ˜•์‹๊ณผ ์˜๋ฏธ 77 2.์ƒคํ”ผ๋กœ์˜ ์–‘์‹๋ก  81 1) ์–‘์‹๊ณผ ๊ธฐํ˜ธ 81 2) ์ถ”์ƒ๋ฏธ์ˆ ์˜ ์ธ๊ฐ„์„ฑ 87 3.๋ชฌ๋“œ๋ฆฌ์•ˆ ์ถ”์ƒ๋ฏธ์ˆ ์— ๋Œ€ํ•œ ์–‘์‹๋ก ์  ๋ถ„์„ 91 ๊ฒฐ ๋ก  99 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 101 ์ฐธ ๊ณ  ๋„ ํŒ AbstractMaste

    Atlas-based Auto-Segmentation for Postoperative Radiotherapy Planning in Endometrial and Cervical Cancers

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    Background: Since intensity-modulated radiation therapy (IMRT) has become popular for the treatment of gynecologic cancers, the contouring process has become more critical. This study evaluated the feasibility of atlas-based auto-segmentation (ABAS) for contouring in patients with endometrial and cervical cancers. Methods: A total of 75 sets of planning CT images from 75 patients were collected. Contours for the pelvic nodal clinical target volume (CTV), femur, and bladder were carefully generated by two skilled radiation oncologists. Of 75 patients, 60 were randomly registered in three different atlas libraries for ABAS in groups of 20, 40, or 60. ABAS was conducted in 15 patients, followed by manual correction (ABASc). The time required to generate all contours was recorded, and the accuracy of segmentation was assessed using Dice's coefficient (DC) and the Hausdorff distance (HD) and compared to those of manually delineated contours. Results: For ABAS-CTV, the best results were achieved with groups of 60 patients (DC, 0.79; HD, 19.7 mm) and the worst results with groups of 20 patients (DC, 0.75; p = 0.012; HD, 21.3 mm; p = 0.002). ABASc-CTV performed better than ABAS-CTV in terms of both HD and DC (ABASc [n = 60]; DC, 0.84; HD, 15.6 mm; all p 0.9 and HD โ‰ค10.0 mm), with significant time reduction compared to that needed for manual delineation (p 40 mm). Furthermore, ABASc-Bladder required a longer processing time than manual contouring to achieve the same accuracy. Conclusions: ABAS could help physicians to delineate the CTV and organs-at-risk (e.g., femurs) in IMRT planning considering its consistency, efficacy, and accuracy.ope

    Kallikrein 5 overexpression is associated with poor prognosis in uterine cervical cancer

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    Objective: Kallikrein 5 (KLK5), which is frequently observed in normal cervico-vaginal fluid, is known to be related to prognosis in several solid tumors. We investigated the prognostic significance of KLK5 in uterine cervical cancer using tumor tissue microarray and immunohistochemistry staining. Methods: We analyzed samples of 165 patients with uterine cervical cancer who received definitive radiation therapy between 2004 and 2012. We divided patients into two groups stratified by their KLK5 activity by immunohistochemistry staining: negative/weak (0-1+) (n=120 patients) and moderate/strong (2-3+) group (n=45 patients). Patient and tumor characteristics, patterns of failure, and survival outcomes were compared. Univariable and multivariable analyses were performed to identify prognostic factors. Results: Patients with KLK5 2-3+ were younger (median: 52 vs. 60 years) and had frequent paraaortic lymph node involvement (40.0% vs. 18.3%) than those with KLK5 0-1+. With a median follow-up of 60.8 (interquartile range, 47.5-77.9) months, patients with KLK5 2-3+ had inferior 5-year locoregional recurrence-free survival and distant metastasis-free survival of 61.7% (vs. 77.5% in KLK5 0-1+ group) and 59.4% (vs. 72.8% in the KLK5 0-1+ group), respectively (all p<0.05). KLK5 2-3+ expression retained its significance after adjusting for other well-known prognostic factors of tumor size and stage in multivariable analysis. Conclusions: KLK5 overexpression is associated with the aggressiveness of cervical cancer and may underlie the diminished response to conventional treatments. Therefore, KLK5 could be a reliable prognostic factor in cervical cancer.ope

    Early hypopharyngeal cancer treated with different therapeutic approaches: a single-institution cohort analysis

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    PURPOSE: Early hypopharyngeal squamous cell carcinoma (HPSCC) is a rarely diagnosed disease, for which the optimal treatment has not been defined yet. We assessed patterns of failure and outcomes in early HPSCC treated with various therapeutic approaches to identify its optimal treatment. MATERIALS AND METHODS: Thirty-six patients with stage I (n = 10) and II (n = 26) treated between January 1992 and March 2014 were reviewed. Patients received definitive radiotherapy (RT) (R group, n = 10), surgery only (S group, n = 19), or postoperative RT (PORT group, n = 7). All patients in both the R and PORT groups received elective bilateral neck irradiation. In the S group, 7 patients had ipsilateral and 8 had bilateral dissection, while 4 patients had no elective dissection. RESULTS: At a median follow-up of 48 months, the 5-year locoregional control (LRC) rate was 65%. Six patients had local failure, 1 regional failure (RF), 3 combined locoregional failures, and 2 distant failures. There was no difference in 5-year LRC among the R, S, and PORT groups (p = 0.17). The presence with a pyriform sinus apex extension was a prognosticator related to LRC (p = 0.01) in the multivariate analysis. Patients with a bilaterally treated neck showed a trend toward a lower RF rate (p = 0.08). CONCLUSION: This study shows that patients with early stage HPSCC involving the pyriform sinus apex might need a tailored approach to improve LRC. Additionally, our study confirms elective neck treatment might have an efficacious role in regional control.ope

    Prognostic values of mid-radiotherapy 18F-FDG PET/CT in patients with esophageal cancer

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    BACKGROUND: To identify whether early metabolic responses as determined using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) during radiotherapy (RT) predict outcomes in patients with esophageal cancer. METHODS: Twenty-one patients with esophageal cancer who received pre-treatment 18F-FDG PET/CT (PET1) and inter-fractional 18F-FDG PET/CT (PET2) after 11 fractions of RT (median 23.1โ€‰Gy, 2.1โ€‰Gy per fraction) were retrospectively reviewed. The region of interest for each calculation was delineated using "PET Edge". We calculated PET parameters including maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The relative changes (%) were calculated using the logarithmically transformed parameter values for the PET1 and PET2 scans. Multivariate analysis of locoregional recurrence and distant failures were performed using Cox regression analysis. After identifying statistically significant PET parameters for discriminating responders from non-responders, receiver operating characteristics curve analyses were used to assess the potentials of the studied PET parameters. RESULTS: After a median follow-up of 13โ€‰months, the 1-year overall and progression-free survival rates were 79.0% and 34.4%, respectively. Four patients developed locoregional recurrences (LRRs) and 8 had distant metastases (DMs). The 1-year overall LRR-free rate was 76.9% while the DM-free rate was 60.6%. The relative changes in MTV (ฮ”MTV) were significantly associated with LRR (p =โ€‰0.03). Conversely, the relative changes in SUVmean (ฮ”SUVmean) were associated with the risk of DM (p =โ€‰0.02). An ฮ”MTV threshold of 1.14 yielded a sensitivity of 60%, specificity of 94%, and an accuracy of 86% for predicting an LRR. Additionally, a ฮ”SUVmean threshold of a 35% decrease yielded a sensitivity of 67%, specificity of 83%, and accuracy of 76% for the prediction DM. TRIAL REGISTRATION: Retrospectively registered. CONCLUSIONS: Changes in tumor metabolism during RT could be used to predict treatment responses, recurrences, and prognoses in patients with esophageal cancer.ope

    Predictive value of interim 18F-FDG-PET in patients with non-small cell lung cancer treated with definitive radiation therapy

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    Purpose: We evaluated that early metabolic response determined by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) during radiotherapy (RT), predicts outcomes in non-small cell lung cancer. Material and methods: Twenty-eight patients evaluated using pretreatment 18F-FDG-PET/CT (PETpre) and interim 18F-FDG-PET/CT (PETinterim) after 11 fractions of RT were retrospectively reviewed. Maximum standardized uptake value (SUVmax) was calculated for primary lesion. Predictive value of gross tumor volume (ฮ”GTV) and SUVmax (ฮ”SUVmax) changes was evaluated for locoregional control (LRC), distant failure (DF), and overall survival (OS). Metabolic responders were patients with ฮ”SUVmax >40%. Results: Metabolic responders showed better trends in 1-year LRC (90.9%) than non-responders (47.1%) (p = 0.086). Patients with large GTVpre (โ‰ฅ120 cc) demonstrated poor LRC (hazard ratio 4.14, p = 0.022), while metabolic non-responders with small GTVpre ( 25% demonstrated inferior diagnostic values than metabolic response. Conclusions: Changes in tumor metabolism diagnosed using PETinterim during RT better predicted treatment responses, recurrences, and prognosis than other factors historically used.ope

    Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area

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    This study investigated the feasibility of deep learning-based segmentation (DLS) and continual training for adaptive radiotherapy (RT) of head and neck (H&N) cancer. One-hundred patients treated with definitive RT were included. Based on 23 organs-at-risk (OARs) manually segmented in initial planning computed tomography (CT), modified FC-DenseNet was trained for DLS: (i) using data obtained from 60 patients, with 20 matched patients in the test set (DLSm); (ii) using data obtained from 60 identical patients with 20 unmatched patients in the test set (DLSu). Manually contoured OARs in adaptive planning CT for independent 20 patients were provided as test sets. Deformable image registration (DIR) was also performed. All 23 OARs were compared using quantitative measurements, and nine OARs were also evaluated via subjective assessment from 26 observers using the Turing test. DLSm achieved better performance than both DLSu and DIR (mean Dice similarity coefficient; 0.83 vs. 0.80 vs. 0.70), mainly for glandular structures, whose volume significantly reduced during RT. Based on subjective measurements, DLS is often perceived as a human (49.2%). Furthermore, DLSm is preferred over DLSu (67.2%) and DIR (96.7%), with a similar rate of required revision to that of manual segmentation (28.0% vs. 29.7%). In conclusion, DLS was effective and preferred over DIR. Additionally, continual DLS training is required for an effective optimization and robustness in personalized adaptive RT.ope
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