285 research outputs found
Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules β₯ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657-0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.ope
Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence
This paper evaluated 3-dimensional radiomics features of breast magnetic resonance imaging (MRI) as prognostic factors for predicting systemic recurrence in triple-negative breast cancer (TNBC) and validated the results with a different MRI scanner. The Rad score was generated from 3-dimensional radiomic features of MRI for 231 TNBCs (training set (GE scanner), nβ=β182; validation set (Philips scanner), nβ=β49). The Clinical and Rad models to predict systemic recurrence were built up and the models were externally validated. In the training set, the Rad score was significantly higher in the group with systemic recurrence (median, -8.430) than the group without (median, -9.873, Pβ<β0.001). The C-index of the Rad model to predict systemic recurrence in the training set was 0.97, which was significantly higher than in the Clinical model (0.879; Pβ=β0.009). When the models were externally validated, the C-index of the Rad model was 0.848, lower than the 0.939 of the Clinical model, although the difference was not statistically significant (Pβ=β0.100). The Rad model for predicting systemic recurrence in TNBC showed a significantly higher C-index than the Clinical model. However, external validation with a different MRI scanner did not show the Rad model to be superior over the Clinical model.ope
Annual Trends in Ultrasonography-Guided 14-Gauge Core Needle Biopsy for Breast Lesions
OBJECTIVE:
To examine time trends in ultrasonography (US)-guided 14-gauge core needle biopsy (CNB) for breast lesions based on the lesion size, Breast Imaging-Reporting and Data System (BI-RADS) category, and pathologic findings.
MATERIALS AND METHODS:
We retrospectively reviewed consecutive US-guided 14-gauge CNBs performed from January 2005 to December 2016 at our institution. A total of 22,297 breast lesions were included. The total number of biopsies, tumor size (β€ 10 mm to > 40 mm), BI-RADS category (1 to 5), and pathologic findings (benign, high risk, ductal carcinoma in situ [DCIS], invasive cancer) were examined annually, and the malignancy rate was analyzed based on the BI-RADS category.
RESULTS:
Both the total number of US scans and US-guided CNBs increased while the proportion of US-guided CNBs to the total number of US scans decreased significantly. The number of biopsies classified based on the tumor size, BI-RADS category, and pathologic findings all increased over time, except for BI-RADS categories 1 or 2 and category 3 (odds ratio [OR] = 0.951 per year, 95% confidence interval [CI]: 0.902, 1.002 and odds ratio = 0.979, 95% CI: 0.970, 0.988, respectively). Both the unadjusted and adjusted total malignancy rates and the DCIS rate increased significantly over time. BI-RADS categories 4a, 4b, and 4c showed a significant increasing trend in the total malignancy rate and DCIS rate.
CONCLUSION:
The malignancy rate in the results of US-guided 14-gauge CNB for breast lesions increased as the total number of biopsies increased from 2005 to 2016. This trend persisted after adjusting for the BI-RADS category.ope
Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma
PURPOSE:
To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC).
METHODS:
From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mmΒ±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109).
RESULTS:
Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAFV600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAFV600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516-0.742) to 0.718 (95% CI: 0.650-0.786), and 0.567 (95% CI: 0.434-0.699) to 0.729 (95% CI: 0.632-0.826), respectively.
CONCLUSION:
Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC regardless of size.ope
The Influences of Individualized Learning Adapted to Students Conception and Small Group Learning Using Smart Devices in Secondary Chemistry Classes
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ABSTRACT 176Docto
Diagnostic Value of CYFRA 21-1 Measurement in Fine-Needle Aspiration Washouts for Detection of Axillary Recurrence in Postoperative Breast Cancer Patients
Purpose
The objective of this study was to evaluate the diagnostic value and threshold levels of cytokeratin fragment 21-1 (CYFRA 21-1) in fine-needle aspiration (FNA) washouts for detection of lymph node (LN) recurrence in postoperative breast cancer patients.
Materials and Methods
FNA cytological assessments and CYFRA 21-1 measurement in FNA washouts were performed for 64 axillary LNs suspicious for recurrence in 64 post-operative breast cancer patients. Final diagnosis was made on the basis of FNA cytology and follow-up data over at least 2 years. The concentration of CYFRA 21-1 was compared between recurrent LNs and benign LNs. Diagnostic performance and cut-off value were evaluated using a receiver operating characteristic curve.
Results
Regardless of the non-diagnostic results, the median concentration of CYFRA 21-1 in recurrent LNs was significantly higher than that in benign LNs (p < 0.001). The optimal diagnostic cut-off value was 1.6 ng/mL. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CYFRA 21-1 for LN recurrence were 90.9%, 100%, 100%, 98.1%, and 98.4%, respectively.
Conclusion
Measurement of CYFRA 21-1 concentration from ultrasound-guided FNA biopsy aspirates showed excellent diagnostic performance with a cut-off value of 1.6 ng/mL. These results indicate that measurement of CYFRA 21-1 concentration in FNA washouts is useful for the diagnosis of axillary LN recurrence in post-operative breast cancer patients.ope
Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists
Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1-2βcm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all pβ>β0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all pβ<β0.001). In small nodules, experienced radiologists showed higher specificity, PPV and accuracy than sCAD (all pβ<β0.05). In conclusion, dCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity.ope
Associations between Bethesda categories and tumor characteristics of conventional papillary thyroid carcinoma
PURPOSE: The aim of this study was to investigate the associations of Bethesda categories III, V, and VI with the clinical and pathological features of thyroid nodules surgically confirmed as conventional papillary thyroid carcinomas (PTCs).
METHODS: We analyzed 1,990 consecutive patients diagnosed with conventional PTC at surgery with preoperative Bethesda categories III, V, or VI. We determined the odds ratio (ORs) of the clinical and pathological variables associated with categories III and V, using category VI as the reference.
RESULTS: Category III and V PTCs had a smaller pathological tumor size (OR, 0.934 and OR, 0.969, respectively) and less frequently had central lymph node metastasis (OR, 0.487 and OR, 0.780, respectively) than category VI PTCs. Category III PTCs less frequently showed suspicious ultrasonographic features (OR, 0.296) than category VI PTCs, and category V PTCs less frequently had gross extrathyroidal extension, with borderline significance (OR, 0.643; P=0.059).
CONCLUSION: Conventional PTCs with a preoperative Bethesda category of III or V may less frequently exhibit poor prognostic factors than those with malignant cytology.ope
2020 Imaging Guidelines for Thyroid Nodules and Differentiated Thyroid Cancer: Korean Society of Thyroid Radiology
Imaging plays a key role in the diagnosis and characterization of thyroid diseases, and the information provided by imaging studies is essential for management planning. A referral guideline for imaging studies may help physicians make reasonable decisions and minimize the number of unnecessary examinations. The Korean Society of Thyroid Radiology (KSThR) developed imaging guidelines for thyroid nodules and differentiated thyroid cancer using an adaptation process through a collaboration between the National Evidence-based Healthcare Collaborating Agency and the working group of KSThR, which is composed of radiologists specializing in thyroid imaging. When evidence is either insufficient or equivocal, expert opinion may supplement the available evidence for recommending imaging. Therefore, we suggest rating the appropriateness of imaging for specific clinical situations in this guideline.ope
μμΈμμ μ΄κ΅κ°μ μνμ΄μμ λν μ±κ°ν¬λ₯΄μ μ λ΅
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μ¬νκ³Όνλν μ μΉμΈκ΅νλΆ(μΈκ΅νμ 곡), 2019. 2. κΉμλ°°.21μΈκΈ°μ νκ²½μ νΈλ¦¬ν μ΄μ°κ²° μ¬νμ λν κΈ°λμ ν¨κ» μλ‘μ΄ μνλ€μ λμ μ λν μ°λ €λ₯Ό μ¦νμν€κ³ μλ€. μ΄λ€ μνλ€μ κ΅κ²½μ μ΄μν μ΄ν΄κ΄κ³μ κ°μ μνΈμμ©μ ν΅ν΄ μμΈ‘ λΆκ°λ₯ν λ°©ν₯μΌλ‘ νλ₯΄κΈ°λ νλ©°, λλ‘λ 물리μ Β·μ μΉμ νλ°λ ₯μ κ°μ§ μ¬λμΌλ‘ κ·κ²°λκΈ°λ νλ€. κΈλ‘λ² μ μΌλ³, ν
λ¬λ¦¬μ¦, κΈ°νλ³ν λ±μμ 보λ―μ΄ μ΅κ·Ό μΈλ₯κ° λ§μ΄νκ³ μλ μνλ€μ μΌκ΅ μ°¨μμ λμμΌλ‘λ ν΄κ²°μ΄ μ΄λ €μ΄ νΉμ§λ€μ λ΄ν¬νκ³ μλ€. μ΄λ¬ν λ³νλ μ°λ¦¬μκ² μ ν΅μ μΈ μ보 κ΄μ μ λμ΄ μλ‘μ΄ μκ°μμ μ΄λ€μ λ°λΌλ³Ό κ²μ μꡬνλ€.
μ΄λ¬ν λ§₯λ½μμ λ³Έκ³ λ μ ν₯μ보(emerging security) κ°λ
μ κΈ°λ°ν λμμ μ κ·Όμ μ μνκ³ μ νλ€. μ ν₯μ보λ μμ€ν
λ΄ λ―Έμμ μν μμκ° μνΈμμ©μ ν΅ν΄ μμ γ»μ§μ λ³νμ μκ³μ μ λμ λ, κ΅κ°μ보λ₯Ό μννλ μ¬κ°ν μ¬μμΌλ‘ μ νλ μ μλ€κ³ 보λ κ°λ
μ΄λ€. κ·Έλ¬λ μ ν₯μ보 μνλ€μ κ΅μ μ μΉνμ μκ°μμ 체κ³μ μΌλ‘ μ΄ν΄λ³΄κ³ , μ μ ν λμ κ±°λ²λμ€μ λ©μ»€λμ¦μ μ°Ύκ³ μνλ €λ μλλ μΆ©λΆνμ§ λͺ»νμλ€. μ£Όλ‘ μ ν΅ μ보μ ꡬλ³λλ λΉμ ν΅ μ보λ‘μμ κ°λ
μ μ°¨μ΄μ νΉμ§, κ·Έλ¦¬κ³ κ·Έκ²μ΄ λ°νλ νΉμ μ§μμ μ¬λ‘ λΆμμ μ€μ¬μΌλ‘ μ΄λ£¨μ΄μ‘κΈ° λλ¬Έμ΄λ€. 21μΈκΈ°μ 볡ν©μ μΈ μ보 νκ²½ λμ μ λμ²νκΈ° μν΄μλ μ ν₯μλ³΄κ° κ°μ§ λ³Έμ§μ μΈ μμ±κ³Ό μ΄λ₯Ό κ³ λ €ν κ±°λ²λμ€μ μ§ν ννλ₯Ό λͺ¨μν νμμ±μ΄ μ κΈ°λλ€.
λ°λΌμ λ³Έ μ°κ΅¬λ μ ν₯μ보 μνμ΄ κ°λ νΉμ§μ 무μμ΄λ©°, μ΄μ λμ²νκΈ° μν΄μλ μ΄λ ν κ±°λ²λμ€ λ©μ»€λμ¦μ΄ νμνμ§μ λν΄ λ΅νκ³ μ νλ€. μ΄λ₯Ό μν΄ λ³Έκ³ λ ν¬κ² μΈ κ°μ§ λ
Όμμ μ΄μ μ λ§μΆλ€. 첫째, νκ·Όλ μλμ λ€μν μ ν₯μ보 μνλ€μ μ΄λ»κ² μ ννν μ μμΌλ©° κ°κ°μ νΉμ§μ 무μμΈμ§λ₯Ό μ΄ν΄λ³Έλ€. μ΄λ₯Ό μν΄ λ¨Όμ κΈ°μ‘΄μ μ ν΅μ μ보 κ°λ
κ³Ό κ±°λ²λμ€ μλλ°©μμ λμ΄ λ°νμλμ νκΈλ²μλ₯Ό μΆμΌλ‘ νλ μ ν₯μ보 μν μ νμ λμμ λΆμνμ μ μνλ€. λμ§Έ, λΆμνμ ν΅ν΄ λμΆλ λ€ κ°μ§ μ ν₯μ보 μν μ νμ μ‘°μνλ κ±°λ²λμ€ λͺ¨λΈλ€μ νΉμ§μ λμμ ν΅μ¬ 주체μ νλ ₯μ λ°©μ μΈ‘λ©΄μμ λΆλ₯νμ¬ μ΄ν΄λ³Έλ€. μ
μ§Έ, μ ν©ν λμλͺ¨λΈλ‘μ μ μ°ν μ νμ κ°λ₯μΌνλ κ±°λ²λμ€ λ©μ»€λμ¦κ³Ό μ΄λ₯Ό μν λ€νΈμν¬ μ λ΅μ μ£Όλͺ©νλ€.
μ΄λ¬ν λΆμνμ ν λλ‘ λ³Έ μ°κ΅¬λ κ° μ νλ³λ‘ λλ¨μμμμ μ£Όμ μ ν₯μ보 μνμ΄μμ λμ μ¬λ‘μ μ μ©ν¨μΌλ‘μ¨, μ΄λ‘ μ μ μ€μ±μ κ²ν ν΄λ³΄μλ€. μ ν₯μ보μ λμ μ μμ νκ·Όλ μ¬νλ₯Ό κ²½ννκ³ μλ μꡬμμ λΏλ§ μλλΌ κ·Όλμ μ μ°μ΄ μλμ μΌλ‘ κ°κ³ νκ² λ¨μμλ μ§μμμλ λλλ¬μ§κ² λνλκ³ μλ€. λνμ μΌλ‘, λλ¨μμμλ λ€μν μ΄κ΅κ°μ μ ν₯μ보 μ΄μλ€μ΄ κΈμ¦νκ³ μλ 곡κ°μ΄λ©΄μ λμμ μμΈμλ°©μ(ASEAN Way)'μ΄λΌλ λ
νΉν κ·Όλμ μ£ΌκΆμμΉμ΄ μμ©νκ³ μλ κ³³μ΄κΈ°λ νλ€. λ³Έκ³ λ μ€μ 2000λ
λ λλ¨μμμλ₯Ό μ°μμ μΌλ‘ κ°ννλ μ΄κ΅κ°μ μ΄μμλ μ¬μ€(SARS), μ°λλ―Έ, μ°λ¬΄(haze), ννν
λ¬μ νμ° κ³Όμ μ λνλ λ€μν κ±°λ²λμ€ ννμ λ³νλ₯Ό νꡬνμλ€. κ·Έλ¦¬κ³ μ΄ κ³Όμ μμ μμΈμ λ°©μμ ꡬ쑰μ μ μ½μ 극볡νκ³ μλ΄μ νλ ₯ λΏλ§ μλλΌ κΈλ‘λ² μ°¨μμ 곡쑰체μ λ₯Ό ꡬμΆνλλ° μ£Όλμ μΈ μν μ νλ μ±κ°ν¬λ₯΄μ μ£Όλͺ©νμλ€.
μ±κ°ν¬λ₯΄λ μ°λλ―ΈλΌλ μ§μ μ°¨μμ λλ°/νμ ν μνμ μ§λ©΄ν΄μλ μλ΄ μ λΆ μ£Όλν μ κ·Όμ μλνμμΌλ©° κΈλ‘λ² μ°¨μμ λλ°/μ°κ³ν μνμΈ ννν
λ¬μ λν΄μλ μμΈ κ°λκ΅μ ν¬κ΄νλ μμΈ μ λΆ κ³΅μ‘°ν λͺ¨λΈλ‘ λμνμλ€. λν λνμ μΈ μ§μ μ°¨μμ μ μ¦/νμ ν μν μ΄μμΈ μ°λ¬΄μ λν΄μλ μλ΄ λ€μ νμμ μ°Έμ¬ν μ λ΅μ, λ§μ§λ§μΌλ‘ κΈλ‘λ² μ°¨μμ μ μ¦/μ°κ³ν μνμ΄μλ μ¬μ€μ λν΄μλ κΈλ‘λ² λ€μ νμμ 곡쑰ν μ λ΅μ 보μ¬μ£Όμλ€.
μ’
ν©νλ©΄, λ€μν μ΄κ΅κ°μ μ ν₯μ보 μνμ μ§λ©΄νμ¬ μ±κ°ν¬λ₯΄λ μ€μ¬ μκ΅μ μ§μ μ μΈ νΌν΄λ‘ μ΄μ΄μ§μ§ μλλΌλ μ§μ λ° κΈλ‘λ² μ°¨μμ μ보μ μ¬μμΌλ‘ μμ ννκ³ ν¨κ³Όμ μΈ κ±°λ²λμ€ κ΅¬μΆμ μν λ€μν μλλ₯Ό νΌμ³€λ€κ³ λ³Ό μ μλ€. μ΄ κ³Όμ μμ λνλ μ±κ°ν¬λ₯΄μ λ€νΈμν¬ μ λ΅μ 볡μ‘ν μ ν₯μ보 μνλ€μ λν΄ κ΅κ°κ° μ΄λ»κ² μ μ°νκ² μ κ·Ό λ°©μμ μ νν μ μλμ§, κ·Έλ¦¬κ³ κΆνμ μ΄λ»κ² ν¨κ³Όμ μΌλ‘ 곡μ Β·μμν μ μλμ§λ₯Ό 보μ¬μ£Όλ μλ―Έμλ μ¬λ‘λΌ ν μ μμ κ²μ΄λ€. νΉν, μ±κ°ν¬λ₯΄μ λμ μ¬λ‘λ μ ν₯μ보λΌλ μλ‘μ΄ λμ μ λν μ€κ²¬κ΅ μν μ κ°λ₯μ±μ 보μ¬μ£Όμλ€λ μ μμλ μμ¬νλ λ°κ° ν¬λ€.The 21st century environment, marked by globalization and the advent of the information age, is posing new kinds of threats. These risks flow in unpredictable directions through cross-border stakeholder interactions, sometimes resulting in catastrophic physical and political explosions. As seen in the cases of global epidemics, terrorism and climate change, the risks that humanity faces in recent years have features that are difficult to solve with national responses. These changes lead us to look beyond the traditional view on security issues.
In this context, this paper proposes an alternative approach based on the concept of emerging security. Emerging security is a concept that suggests that when micro-risk components in a system cross the threshold of quantitative and qualitative change through interaction, they can be converted into serious issues that threaten national security. However, attempts to systematically examine emerging security risks from the perspective of international politics and seek appropriate mechanisms of response governance have been lacking. This is because the existing literature on the topic limited itself to highlighting the differences between emerging and traditional security and to case analysis of specific regions in which an emerging security issue transpired. In order to cope with the challenges of the 21st centurys complex security environment, it is necessary to explore the essential nature of such emerging security and the evolution of governance that takes this into consideration.
Therefore, this study addresses what features characterize emerging security risks and what governance mechanisms are needed to cope with them. To that end, this article focuses largely on three discussions. First, I examine how emerging security risks in the postmodern era can be categorized and what their characteristics are. In doing so, I propose an alternative framework for analyzing emerging security risks that goes beyond conventional traditional security concepts and governance mechanisms and focuses on the speed of a risks emergence and the geographic coverage of a risk as the two main analytic dimensions. Second, the characteristics of the governance models that correspond to the four types of emerging security risks derived from the analytical framework are categorized in terms of the core subjects and cooperation methods. Third, I focus on governance mechanisms and network strategies that enable a flexible transition to appropriate response models.
Based on this analytic framework, this study examines the theoretical adequacy of the the governance models by applying each type of response to major emerging security risk issues in Southeast Asia. Emerging security issues are frequently observed not only in the Western world, which has already transitioned to a postmodern society in many respects, but also in other regions where the heritage of modern political order prevails. In Southeast Asia, for example, various supranational security problems are rapidly increasing, while the emphasis on the unique modern sovereignty principle called 'ASEAN Way' remains in place. This paper explores the changes in various forms of governance in the proliferation of SARS, tsunami, haze, and bombing terrorism, which are all transnational issue that have continuously hit Southeast Asia in the 2000s. In this process, I focus on Singapore, which overcame the structural constraints of the ASEAN Way and played a leading role in establishing a global as well as a regional cooperation system.
Singapore tried the regional government-led approach in the face of the sudden/limited risk of a tsunami, and responded to bombing terrorismβa sudden/expanding risk at the global levelβby resorting to the cross-regional cooperation model encompassing the great powers. In addition, the regional multilateral actor participation strategy was applied to the haze, which was a incremental/limited risk issue at the regional level, and the global multilateral actor cooperation strategy was adopted for the SARS, an incremental/expanding risk at the global level.
In sum, in the face of transnational emerging security risks, Singapore has made a variety of attempts to set regional and global security agendas and build effective governance models, even when the risks do not directly cause damage to the country. Singapore's network strategy that emerged during this process could be a meaningful example of how the state can flexibly shift its approach to dealing with complex emerging security risks and how to effectively share and delegate authority. Furthermore, Singapore's case is also worth noting in terms of 'middle power diplomacy' in response to emerging security challenges.β
. μλ‘ 1
1. λ¬Έμ μ κΈ° 1
2. μ£Όμ κ΅μ μ μΉμ΄λ‘ μΌλ‘ λ³Έ μ ν₯μ보 κ±°λ²λμ€μ μ€λͺ
κ³Ό νκ³μ 4
1) νμ€μ£Όμμ κ΄μ 5
2) μμ μ£ΌμΒ·μ λμ£Όμμ κ΄μ 8
3) ꡬμ±μ£Όμμ κ΄μ 14
4) μμ¬μ κ³Ό λμμ μ κ·Όμ νμμ± 18
3. μ°κ΅¬ λ°©λ² 21
1) μ°κ΅¬μ μ΄μ 21
2) μ°κ΅¬ μ¬λ‘μ μ μ 22
3) μ°κ΅¬ λΆμν 24
4. λ
Όλ¬Έμ κ΅¬μ± 27
β
‘. μ΄λ‘ μ λΆμν 31
1. μν μ°κ΅¬μ μ΄λ‘ μ λ°°κ²½ 31
1) νκ·Όλ μνμ¬νμ λλ 31
2) μνμ μ¬νμ ꡬμ±κ³Ό μν΅ 35
2. κ±°λ²λμ€ λ©μ»€λμ¦μ μ΄λ‘ μ κ³ μ°° 39
1) μ΄κ΅κ°μ κ±°λ²λμ€μ λλ 39
2) κ±°λ²λμ€μ μλ κΈ°μ λ‘μμ κ±°λ²λ©ν€λ¦¬ν°(governmentality) 42
3. μ ν₯μ보 μ΄μμ λΆμκ³Ό λμ κ±°λ²λμ€ 44
1) λ―Έμμ μνμμ μ ν₯μ보 μ΄μλ‘μ μ ν 44
2) μ ν© κ±°λ²λμ€λ‘μ μ νμ μν λ€νΈμν¬ μ λ΅ 47
4. μ ν₯μ보 κ±°λ²λμ€μ μ§μμ λ³μ 49
1) μ ν₯μ보 μνμ΄μμ μ§μ κ±°λ²λμ€μ λ¬Έμ 49
2) λλ¨μμμ μ ν₯μ보μ μ§μ νμ λ³μ 51
5. μ ν₯μ보 μν μ νκ³Ό μ ν© κ±°λ²λμ€μ λΆμν 53
1) μ ν₯μ보 μνμ΄μμ λμμ λΆμν 53
2) μ ν₯μ보 μν μ νλ³ μ ν© κ±°λ²λμ€ λͺ¨λΈ 58
β
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1. μ ν₯μ보 μνμ΄μλ‘μ μ°λλ―Έμ νΉμ§ 62
1) 2004λ
μΈλμ μ°λλ―Έμ λ°μκ³Ό νκΈλ ₯ 62
2) λλ¨μμμ κ΅κ°λ€μ νΌν΄μ μ°Έμ¬ μμΈ 65
2. μ΄κΈ° λμ κ³Όμ μ λνλ λ¬Έμ μ 69
1) κΈλ‘λ² λ―Έλμ΄μ κ΄μ¬κ³Ό κ΅μ μ¬νμ μ§μ μλ 69
2) κ΅μ μ¬νμ μΈλμ μ§μ κ³Όμ μ λνλ λ¬Έμ λ€ 73
3) μ§μ μ€μ¬μ λμμ μν μ±κ°ν¬λ₯΄μ κ±°λ²λμ€ μ ν μλ 79
3. μ±κ°ν¬λ₯΄μ μλ΄ μ λΆ μ£Όλν λμ μ λ΅ 86
1) μ λΆμ£Όλμ λκ·λͺ¨ μ§μκ³Ό μΈνλΌμ μ¬κ±΄ 86
2) νΌν΄κ΅κ³Ό μμΈ μ λΆβ€NGOμμ μ€μ¬ μν 88
3) μ§μμ κ·λͺ¨μ μ¬λμ λν μμΈμ μ€μ¬μ λμμ²΄κ³ κ°ν λ
Έλ ₯ 91
4. μκ²° 100
β
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λ¬μ λλ°/μ°κ³ν μνμ΄μμ λμμ λ΅ 103
1. μ ν₯μ보 μνμ΄μλ‘μ ννν
λ¬μ νΉμ§ 103
1) ννν
λ¬κ° λλ¨μμμ μ보νκ²½μ κ°λ μλ―Έ 103
2) μνμ λμλ°©μμ λλ¬μΌ μ§μμ μ μ½ μμ 105
2. μ΄κΈ° λμ κ³Όμ μ λνλ λ¬Έμ μ 106
1) ννν
λ¬μ μνμ λν μμΈμμ μΈμ 106
2) μ±κ°ν¬λ₯΄μ λλ΄μΈμ μκΈ°μΈμκ³Ό κ±°λ²λμ€μ μ ν 107
3. μ±κ°ν¬λ₯΄μ μμΈ μ λΆ κ³΅μ‘°ν μ λ΅ 110
1) νν ν
λ¬μ μ§μμ보 μμ ν 110
2) μμΈμ λ΄ μ κ·Ήμ λμκ·Έλ£Ήμ λΆλ¦¬ 115
3) μμΈμ λ° μμΈμ νμμ ν¬μ μ λ΅ 118
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λ¬λ°©μ§ λ€νΈμν¬μ μ€κ°μλ‘ 123
4. μκ²° 126
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€. μ°λ¬΄μ μ μ¦/νμ ν μνμ΄μμ λμμ λ΅ 129
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1) μΈλλ€μμ μ°λ¬΄μ νΌν΄μ λ°μ μμΈ 130
2) μ°λ¬΄ λ¬Έμ λ₯Ό λλ¬μΌ λκ΄λ€ 133
2. μ΄κΈ° λμ κ³Όμ μ λνλ λ¬Έμ μ 135
1) μ±κ°ν¬λ₯΄-μΈλλ€μμ μ λΆ κ° μμ νμμ λ¬Έμ 135
2) μλ΄ μ λΆ λ° κ΅μ 기ꡬμμ 곡쑰 μλ 138
3. μ±κ°ν¬λ₯΄μ μλ΄ λ€μ νμμ μ°Έμ¬ μ λ΅ 141
1) NGOμμ μ λ΅μ μ°κ³ 141
2) κΈλ‘λ² νμ€μΌ κΈ°μ
μ λν μλ°κ³Ό μ€λ 145
3) νμ§ κ³΅λ체 λ° μλ―Όμ¬νμμ μ°λ 147
4. μκ²° 150
β
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1. μ ν₯μ보 μνμ΄μλ‘μ μ¬μ€μ νΉμ§ 153
1) 21μΈκΈ° μ΅μ΄μ κΈλ‘λ² λ³΄κ±΄μ보 μ΄μ μ¬μ€μ 좩격 153
2) μ€κ΅ μ λΆμ μνμ μ§κ΅¬μ νμ° κ³Όμ 156
3) μ¬μ€μ μμΈμ 보건μ보μ μκΈ° λλ 159
2. μ΄κΈ° λμ κ³Όμ μ λνλ λ¬Έμ μ 162
1) μ€κ΅ μ λΆμ μ 보 ν΅μ μ λ
μμ λμλ°©μμ νκ³ 162
2) λλ§ μ λΆμ κ³ λ¦½μ λμκ³Ό νκ³ 166
3) ν콩 μ λΆμ μ€κ΅ μμ‘΄κ³Ό νκ³ 170
3. μ±κ°ν¬λ₯΄μ κΈλ‘λ² λ€μ νμμ 곡쑰ν λμμ λ΅ 175
1) μ¬μ€μ νμ°κ³Ό μ±κ°ν¬λ₯΄μ κ΅λ΄μ μ보 μμ ν 175
2) μμ-μ§μ μ°¨μμ νλ ₯ μλ 181
3) ASEAN+3λ₯Ό ν¬κ΄νλ κ΄μ 곡쑰체μ λ§λ ¨ 184
4) WHO-κΈλ‘λ² λ€μ νμμμμ μ κ·Ήμ μ°κ³ 188
4. μκ²° 192
β
¦. κ²°λ‘ 194
1. μ ν₯μ보 μ νκ³Ό μ ν© κ±°λ²λμ€ 194
1) λλ°/νμ ν μ ν₯μ보 μ΄μμ μλ΄ μ λΆ μ£Όλν μ λ΅ 194
2) λλ°/μ°κ³ν μ ν₯μ보 μ΄μμ μμΈ μ λΆ κ³΅μ‘°ν μ λ΅ 195
3) μ μ¦/νμ ν μ ν₯μ보 μ΄μμ μλ΄ λ€μ νμμ μ°Έμ¬ν μ λ΅ 196
4) μ μ¦/μ°κ³ν μ ν₯μ보 μ΄μμ κΈλ‘λ² λ€μ νμμ 곡쑰ν μ λ΅ 197
2. μ ν© κ±°λ²λμ€μ λ©μ»€λμ¦κ³Ό μ±κ°ν¬λ₯΄μ μ ν μλ 199
3. λ€νΈμν¬ κ΅κ°μ μΈκ΅μ λ΅μ ν¨μ 204
μ°Έκ³ λ¬Έν 207
Abstract 228
λΆλ‘ 231Docto
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