Purpose To determine the relationship between tumor heterogeneity assessed by means of magnetic resonance (MR) imaging texture analysis and survival outcomes in patients with primary breast cancer. ...Materials and Methods Between January and August 2010, texture analysis of the entire primary breast tumor in 203 patients was performed with T2-weighted and contrast material-enhanced T1-weighted subtraction MR imaging for preoperative staging. Histogram-based uniformity and entropy were calculated. To dichotomize texture parameters for survival analysis, the 10-fold cross-validation method was used to determine cutoff points in the receiver operating characteristic curve analysis. The Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of texture parameters and morphologic or volumetric information obtained at MR imaging or clinical-pathologic variables with recurrence-free survival (RFS). Results There were 26 events, including 22 recurrences (10 local-regional and 12 distant) and four deaths, with a mean follow-up time of 56.2 months. In multivariate analysis, a higher N stage (RFS hazard ratio, 11.15 N3 stage; P = .002, Bonferroni-adjusted α = .0167), triple-negative subtype (RFS hazard ratio, 16.91; P < .001, Bonferroni-adjusted α = .0167), high risk of T1 entropy (less than the cutoff values mean, 5.057; range, 5.022-5.167, RFS hazard ratio, 4.55; P = .018), and T2 entropy (equal to or higher than the cutoff values mean, 6.013; range, 6.004-6.035, RFS hazard ratio = 9.84; P = .001) were associated with worse outcomes. Conclusion Patients with breast cancers that appeared more heterogeneous on T2-weighted images (higher entropy) and those that appeared less heterogeneous on contrast-enhanced T1-weighted subtraction images (lower entropy) exhibited poorer RFS.
RSNA, 2016 Online supplemental material is available for this article.
Thyroid nodules are initially handled by fine needle aspiration (FNA). However, the stance of thyroid core needle biopsy (CNB) still is a challenge. This study aimed to compare the diagnostic ...performances and conclusive rates of FNA and CNB for the diagnosis of thyroid nodules and to define effective indications of CNB. This retrospective study enrolled 1,060 consecutive thyroid nodules in 1,037 patients who underwent FNA from January 2008 to May 2008, and 462 consecutive nodules in 453 patients who underwent CNB from January 2014 to December 2015 at our institution. Ultrasound (US) features were classified according to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) and Korean TIRADS (K-TIRADS). We compared diagnostic performances and conclusive rates between FNA and CNB groups. Propensity score matching was conducted to match FNA patients with CNB patients. After matching, the diagnostic performances for selecting surgical candidates and predicting malignancy were comparable between the two biopsy groups. Based on US findings, conclusive results were obtained significantly more in CNB than in FNA when thyroid nodules were classified as ACR TI-RADS or K-TIRADS category 4 and measured larger than 2 cm. Diagnostic performances between FNA and CNB were comparable. Superiority of CNB to FNA was found for thyroid nodules larger than 2 cm and classified as ACR TI-RADS or K-TIRADS category 4.
The benefit of neuromuscular blockades (NMBs) in critically ill patients receiving mechanical ventilation remains uncertain. Therefore, we aimed to investigate whether NMB use is associated with ...improved survival of mechanically ventilated pneumonia patients with moderate to severe hypoxemia.
This retrospective multicenter study was conducted at five university-affiliated hospitals. Data of pneumonia patients aged 18 years and older who received mechanical ventilation between January 1, 2011, and December 31, 2020, were analyzed.
In a total of 1,130 patients, the mean patient age was 73.1 years (SD±12.6), and the overall mortality rate at 30 d was 29.5% (n = 333). NMB users had a higher 30 d mortality rate than NMB nonusers (33.9% vs. 26.8%, P = 0.014). After PS matching, the 30 d mortality rate was not significantly different between NMB users and nonusers (33.4% vs. 27.8%, p = 0.089). However, 90 d mortality rate was significantly increased in NMB users (39.7% vs. 31.9%, p = 0.021). Univariable Cox proportional hazard regression analyses showed that NMB use ≥ 3 d was significant risk factor for the 90 d mortality than those with < 3 d use (90 d mortality HR 1.39 95% CI: 1.01-1.91, P = 0.045).
NMB use was not associated with lower 30 d mortality among mechanically ventilated pneumonia patients with moderate to severe hypoxemia. Rather, NMB users had higher 90 d mortality, furthermore, and NMB use ≥ 3 d was associated with a higher risk of long-term mortality compared to NMB use < 3 d. Therefore, care should be taken to avoid extended use of NMB in critically ill pneumonia patients during mechanical ventilation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We introduce a data-adaptive nonparametric dimension reduction tool to obtain a low-dimensional approximation of functional data contaminated by erratic measurement errors following symmetric or ...asymmetric distributions. We propose to apply robust submatrix completion techniques to matrices consisting of coefficients of basis functions calculated by projecting the observed trajectories onto a given orthogonal basis set. In this process, we use a composite asymmetric Huber loss function to accommodate domain-specific erratic behaviors in a data-adaptive manner. We further incorporate the
L
1
penalty to regularize the smoothness of latent factor curves. The proposed method can also be applied to partially observed functional data, where each trajectory contains individual-specific missing segments. Moreover, since our method does not require estimating the covariance operator, the extension to any dimensional functional data observed over a continuum is straightforward. We demonstrate the empirical performance in estimating lower-dimensional space and reconstruction of trajectories of the proposed method through simulation studies. We then apply the proposed method to two real datasets, one-dimensional Advanced Metering Infrastructure (AMI) data in South Korea and two-dimensional max precipitation spatial data collected in North America and South America.
This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose ...a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.
Effective chronic disease management requires the active participation of patients, communities, and physicians. The objective of this study was to estimate the effectiveness of the Community-based ...Registration and Management for elderly patients with Hypertension or Type 2 Diabetes mellitus Project (CRMHDP) by using motivated primary care physicians and patients supported by prepared communities, to utilise healthcare and health outcomes in four cities in South Korea. We conducted a propensity score-matched retrospective cohort study using 2010-2011 as the baseline years, alongside a follow-up period until 2015/2016, based on the Korean National Health Insurance database. Both a CRMHDP group (n = 46,865) and a control group (n = 93,730) were applied against healthcare utilisation and difference-in-differences estimations were performed. For the health outcome analysis, the intervention group (n = 27,242) and control group (n = 54,484) were analysed using the Kaplan-Meier method and Cox proportional hazard regression. Results: The difference-in-differences estimation of the average annual clinic visits per person and the average annual days covered were 1.26 (95% confidence interval, 1.13-1.39) and 22.97 (95% CI, 20.91-25.03), respectively, between the intervention and control groups. The adjusted hazard ratio for death in the intervention group, compared to the control group, was 0.90 (95% CI, 0.86-0.93). For stroke and chronic renal failure, the adjusted hazard ratios for the intervention group compared to the control group were 0.94 (95% CI, 0.88-0.99) and 0.80 (95% CI 0.73-0.89), respectively. Our study suggests that for effective chronic disease management both elderly patients and physicians need to be motivated by community support.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along ...the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data.
This study examines the problem of robust spectral analysis of long-memory processes. We investigate the possibility of using Laplace and quantile periodograms for a non-Gaussian distribution ...structure. The Laplace periodogram, derived by the least absolute deviations in the harmonic regression procedure, demonstrates its superiority in handling heavy-tailed noise and nonlinear distortion. In this study, we discuss an asymptotic distribution of the Laplace periodogram for long-memory processes. We also derive an asymptotic distribution of the quantile periodogram. Through numerical experiments, we demonstrate the robustness of the Laplace periodogram and the usefulness of the quantile periodogram in detecting the hidden frequency for the spectral analysis of the long-memory process under non-Gaussian distribution. Moreover, as an application of robust periodograms under the long-memory process, we discuss the long-memory parameter estimation based on a log periodogram regression approach.
In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal ...components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.
동적요인모형에 기반한 한국의 GDP 성장률 예측 이경서(Kyoungseo Lee); 임예지(Yaeji Lim)
Ŭngyong tʻonggye yŏnʼgu,
2024, Letnik:
37, Številka:
2
Journal Article
Odprti dostop
GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a ...representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID. GDP는 한 나라의 가계, 기업, 정부 등 모든 경제 주체가 일정 기간 동안 창출한 재화와 서비스의 시장 가치의 합을 나타낸다. GDP를 통하여 국가의 경제 규모를 파악할 수 있으며, 정부의 정책 방향에 영향을 미치는 대표적인 경제 지표이므로 이에 대한 연구가 다양하게 이루어지고 있다. 본 논문에서는 G20 국가들의 주요 거시경제 지표를 활용하여 dynamic factor model 기반의 GDP 성장률 예측 모델을 제시하였다. 추출된 factor를 다양한 회귀분석 방법론과 결합하여 그 결과들을 비교하였으며, 기존의 전통적인 시계열 예측방법인 ARIMA 모델, common component를 이용한 예측 등도 함께 비교하였다. COVID 이후 지표의 변동성이 큰 점을 고려하여 예측 시기를 COVID 전후로 나누었으며, 그 결과 factor에 대해 ridge regression과 lasso regression을 적용하여 예측한 경우 가장 좋은 성능을 나타내었다.
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