Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.
Objectives: The objective of this study is to investigate the relationship among professional self-concept, self-esteem and job satisfaction in the clinical dental hygienists and provide the basic data for the effective management of clinical dental hygienists. Methods: A self-reported questionnaire was filled out by 250 clinical dental hygienists in Seoul, Gyeonggi, Chungcheong, Jeolla, and Gyeongsang Provinces from February 1 to March 30, 2017. Data were analyzed using IMB SPSS/WIN 22.0 program. Professional self-concept, self-esteem and job satisfaction according to the general characteristics and job characteristics were analzyed by independent t-test, one way ANOVA and Tukey. The relationship among professional self-concept, self-esteem and job satisfaction was found by Pearson's correlation. Multiple regression analysis was used to find out the relationship among professional self-concept, self-esteem and job satisfaction in the clinical dental hygienist. The significance level was a=0.05. Results: Clinical dental hygienist's professional self-concept was 3.76 points, self-esteem was 3.08 points and job satisfaction was 3.35 points. In general characteristics, professional self-concept showed the significant differences by age (p=0.043), marital status (p=0.005), level of education (p=0.001), duration of clinical career (p=0.003) and economic level (p=0.001). Job satisfaction showed the significant differences by age (p=0.019) and level of education (p=0.024). In job related characteristic, professional self-concept showed the significant differences by working area (p=0.001), working intensity (p=0.011) and position (p=0.001), while job satisfaction showed the significant differences by working area (p=0.014) and working intensity (p=0.001). Influencing factors in the job satisfaction in the clinical dental hygienists were age, working intensity, self-esteem, and professional self-concept. The explanatory of the model was 29.0%. Conclusions: Professional self-concept, self-esteem and job satisfaction in the clinical dental hygienists are related. To improve job satisfaction of the clinical dental hygienists, program development and institutional support to improve self-esteem and professional self-concept are needed.
Changsoo Woo;Kwan Hyeong Jo;Beomseok Sohn;Kisung Park;Hojin Cho;Won Jun Kang;Jinna Kim;Seung-Koo Lee
Korean Journal of Radiology
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제24권1호
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pp.51-61
/
2023
Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.
Subhanik Purkayastha;Yanhe Xiao;Zhicheng Jiao;Rujapa Thepumnoeysuk;Kasey Halsey;Jing Wu;Thi My Linh Tran;Ben Hsieh;Ji Whae Choi;Dongcui Wang;Martin Vallieres;Robin Wang;Scott Collins;Xue Feng;Michael Feldman;Paul J. Zhang;Michael Atalay;Ronnie Sebro;Li Yang;Yong Fan;Wei-hua Liao;Harrison X. Bai
Korean Journal of Radiology
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제22권7호
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pp.1213-1224
/
2021
Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.
Ahn, Suk-Won;Yoon, Byung-Nam;Kim, Jee-Eun;Seok, Jin Myoung;Kim, Kwang-Kuk;Lim, Young Min;Kwon, Ki-Han;Park, Kee Duk;Suh, Bum Chun;Korean Society of Clinical Neurophysiology Education Committee
Annals of Clinical Neurophysiology
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제20권2호
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pp.71-78
/
2018
Nerve conduction study (NCS) is an electrophysiological tool to assess the overall function of cranial and peripheral nervous system, therefore NCS has been diagnostically helpful in the identification and characterization of disorders involving nerve roots, peripheral nerves, muscle and neuromuscular junction, and are frequently accompanied by a needle Electromyography. Furthermore, NCS could provide valuable quantitative and qualitative results into neuromuscular function. Usually, motor, sensory, or mixed nerve studies can be performed with using NCS, stimulating the nerves with the recording electrodes placed over a distal muscle, a cutaneous sensory nerve, or the entire mixed nerve, respectively. And these findings of motor, sensory, and mixed nerve studies often show different and distinct patterns of specific abnormalities indicating the neuromuscular disorders. The purpose of this special article is to review the neurophysiologic usefulness of NCS, to outline the technical factors associated with the performance of NCS, and to demonstrate characteristic NCS changes in the setting of various neuromuscular conditions.
Objective : This study aimed to show the possibility of neural canal enlargement and restoration of bony fragments through laminectomy and minimal facetectomy without pediculectomy or an anterior approach, and also to prove the adequacy of posterior stabilization of vertebral deformities after thoracolumbar bursting fracture. Methods : From January 2003 to June 2009, we experienced 45 patients with thoracolumbar burst fractures. All patients enrolled were presented with either a neural canal compromise of more than 40% with a Benzel-Larson Grade of VI, or more than 30% compromise with less than a Benzel-Larson Grade of V. Most important characteristic of our surgical procedure was repositioning retropulsed bone fragments using custom-designed instruments via laminectomy and minimal facetectomy without removing the fractured bone fragments. Beneath the dural sac, these custom-designed instruments could push the retropulsed bone fragments within the neural canal after the decompression and bone fragment repositioning. Results : The mean kyphotic deformities measured preoperatively and at follow-up within 12 months were 17.7 degrees (${\pm}6.4$ degrees) and 9.6 degrees (${\pm}5.2$ degrees), respectively. The mean midsagittal diameter improved from 8.8 mm (${\pm}2.8$ mm) before surgery to 14.2 mm (${\pm}1.6$ mm) at follow-up. The mean traumatic vertebral body height before surgery was 41.3% (${\pm}12.6%$). At follow-up assessment within 12 months, this score showed a statistically significant increase to 68.3% (${\pm}12.8%$). Neurological improvement occurred in all patients. Conclusion : Though controversy exists in the treatment of severe thoracolumbar burst fracture, we achieved effective radiological and clinical results in the cases of burst fractures causing severe canal compromise and spinal deformity by using this novel custom-designed instruments, via posterior approach alone.
Temporomandibular joint(TMJ) disorders have characteristic clinical findings such as pain, joint sound, and abnormal jaw function. With the rapid progress in TMJ imaging techniques, magnetic resonance imaging(MRI) especially provides the clinician and researcher with the ability to evaluate TMJ disorders(effusion, anterior disk displacement) and the hard and soft tissue of the TMJ. In order to manage the internal derangement of TMJ such as anterior disk displacement, the so-called appliance therapy is commonly used. This technique became the treatment of choice before surgical intervention by many practitioners. The purpose of the present study was to evaluate and determine the efficacy of anterior repositioning splint by means of the change of the position of the disk through pre- and post-treatment MRI. MRI and clinical symptom were carefully recorded in TMJ disorder patients. The relationship between TMJ symptoms such as pain, clicking sound, and disk displacement and effusion in MRI were analyzed. Then, splint therapy was applied for 3 months according to our protocol; at day 1, patients should wear 24 hours, in order to accomodate the splint and then, wearing time is reduced to 12 hours per day for one month. During next 2 months, it is tapered to 8 hours per day according to the evaluation of the clinical symptoms. Post-treatment MRI was obtained after 3 month protocol. This treatment strategy improved the clinical symptoms of TMJ disorder, and effusion and articular disk position showed a significant change. In conclusion, a treatment modality using an anterior repositioning splint therapy is an appropriate method for temporomandibular disorder patients.
This study was conducted to verify the current criteria and classification system used to determine specialized general hospitals status. In this study, we proposed a new classification system which Is simpler and more convenient than the current one. In the new classification system clinical procedure was chosen as the unit of analysis in order to reflect all the resource consumption and the complexities and degree of medical technologies in determining specialized general hospitals. We developed a statistical model and applied this model to 117 general hospitals which claim their national insurance through electronic data interchange(EDI). Analysis based on 984 clinical procedures and medical facilities' characteristic variable discriminated specialized general hospital in present without misclassification. It means that we can determine specialized general hospital's permission In new way without using the current complicated criteria. This study discriminated specialized general hospital by the new proposed model based on clinical procedures provided by each hospital. For clustering the same types of medical facilities using 984 clinical procedures, we executed multidimensional scale analysis and divided 117 hospitals into 4 groups by two axises : a variety of procedure and the Proportion of high technology Procedure. Therefore, we divided 117 hospitals into 4 groups and one of them was considered as specialized general hospital. In discriminating analysis, we abstracted proportion of 16 clinical procedures which effect on discriminating the specialized general hospital in statistical system also we identify discriminating function which include these variables. As a result, we identify 2 discriminating functions, one is for current discriminating system and the other two is for new discriminating system of specialized general hospital.
Purpose: The clinical presentations of inflammatory bowel disease (IBD) prior to diagnosis are so diverse or vague that many of them waste time before final diagnosis. This study was undertaken to know the medical history of the pediatric patients until the final diagnosis could be reached. Methods: The medical records of all pediatric patients who were diagnosed with IBD (Crohn's disease [CD] in 14 children, ulcerative colitis [UC] in 17) during the last 13 years were reviewed. We investigated the length of the diagnostic time lag, chief clinical presentation, and any useful laboratory predictor among the routinely performed examinations. Indeterminate colitis was not included. Results: The mean ages of children at the final diagnosis was similar in both diseases. As for the pre-clinical past history of bowel symptoms in CD patients, 5 were previously healthy, 9 had had 1-3 gastrointestinal (GI) symptoms, weight loss, bloody stool, anemia and rectal prolapse. With UC, 9 were previously healthy, 8 had had 1-3 GI symptoms, bloody stool, anorexia. The average diagnostic time lag with CD was 3.36 months, and with UC 2.2 months. Body mass index (BMI) and the initial basic laboratory data (white blood cell, hemoglobin, mean corpuscular volume, serum albumin, and serum total protein) were lower in CD, statistically significant only in BMI. Conclusion: IBD shows diverse clinical symptoms before its classical features, making the patients waste time until diagnosis. It is important to concern possibility of IBD even in the mildly sick children who do not show the characteristic symptoms of IBD.
Purpose: Early identification of neonatal sepsis is a global issue because of limitations in diagnostic procedures. The objective of this study was to compare the diagnostic accuracy of neutrophil CD64 and C-reactive protein (CRP) as a single test for the early detection of neonatal sepsis. Methods: A prospective study enrolled newborns with documented sepsis (n=11), clinical sepsis (n=12) and control newborns (n=14). CRP, neutrophil CD64, complete blood counts and blood culture were taken at the time of the suspected sepsis for the documented or clinical group and at the time of venipuncture for laboratory tests in control newborns. Neutrophil CD64 was analyzed by flow cytometry. Results: CD64 was significantly elevated in the groups with documented or clinical sepsis, whereas CRP was not significantly increased compared with controls. For documented sepsis, CD64 and CRP had a sensitivity of 91% and 9%, a specificity of 83% and 83%, a positive predictive value of 83% and 33% and a negative predictive value of 91% and 50%, respectively, with a cutoff value of 3.0 mg/dL for CD64 and 1.0 mg/dL for CRP. The area under the receiver-operating characteristic curves for CD64 index and CRP were 0.955 and 0.527 ($P$ <0.01), respectively. Conclusion: These preliminary data show that diagnostic accuracy of CD64 is superior to CRP when measured at the time of suspected sepsis, which implies that CD64 is a more reliable marker for the early identification of neonatal sepsis as a single determination compared with CRP.
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