• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1826-1833
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• 2020

Administration of stable iodine has been considered a best measure to protect the thyroid from internal irradiation by radioiodine intake, and its efficacy on thyroid protection has been quantitatively evaluated in several simulation studies on the basis of simple iodine biokinetic models (i.e., three-compartment model). However, the new iodine biokinetic model adopted by the International Commission on Radiological Protection interprets and expresses the thyroid blocking phenomenon differently. Therefore, in this study, the new model was analyzed in terms of thyroid blocking and implemented to reassess the protective effects and to produce dosimetric data. The biokinetic model calculation was performed using computation modules developed by authors, and the results were compared with those of experimental data and prior simulation studies. The new model predicted protective effects that were generally consistent with those of experimental data, except for those in the range of stable iodine administration -72 h before radioiodine exposure. Additionally, the dosimetric data calculated in this study demonstrates a critical limitation of the three-compartment model in predicting bioassay functions, and indicated that dose assessment 1 d after exposure would result in a similar dose estimate irrespective of the administration time of stable iodine.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.317-325
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• 2024

This study aimed to classify normal and nodule images in thyroid ultrasound images using GLCM and machine learning. The research was conducted on 600 patients who visited S Hospital in Busan and were diagnosed with thyroid nodules using thyroid ultrasound. In the thyroid ultrasound images, the ROI was set to a size of 50x50 pixels, and 21 parameters and 4 angles were used with GLCM to analyze the normal thyroid patterns and thyroid nodule patterns. The analyzed data was used to distinguish between normal and nodule diagnostic results using the SVM model and KNN model in MATLAB. As a result, the accuracy of the thyroid nodule classification rate was 94% for SVM model and 91% for the KNN model. Both models showed an accuracy of over 90%, indicating that the classification rate is excellent when using machine learning for the classification of normal thyroid and thyroid nodules. In the ROC curve, the ROC curve for the SVM model was generally higher compared to the KNN model, indicating that the SVM model has higher within-sample performance than the KNN model. Based on these results, the SVM model showed high accuracy in diagnosing thyroid nodules. This result can be used as basic data for future research as an auxiliary tool for medical diagnosis and is expected to contribute to the qualitative improvement of medical services through machine learning technology.

• Journal of Radiation Protection and Research
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• v.19 no.1
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• pp.69-80
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• 1994

The TCMI(Three-Compartment Model for iodine) computer code has been developed, which is based on the three-compartment model and the respiratory model recommended in ICRP publication 54. This code is able to evaluate the thyroid burden, dose equivalent, committed dose equivalent and urinary excretion rate as time-dependent functions from the input data: working time and the radioiodine concentration in air. Using the TCMI code, the time-dependent thyroid burdens, the thyroid doses and the urinary excretion rates were calculated for three specific exposure patterns : acute, chronic and periodic. Applicability as an internal dose evaluation method has been assessed by comparing the results with some operational experiences. Simple equations and tables are provided to be used in the evaluation of the thyroid burden and the resulting doses for given I-131 concentration in air and the working time.

• Journal of the Korean Society of Radiology
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• v.81 no.5
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• pp.1164-1174
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• 2020

Purpose To evaluate a deep learning model to predict recurrence of thyroid tumor using preoperative ultrasonography (US). Materials and Methods We included representative images from 229 US-based patients (male:female = 42:187; mean age, 49.6 years) who had been diagnosed with thyroid cancer on preoperative US and subsequently underwent thyroid surgery. After selecting each representative transverse or longitudinal US image, we created a data set from the resulting database of 898 images after augmentation. The Python 2.7.6 and Keras 2.1.5 framework for neural networks were used for deep learning with a convolutional neural network. We compared the clinical and histological features between patients with and without recurrence. The predictive performance of the deep learning model between groups was evaluated using receiver operating characteristic (ROC) analysis, and the area under the ROC curve served as a summary of the prognostic performance of the deep learning model to predict recurrent thyroid cancer. Results Tumor recurrence was noted in 49 (21.4%) among the 229 patients. Tumor size and multifocality varied significantly between the groups with and without recurrence (p < 0.05). The overall mean area under the curve (AUC) value of the deep learning model for prediction of recurrent thyroid cancer was 0.9 ± 0.06. The mean AUC value was 0.87 ± 0.03 in macrocarcinoma and 0.79 ± 0.16 in microcarcinoma. Conclusion A deep learning model for analysis of US images of thyroid cancer showed the possibility of predicting recurrence of thyroid cancer.

• Korean Journal of Radiology
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• v.20 no.1
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• pp.166-170
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• 2019

Objective: To establish a cost-effective and easily available phantom for training residents in ultrasound-guided fine needle thyroid nodule targeting punctures. Materials and Methods: Tofu, drinking straws filled with coupling gel, a urine tube, and 21-gauge needles were used to generate a phantom thyroid with nodules for training. Twelve radiology residents were involved in the study. The puncture success rates were recorded and compared before and after phantom training using the Wilcoxon signed-rank test. Results: On ultrasonography, tofu mimicked the texture of the thyroid. Drinking straws filled with coupling gel mimicked vessels. The urine tube filled with air mimicked the trachea, and 21-gauge needles mimicked small nodules in the transverse section. The entire phantom was similar to the structure of the thyroid and surrounding tissues. The puncture success rates of radiology residents were significantly increased from 34.4 ± 14.2% to 66.7 ± 19.5% after training (p = 0.003). The phantom was constructed in approximately 10 minutes and materials cost less than CNY 10 (approximately $ 1.5) at a local store. Conclusion: The tofu model was cost-effective, easily attainable, and effective for training residents in ultrasound-guided fine needle thyroid nodule targeting punctures in vitro.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6385-6390
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• 2012

A number of studies have been conducted to explore the association of XRCC1 polymorphisms with thyroid cancer risk, but the results have been inconsistent. Thus we performed the present meta-analysis to clarify this issue based on all of the evidence available to date. Relevant studies were retrieved by searching PubMed and statistical analysis conducted using Stata software. Nine studies were included in this meta-analysis (1,620 cases and 3,557 controls). There were 6 studies (932 cases and 2,270 controls) of the Arg194Trp polymorphism, 7 studies (1432 cases and 3356 controls) of the Arg280His polymorphism and 9 studies (1,620 cases and 3,557 controls) for the Arg399Gln polymorphism. No association of XRCC1 Arg194Trp, Arg280His and Arg399Gln polymorphism with thyroid cancer risk was observed in the overall analysis. However, subgroup analysis revealed: 1) an elevated risk in aa vs AA analysis (OR=2.03, 95%CI= 1.24-3.31) and recessive genetic model analysis (OR=1.93, 95%CI= 1.20-3.08) in the larger sample size trials for XRCC1 Arg194Trp polymorphism; 2) a decreased thyroid cancer risk on subgroup analysis based on ethnicity in Aa vs AA analysis (OR=0.84, 95%CI= 0.72-0.98) and in a dominant genetic model (OR=0.84, 95%CI= 0.72-0.97) in Caucasian populations for the XRCC1 Arg399Gln polymorphism; 3) a decreased thyroid cancer risk on subgroup analysis based on design type in Aa vs AA analysis (OR=0.72, 95% CI= 0.54-0.97) among the PCC trials for the Arg399Gln polymorphism. Our results suggest that the XRCC1 Arg399Gln polymorphism may be associated with decreased thyroid cancer risk among Caucasians and XRCC1 Arg194Trp may be associated with a tendency for increased thyroid cancer risk in the two larger sample size trials.

• Nutrition Research and Practice
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• v.10 no.3
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• pp.336-341
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• 2016

BACKGROUND/OBJECTIVES: The incidence of thyroid cancer has increased in many countries, including Korea. International differences in the incidence of thyroid cancer may indicate a role of diet, but findings from previous studies are inconclusive. Therefore, we aimed to investigate the roles of nutrients in thyroid cancer risk in Korean women. SUBJECTS/METHODS: We conducted a case-control study comprising 113 cases and 226 age-matched controls. Nutrient intake was assessed using a validated food frequency questionnaire, and the association between nutrient intake and thyroid cancer risk was estimated using a logistic regression model. RESULTS: We found that high calcium intake was associated with a reduced risk of thyroid cancer (OR [95% CI] = 0.55 [0.35-0.89]). Significant associations were observed among subjects who were older than 50 years, had low BMI, and had low calorie intake. However, other nutrients included in this study did not show any significant associations with thyroid cancer risk. CONCLUSION: This study suggested a possible protective effect of calcium on thyroid cancer risk. Well-designed prospective studies are required to confirm these findings.

• Journal of Korean society of Dental Hygiene
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• v.20 no.3
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• pp.269-279
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• 2020

Objectives: Previous studies have reported inconsistent findings in the association between dental diagnostic X-ray exposure and thyroid cancer. This study was a meta-analysis of case-control studies evaluating the association between exposure to dental radiation and the thyroid cancer risk. Methods: We searched the PubMed and EMBASE databases to identify studies on dental radiation and thyroid cancer risks that were published up to September 2018. Quality of studies was assessed using the Newcastle-Ottawa scale. A fixed-effects model was used to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs) using STATA 14.0. Potential publication biases were evaluated using Egger's test and Begg's funnel plot. Results: From the literature search, we included six case-control studies in this meta-analysis. The meta-analysis using the fixed-effects model found that dental X-ray exposure was associated with 2.34 times increased risk for thyroid cancer (OR=2.34, 95% CI=1.79-3.21). There was no heterogeneity in the data (p=0.662, I² =0%). Egger's test showed that there was no publication bias (p=0.532). Conclusions: This meta-analysis confirmed the association of dental X-ray exposure and thyroid cancer risk. The current results underscore the importance of applying safety regulations at dental clinics to protect thyroid glands during dental radiography examinations.

• Medical Lasers
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• v.10 no.1
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• pp.37-44
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• 2021

Background and Objectives Hypothyroidism is the most common endocrine disease. On the other hand, there is no treatment that can improve the thyroid function. Low-level laser therapy (LLLT) can improve the cellular activity. The effect of hypothyroidism is not obvious. This study examined the effects of LLLT on the thyroid gland function of a propylthiouracil (PTU)-induced hypothyroidism mouse model. Materials and Methods Twenty-five male ICR mice were distributed into five groups of five animals each: Negative control (none PTU animal) and positive control (PTU animal) of unirradiated animals, and three experimental groups with LLLT (3J, 6J, and 12J). Each mouse was exposed to a distinct dose of a 632-nm laser once a week for three rounds. The positive control group and three LLLT groups were induced into a hypothyroidism state by PTU administration. The animals' thyroid-stimulating hormone and thyroxine levels were measured using an ELISA kit, and their thyroid tissue was harvested and analyzed after sacrifice at the end of the experiment. The hormone level and morphological changes in the tissue of the five groups were compared. Results The thyroid hormone levels in the control group and LLLT groups were similar. On the other hand, the thyroid tissue of the LLLT groups showed some morphological changes that were similar to those of iodine deficiency thyroid. Conclusion LLLT did not affect the thyroid gland function in PTU-induced hypothyroidism mice.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7111-7115
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• 2015

Background: The aim of the meta-analysis was to derive a more precise assessment of the association between p16 promoter methylation and thyroid cancer risk. Materials and Methods: The PubMed, Web of Science databases and Chinese CNKI were searched for relevant articles. Ultimately, seventeen case-control studies were included with a total of 804 thyroid cancer cases and 487 controls analysis by R Software (R version 3.1.2) including meta. Crude odds ratios with 95% confidence intervals were calculated using the random-effects model which were used to assess the strength of relationship between p16 methylation and lung carcinogenesis. Funnel plots were carried out to evaluate publication bias. Results: The meta-analysis results showed that the frequency of p16 promoter methylation in cancer tissue/blood was significantly higher than that normal tissue/blood (OR=5.46, 95%CI 3.12-9.55, P<0.0001) by random effects model with small heterogeneity. Conclusions: Thus, p16 promoter methylation may be associated with thyroid cancer risk.