Objective To reveal the relationship between the iodine nutrition and the change of spectrum of thyroiddiseases by analyzing the change of spectrum of thyroid diseases in different iodine environments before and after the implementation of universal salt iodization （USI）. Methods To compare the urinary iodine concentration between the normal people （1 000 cases） and the patients with thyroid diseases （5 998 cases） by surgical operations who were from 4 cities of Guangxi Zhuang Autonomous Region， covering the iodine deficient areas and the iodine rich areas. Results After the USI was put into practice， the proportions of nodular goiter decreased， but the proportions of toxic nodular goiter， thyroid papillary carcinoma， and Hashimoto thyroiditis were higher than those before USI （P＜0.05）. The urinaryiodine concentrations of nodular goiter， Graves disease， toxic nodular goiter， thyroid papillary carcinoma， and Hashimotothyroiditis were higher than those before the measure was taken （P＜0.05）. The urinary iodine concentration of patients with thyroid was higher than that of normal people （P＜0.05）， and the urinary iodine concentration of patients with thyroidand normal people was higher than those before the USI （P＜0.05）. Conclusions The change of spectrum of thyroid diseases in Guangxi Zhuang Autonomous Region is obvious within 10 years after USI had been taken. The excessive intake of iodine may be one of the dangerous factors leading to toxic nodular goiter， thyroid papillary carcinoma， and Hashimoto thyroiditis.
Diagnosis is the critical component of health care and the studies of diagnostic test can provide important evidence for clinical decisions. Studies of diagnostic test are subject to different sources of bias in design, performance and reporting of studies. Therefore, researchers who understand various sources of bias can reasonably perform the diagnostic test and evaluate its quality, and will provide scientific evidences for clinical practice.
ObjectiveTo compare the effectiveness of T2 weighted image (T2WI) and some compounded MRI techniques, including T2WI combined with magnetic resonance spectroscopy (T2WI+MRS), T2WI combined with diffusion weighted imaging (T2WI+DWI) and T2WI combined with dynamic contrast-enhancement [T2WI+(DCE-MRI)] respectively, with 1.5 T MR scanner in diagnosing prostate cancer through a blinding method. MethodsBetween March 2011 and April 2013, two observers diagnosed 59 cases with a blinding method. The research direction of radiologist A was to diagnose prostate cancer. The observers diagnosed and scored the cases with T2WI, T2WI+(DCE-MRI), T2WI+MRS, T2WI+DWI and compositive method respectively. The data were statistically analyzed with receiver operating characteristic (ROC) curve. ResultsAccording to the ROC curve, both observers got the sequence of area under curve (AUC) as T2WI+DWI > T2WI+(DCE-MRI) > T2WI+MRS > T2WI. On the basis of the result from observer A, the AUC from each technique was similar. The AUC of T2+DWI was slightly bigger than others. The specificity of single T2WI was the lowest; the sensitivity of T2WI was slightly higher. The AUC of the compositive method was marginally larger than T2WI+DWI. According to the result from observer B, the AUC of T2WI+DWI was obviously larger than the others. The AUC of single T2WI was much smaller than the other techniques. The single T2WI method had the lowest sensitivity and the highest specificity. The AUC of T2WI+DWI was slightly larger than the compositive method. The AUC of T2WI+(DCE-MRI), T2WI+MRS, single T2WI methods from observer A was obviously higher than those from the score of observer B. The AUC of T2WI+DWI from the two observers was similar. ConclusionThe method of combined T2WI and functional imaging sequences can improve the diagnosing specificity when a 1.5 T MR scanner is used. T2WI+DWI is the best method in diagnosing prostate cancer with least influence from the experience of observers in this research. The compositive method can improve the diagnosis of prostate cancer effectively, but when there are contradictions between different methods, the T2WI+DWI should be considered as a key factor.
ObjectiveTo explore the impact of different monochromatic reconstruction on image quality of early lesions of coronavirus disease 2019 (COVID-19).MethodsThe chest spectral CT images of 11 patients confirmed as COVID-19 in West China Hospital of Sichuan University between January and February 2020 were retrospectively analyzed. A total of 34 inflammatory lesions were found in the 11 cases. Seven groups of images were reconstructed from the raw data for each patient, including the conventional polychromatic image and different monochromatic images of 40-140 keV (with intervals of 20 keV). CT and standard deviation (SD) values of all lesions were measured to calculate the signal-noise ratio (SNR) and contrast-noise ratio (CNR). The image quality was subjectively scored by two radiologists, and the differences in image quality among different monochromatic groups and the polychromatic group were compared.ResultsWith the increase of X-ray energy, the CT values and SD values of reconstructed images in monochromatic groups gradually decreased, and the SNRs and CNRs gradually increased, and the differences between adjacent two groups were all statistically significant (P<0.001). In the range of 80-140 keV, the SD values of different monochromatic groups were lower than that of the polychromatic group, and the SNRs and CNRs were higher than those of the polychromatic group, and the pairwise comparison results showed statistically significant differences (P<0.001). The 120 keV-reconstructed image had the highest subjective score, and the difference from that of the polychromatic image was statistically significant (P<0.05).ConclusionsDifferent monochromatic reconstruction of spectral CT can significantly reduce the image noise in early COVID-19 lesions, and improve the image quality. Combining subjective and objective evaluation of images, the 120 keV-reconstructed monochromatic image shows the best early lesions of COVID-19 and is of great significance for early clinical screening.
The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.