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The liver reserve function refers to the compensatory ability to maintain liver function after damage, providing implication for the resection of hepatic malignant tumor. Hepatobiliary scintigraphy imaging can provide quantitative evaluation of liver blood perfusion, and has advantages on the evaluation of liver reserve function and the prediction of postoperative complications. 99Tc m-galactosyl serum albumin (GSA) and 99Tc m-mebrofenin are commonly used imaging agents for hepatobiliary scintigraphy imaging assessment of liver reserve function. This article reviews the application and progress of hepatobiliary scintigraphy in liver reserve function assessment.
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Objective:To investigate the relationship between the expression levels of chemokines in serum of patients with differentiated thyroid carcinoma (DTC) and the progression of DTC.Methods:From January to April in 2017, blood samples of 76 patients (25 males, 51 females, median age: 39 years) with DTC after surgery in Nuclear Medicine Department of Tenth People′s Hospital Affiliated to Tongji University were collected retrospectively for detecting the expression levels of 40 chemokines. Patients were divided into different groups according to (1) with or without metastasis: the non-metastasis group ( n=13) and the metastasis group ( n=63); (2) degree of gradual dedifferentiation: without metastasis group ( n=13), lymph node metastasis group ( n=48), highly malignant group ( n=11) and radioactive iodine refractory (RAIR) with distant metastasis group ( n=4); (3) frequency of 131I treatment in follow-up for nearly 2 years: single treatment group ( n=51) and multiple treatment group ( n=25). Differences in chemokine levels among groups were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of differential chemokines′ levels on DTC metastasis and multiple 131I treatment. Independent-sample t test, Mann-Whitney U test and one-way analysis of variance were used to analyze the data. Results:Compared with the non-metastatic group, the expression levels of Eotaxin-3 ((25.94±6.05) vs (21.76±5.71) ng/L), interferon-γ (IFN-γ; (116.04±28.98) vs (98.71±26.18) ng/L), macrophage-derived chemokine (MDC; (1 468.08±401.74) vs (1 082.94±423.30) ng/L) and thymus expressd chemokine (TECK; (505.22(419.80, 563.36) vs 402.89(347.43, 442.97) ng/L) in metastatic group were decreased, and the differences were statistically significant ( t values: 2.376, 2.131, 3.007, U=215.000, all P<0.05). The area under the ROC curve of IFN-γ+ MDC+ TECK for predicting DTC metastasis was 0.844(95% CI: 0.755-0.932, P<0.001), and the sensitivity was 79.37%(50/63). Only the differences of MDC among without metastasis group, lymph node metastasis group, highly malignant group and RAIR with distant metastasis group were significant ((1 468.08±401.74), (1 121.59±454.20), (976.07±281.04), (922.68±342.41) ng/L; F=3.564, P<0.05), and the expression was gradually decreased with the degree of dedifferentiation. Only IL-8 was significantly increased in the multiple treatment group compared with the single treatment group (28.20(23.22, 32.51) vs 30.51(26.98, 35.57) ng/L; U=801.000, P<0.05). The area under the ROC curve of IL-8 for predicting multiple 131I treatment was 0.648(95% CI: 0.523-0.773, P<0.05), and the sensitivity was 100%(25/25). Conclusions:Decreased expression of IFN-γ, MDC and TECK may be potential markers for predicting metastasis in DTC. MDC is likely to be a potential molecular target for detecting the dedifferentiation degree of DTC, decreased expression of which may indicate the increased malignancy of tumor. IL-8 may be used to predict whether patients need multiple 131I treatments.
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Objective@#To analyze the expression and clinical significance of solute carrier family 5 member 5 (SLC5A5), the coding gene of sodium/iodide symporter (NIS), in thyroid carcinoma.@*Methods@#The messenger RNA (mRNA) expression of SLC5A5 in thyroid carcinoma and normal thyroid tissues from The Cancer Genome Atlas (TCGA) was compared using independent-sample t test and results were shown in one scatter plot. The relation between clinical features of thyroid carcinoma and the changes of SLC5A5 mRNA was analyzed on LinkedOmics using Kruskal-Wallis test or Wilcoxon test.@*Results@#Data from TCGA showed that the SLC5A5 mRNA expression in thyroid carcinoma (1.419±0.049) was significantly reduced compared with that in normal thyroid tissues (3.301±0.087; t=12.66, P<0.01). The expression of SLC5A5 mRNA in thyroid carcinoma is affected by ethnicity (χ2=0.300, P<0.05). Moreover, the expression of SLC5A5 mRNA were decreased with the increase of pathologic grading (Ⅰ, Ⅱ, Ⅲ, Ⅳ) and T, N, M stages (χ2 values: 0.114, 0.215, z values: -0.345, -0.102, all P<0.05).@*Conclusions@#The expression level of SLC5A5 mRNA is associated with clinical characteristic of thyroid carcinoma. SLC5A5 mRNA has the potential to become one candidate biomarker to assess disease and predict the development of thyroid carcinoma.
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Objective To develop a diagnostic model based on deep neural network for intelligent discrimination of thyroid function. Methods A total of 1616 patients ( 283 males, 1333 females, average age:52 years) who underwent thyroid imaging between May 2016 and June 2018 were selected. According to the clinical diagnosis, the 1616 cases included 299 normal thyroid cases, 876 hyperthyroidism cases and 441 hypothyroidism cases. Feature extraction and learning training were performed on 1000 training set sam-ples by two deep neural network models ( AlexNet;deep convolution generative adversarial networks ( DCGAN) ) using deep learning algorithm. Performance verifications were implemented on 616 test set samples. The con-sistency between the verification results of the two models and the clinical diagnosis was analyzed by Kappa test. Meanwhile, the time advantage of the intelligent diagnosis models was analyzed. Results The average diagnostic time of AlexNet model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 82.29％(79/96), 94.62％(369/390), 100％(130/130), respectively. The Kappa value between results of AlexNet model and clinical diagnosis was 0.886 ( P<0.05) . The average di-agnostic time of DCGAN model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 85.42％(82/96), 95.64％(373/390), 99.23％(129/130), respectively. The Kappa value between results of DCGAN model and clinical diagnosis was 0.904 ( P<0.05) . Conclusion The deep neural network intelligent diagnosis model can quickly determine the functional status of thyroid gland in thyroid imaging, and it has a high recognition accuracy, thus providing a new method for thyroid image review.