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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 risk factors for combined coronary microvascular dysfunction (CMD) in patients with ischemia and non-obstructive coronary artery disease (INOCA).Methods:From October 2020 to May 2022, 100 INOCA patients with myocardial ischemic symptoms who underwent coronary angiography (CAG) suggestive of <50% stenosis in all three coronary arteries at the Tenth People′s Hospital of Tongji University were prospectively recruited. Myocardial perfusion imaging (MPI), transthoracic echocardiography and cadmium-zinc-telluride (CZT) SPECT coronary flow quantification were performed in the same month, and 93 INOCA patients (36 males and 57 females, age (63.0±10.9) years) were finally included. CMD was defined as coronary flow reserve (CFR)<2.5. Independent-sample t test, Mann-Whitney U test and χ2 test were used to compare MPI results and left ventricular volume parameters between CMD and non-CMD groups. ROC curve analysis was used to analyze the efficacy of each index in predicting CMD, and independent risk factors for CMD were screened by multivariate logistic regression analysis. Results:Among 93 INOCA patients, 29 were in the CMD group and 64 were in the non-CMD group. The age, proportion of hypertension, left ventricular mass index (LVMI), summed stress score (SSS), summed difference score (SDS), left ventricular internal diameter systolic (LVIDS), interventricular septum thickness (IVST), and left ventricular posterior wall thickness (LVPWT) in the CMD group were higher than those in the non-CMD group ( t values: 2.42-3.76, χ2=8.94, z values: -3.31, -3.41, all P<0.05). ROC curve analysis showed that LVMI, SSS, SDS, LVPWT, IVST and age were significant in predicting CMD (AUCs: 0.67-0.72). Multivariate logistic regression analysis showed that LVMI (odds ratio ( OR)=1.08, 95% CI: 1.01-1.17), SDS ( OR=5.37, 95% CI: 1.95-14.78), hypertension ( OR=5.68, 95% CI: 1.34-24.18) and age ( OR=1.10, 95% CI: 1.03-1.18) were risk factors for CMD. Conclusion:LVMI, SDS, hypertension and age are strongly associated with combined CMD in INOCA patients, which can be used for early risk stratification of INOCA patients.
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Objective:To develop an approach for the automatic diagnosis of bone metastasis and to design a parameter of quantitative evaluation for tumor burden on bone scans based on deep learning technology.Methods:A total of 621 cases (389 males, 232 females, age: 12-93 years) of bone scan images from the Department of Nuclear Medicine in Tenth People′s Hospital of Tongji University from March 2018 to July 2019 were retrospectively analyzed. Images were divided into bone metastasis group and non-bone metastasis group. Eighty percent of the cases were randomly extracted from both groups as the training set, and the rest of cases were used as the test set. A deep residual convolutional neural network ResNet34 was used to construct the classification model and the segmentation model. The sensitivity, specificity and accuracy were calculated and the performance differences of the classification model in different age groups (15 cases of <50 years, 75 cases of ≥50 and <70 years, 33 cases of ≥70 years) were analyzed. The regions of metastatic bone lesions were automatically segmented by the segmentation model. The Dice coefficient was used to evaluate the effect of the segmentation model and the manual labeled results. Finally, the bone scans tumor burden index (BSTBI) was calculated to assess the tumor burden of bone metastases.Results:There were 280 cases with bone metastases and 341 cases with non-bone metastases, including 498 in training set and 123 in test set. The classification model could accurately identify bone metastases, with the sensitivity, specificity and accuracy of 92.59%(50/54), 85.51%(59/69) and 88.62%(109/123), respectively, and it performed best in the <50 years group (sensitivity, 2/2; specificity, 12/13; accuracy, 14/15). The specificity in the ≥70 years group (8/12) was the lowest. The Dice coefficient of bone metastatic area and bladder area were 0.739 and 0.925 in the segmentation model, which performed similarly in the three age groups. Preliminary results showed that the value of BSTBI increased with the increase of the number of bone metastatic lesions and the degree of 99Tc m-MDP uptake. The machine learning model in this study took (0.48±0.07) s for the entire analysis process from input to the final BSTBI calculation. Conclusions:The deep learning based on automatic diagnosis framework for bone metastases can automatically and accurately identify segment bone metastases and calculate tumor burden. It provides a new way for the interpretation of bone scans. The proposed BSTBI may be used as a quantitative evaluation indicator in the future to assess the tumor burden of bone metastases based on bone scans.
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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.
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Objective:To study the effects of ~(125)I-(α_v)ASODN on the in vitro invasive ability of heptocellular carcino-ma cell line(HepG2) through PEI-RGD-mediated receptor process. Methods: Intergrin α_v-specific antisense oligonucle-otide was labeled with ~(125)I, and PEI-RGD/~(125)I-(α_v)ASODN complex was prepared by combining ~(125)I-(α_v)ASODN with polyethyleneimine derivative PEI-RGD. PEI-RGD/~(125)I-(α_v)ASODN complex was transferred into HepG2 cells through the receptor-mediated process. The effect of PEI-RGD/~(125)I-(α_v)ASODN complex on the invasive ability of HepG2 cells was examined by Boyden chamber invasive assay. Results: (1) The labeling yield and radiochemical purity of ~(125)I-(α_v) ASODN were(73.78±4.09)% and(96.68±1.38)%, respectively, and the labeled compound had a good stability in vitro after 48 h at 37℃; (2) The ability of HepG2 cells to uptake PEI-RGD/~(125)I-(α_v)ASODN reached its peek ([12.77±0.85] % ) when PEI-RGD/~(125)I-(α_v)ASODN was at 4 μl/2 μg ([12.77±0.85] %), and then gredually decreased thereafter. So the dosage of PEI-RGD/~(125)I-(α_v)ASODN for the following experiment was chosen as 2 μl/1 μg; (3) The invasive capacity of HepG2 cells was significantly reduced in PEI-RGD/~(125)I-(α_v)ASODN group compared with those in other experiment and control groups (P <0.01 ). Conclusion: ~(125)I-(α_v)ASODN mediated by PEI-RGD can effectively inhibit the invasive capacity of HepG2 cells.