ObjectiveTo conduct a meta-analysis comparing the accuracy of artificial intelligence (AI)-assisted diagnostic systems based on 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) and structural MRI (sMRI) in the diagnosis of Alzheimer's disease (AD). MethodsOriginal studies dedicated to the development or validation of AI-assisted diagnostic systems based on 18F-FDG PET/CT or sMRI for AD diagnosis were retrieved from the Web of Science, PubMed, and Embase databases. Studies meeting the inclusion criteria were collected, and the risk of bias and clinical applicability of the included studies were assessed using the PROBAST checklist. The pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated using a bivariate random-effects model. ResultsTwenty-six studies met the inclusion criteria, yielding a total of 38 2×2 contingency tables related to diagnostic performance. Specifically, 24 contingency tables were based on 18F-FDG PET/CT to distinguish AD patients from normal cognitive (NC) controls, and 14 contingency tables were based on sMRI for the same purpose. The meta-analysis results showed that for 18F-FDG PET/CT, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 89% (95%CI 88% to 91%), 93% (95%CI 91% to 94%), and 0.96 (95%CI 0.93 to 0.97), respectively. For sMRI, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 88% (95%CI 85% to 90%), 90% (95%CI 87% to 92%), and 0.94 (95%CI 0.92 to 0.96), respectively. ConclusionAI-assisted diagnostic systems based on either 18F-FDG PET/CT or sMRI demonstrated similar performance in the diagnosis of AD, with both showing high accuracy.
Objective To investigate the characteristic of the expression of facilitative glucose transporter 1 （Glut1） in bronchial aveolar carcinoma （BAC） and the relationship between expression of Glutl and fluorine-18 fluorodeoxyglucose （18F-FDG） uptake. Methods Twenty patients with BAC were imaged with 18F-FDG positron emission tomography （PET） before surgery. Maximum standard uptake value （SUVmax） and mean standard uptake value （SUVmean ） of tumor and standard uptake value of normal lung （SUVIung） were measured. The expression of Glutl was studied in paraffin sections by streptavidin peroxidase （SP） immunohistochemistry. Results All tumors of the patients could be detected by ^18F-FDG-PET. 18F-FDG uptake of tumor was higher than that of normal lung （SUV SUV and SUVlong were 3.09± 1.35, 2.37±1.03 and 0.39±0.09 respectively）. The expression of Glutl were positive in all tumors （73. 8%± 14. 8% of positive cell rate, 2. 8 ± 0. 9 grade of staining intensity）. Predominantly cytoplasm positive with weak staining intensity were observed in many tumors. Glutl negtive were observed in normal bronchial and lung parenchyma. Positive correlations were found among 18F-FDG uptake, Glut1 expression and tumor size （P〈0. 01）. Conclusion Glutl expression with peculiarity in BAC is correlate with 18F-FDG uptake.
Prostate cancer is the most common malignant tumor in male urinary system, and the morbidity and mortality rate are increasing year by year. Traditional imaging examinations have some limitations in the diagnosis of prostate cancer, and the advent of molecular imaging probes and imaging technology have provided new ideas for the integration of diagnosis and treatment of prostate cancer. In recent years, prostate-specific membrane antigen (PSMA) has attracted much attention as a target for imaging and treatment of prostate cancer. PSMA ligand positron emission tomography (PET) has important reference value in the diagnosis, initial staging, detection of biochemical recurrence and metastasis, clinical decision-making guidance and efficacy evaluation of prostate cancer. This article briefly reviews the clinical research and application progress on PSMA ligand PET imaging in prostate cancer in recent years, so as to raise the efficiency of clinical applications.
Epilepsy is a clinical syndrome characterized by recurrent epileptic seizures caused by various etiologies. Etiological diagnosis and localization of the epileptogenic focus are of great importance in the treatment of epilepsy. Positron emission tomography-computed tomography (PET-CT) technology plays a significant role in the etiological diagnosis and localization of the epileptogenic focus in epilepsy. It also guides the treatment of epilepsy, predicts the prognosis, and helps physicians intervene earlier and improve the quality of life of patients. With the continuous development of PET-CT technology, more hope and better treatment options will be provided for epilepsy patients. This article will review the guiding role of PET-CT technology in the diagnosis and treatment of epilepsy, providing insights into its application in etiological diagnosis, preoperative assessment of the condition, selection of treatment plans, and prognosis of epilepsy.
ObjectiveTo systematically review the diagnostic value of FDG-PET, Aβ-PET and tau-PET for Alzheimer ’s disease (AD).MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect diagnostic tests of FDG-PET, Aβ-PET and tau-PET for AD from January 2000 to February 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by Meta-Disc 1.4 and Stata 14.0 software.ResultsA total of 31 studies involving 3 718 subjects were included. The results of meta-analysis showed that, using normal population as control, the sensitivity/specificity of FDG-PET and Aβ-PET in diagnosing AD were 0.853/0.734 and 0.824/0.771, respectively. Only 2 studies were included for tau-PET and meta-analysis was not performed.ConclusionsFDG-PET and Aβ-PET can provide good diagnostic accuracy for AD, and their diagnostic efficacy is similar. Due to limited quality and quantity of the included studies, more high quality studies are required to verify the above conclusions.
The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography (18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.
Prostate cancer ranks second among the causes of death of malignant tumors in middle-aged and elderly men. A considerable number of patients are not easily detected in early-stage prostate cancer. Although traditional imaging examinations are of high value in the diagnosis and staging of prostate cancer, they also have certain limitations. With the development of nuclear medicine instruments and molecular probes, molecular imaging is playing an increasingly important role in the diagnosis and treatment of prostate cancer. Positron emission tomography and computed tomography (PET/CT) using prostate-specific membrane antigen (PSMA) as a probe has gained increasing recognition. This article will review the latest progress in the application of PET/CT using probes for targeting PSMA to imaging and treatment of prostate cancer, in order to provide a theoretical basis for the application of probes for targeting PSMA in the diagnosis and treatment of prostate cancer.
The PET/CT imaging technology combining positron emission tomography (PET) and computed tomography (CT) is the most advanced imaging examination method currently, and is mainly used for tumor screening, differential diagnosis of benign and malignant tumors, staging and grading. This paper proposes a method for breast cancer lesion segmentation based on PET/CT bimodal images, and designs a dual-path U-Net framework, which mainly includes three modules: encoder module, feature fusion module and decoder module. Among them, the encoder module uses traditional convolution for feature extraction of single mode image; The feature fusion module adopts collaborative learning feature fusion technology and uses Transformer to extract the global features of the fusion image; The decoder module mainly uses multi-layer perceptron to achieve lesion segmentation. This experiment uses actual clinical PET/CT data to evaluate the effectiveness of the algorithm. The experimental results show that the accuracy, recall and accuracy of breast cancer lesion segmentation are 95.67%, 97.58% and 96.16%, respectively, which are better than the baseline algorithm. Therefore, it proves the rationality of the single and bimodal feature extraction method combining convolution and Transformer in the experimental design of this article, and provides reference for feature extraction methods for tasks such as multimodal medical image segmentation or classification.
There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model’s perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.
ObjectiveTo analyze the influencing factors for image quality of 18F-deoxyglucose (FDG) positron emission tomography (PET)/CT systemic tumor imaging and explore the method of control in order to improve the PET/CT image quality. MethodsRetrospective analysis of image data from March to June 2011 collected from 1 000 18F-FDG whole body tumor imaging patients was carried out. We separated standard films from non-standard films according to PET/CT image quality criteria. Related factors for non-standard films were analyzed to explore the entire process quality control. ResultsThere were 158 cases of standard films (15.80%), and 842 of non-standard films (84.20%). Artifact was a major factor for non-standard films (93.00%, 783/842) followed by patients’ injection information recording error (2.49%, 21/842), the instrument factor (1.90%, 16/842), incomplete scanning (0.95%, 8/842), muscle and soft tissue uptake (0.83%, 7/842), radionuclide contamination (0.59%, 5/842), and drug injection (0.24%, 2/842). The waste film rate was 5.80% (58/1 000), and the redoing rate was 2.20% (22/1 000). ConclusionComplex and diverse factors affect PET/CT image quality throughout the entire process, but most of them can be controlled if doctors, nurses and technicians coordinate and cooperate with each other. The rigorous routine quality control of equipment and maintenance, patients’ full preparation, appropriate position and scan field, proper parameter settings, and post-processing technology are important factors affecting the image quality.