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.
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.
Objective To introduce the current study on 18F-fluorodexyglucose positron emission tomography (18F-FDG PET) scanning in diagnosis and treatment of carcinoma of large intestine. Methods The literatures about 18F-FDG PET scanning in diagnosis and treatment of carcinoma of large intestine in recent years were reviewed. Results 18F-FDG PET scanning is superior to CT and MRI in identificating carcinoma of large intestine recurrence, metastasis in the early stage after operation and staging carcinoma of large intestine. Conclusion 18F-FDG PET scanning may be one of the accessory examinations in carcinoma of large intestine and may be helpful for the choice of treatment.
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.
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.
Solitary pulmonary nodule (SPN) is defined as a rounded opacity≤3 cm in diameter surrounded by lung parenchyma. The majority of smokers who undergo thin-section CT have SPNs, most of which are smaller than 7 mm. In the past, multiple follow-up examinations over a two-year period, including CT follow-up at 3, 6, 12, 18, and 24 months, were recommended when such nodules are detected incidentally. This policy increases radiation burden for the affected population. Nodule features such as shape, edge characteristics, cavitation, and location have not yet been found to be accurate for distinguishing benign from malignant nodules. When SPN is considered to be indeterminate in the initial exam, the risk factor of the patients should be evaluated, which includes patients' age and smoking history. The 2005 Fleischner Society guideline stated that at least 99% of all nodules 4 mm or smaller are benign; when nodule is 5-9 mm in diameter, the best strategy is surveillance. The timing of these control examinations varies according to the nodule size (4-6, or 6-8 mm) and the type of patients, specifically at low or high risk of malignancy concerned. Noncalcified nodules larger than 8 mm diameter bear a substantial risk of malignancy, additional options such as contrast material-enhanced CT, positron emission tomography (PET), percutaneous needle biopsy, and thoracoscopic resection or videoassisted thoracoscopic resection should be considered.
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.
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.
With the in-depth study of the pathophysiological changes of ischemic myocardium, some experimental studies have found that in many patients with coronary heart disease, severe lesions and long duration of diseases which do not recover due to revascularization, are mainly related to the viable myocardium in the affected area. Survival myocardium refers to those perfusion defect-induced dysfunctional but not dead cardiomyocytes, including stunned myocardium and hibernating myocardium. Therefore, accurate assessment of viable myocardium is important for the development of clinical treatment options and prognosis assessment of such patients. Positron emission tomography/computed tomography (PET/CT) can evaluate the survival of hibernating myocardium and quantitatively analyze it. Thus PET/CT can provide useful information for the possibility of myocardial function recovery after revascularization, treatment options and prognosis evaluation.
Objective To investigate the advances and clinical efficacy evaluation method on neoadjuvant chemotherapy in patients with gastric cancer. Methods Literatures on the advances and clinical efficacy evaluation method on neoadjuvant chemotherapy in patients with gastric cancer were reviewed and analyzed. The agreement between computed tomography (CT), endoscopic ultrasound (EUS), magnetic resonance imaging (MRI) and positron emission tomography (PET) and the results of histopathology and survival was analyzed.Results CT and EUS were the method of efficacy evaluation commonly used at present, but the evaluation indexes and criteria were controversial, and the criteria for solid tumors seemed to be not feasible for gastric cancer. Diffusionweighted imaging (DWI) method needed more investigation, while PET held advantage in early selection of patients without response accurately.Conclusion There is no uniform standard for clinical efficacy evaluation yet, so an integration of diverse imaging methods may be the best choice to improve the accuracy of neoadjuvant chemotherapy in patients with gastric cancer.