A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). METHODS: We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. RESULTS: The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31-0.44, P = 0.005-0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). CONCLUSION: The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. TRIAL REGISTRATION: NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581.

[A retrospective survey of diagnostic techniques of pulmonary embolism in 13 general hospitals in Guangxi area].

OBJECTIVE: To survey different diagnostic techniques in diagnosing pulmonary embolism (PE). METHODS: Hospital records of PE cases in 13 AAA general hospitals in Guangxi area from 1995 to 2007 were studied retrospectively. Probable PE was defined as the diagnosis based on the clinical data and non-specific imaging, while the definite PE was defined as those with the diagnosis confirmed by specific imaging or autopsy. The percentage of various diagnostic methods of PE was analyzed. RESULTS: From 1995 to 2007, 237 definite PE and 223 probable PE were found in 13 hospitals, and they accounted for 51.52% and 48.48%, respectively, for all patients diagnosed as having PE. The percentage of definite PE cases during 1995-2001 and 2002-2007 were 14.63% and 55.13%, respectively (chi (2)=24.522, P<0.01). Among 237 definite PE, 2 positive diagnostic techniques were employed in 17 patients. Twenty-seven (11.39%), 214 (90.30%), 6 (2.53%), 5 (2.11%) and 2 (0.84%) patients were diagnosed by pulmonary angiography, CT pulmonary angiography (CTPA), ultrasonography, magnetic resonance imaging (MRI) and autopsy, respectively. No ventilation-perfusion lung scanning was performed in these patients. Compared with other diagnostic imaging, the percentage of CTPA in diagnosis of PE increased slightly since 2003. CONCLUSION: CTPA is the first choice in the diagnosis of PE in Guangxi area, and more attention should be paid to other diagnostic imaging techniques.