OBJECTIVE: To determine the accuracy of pre-contrast abdominal MR imaging for lesion detection and characterization in pediatric oncology patients.
MATERIALS AND METHODS: We included 120 children (37 boys and 83 girls; mean age 8.94 years) referred by oncology services. Twenty-five had MRI for the first time and 95 were follow-up scans. Two authors independently reviewed pre-contrast MR images to note the following information about the lesions: location, number, solid vs. cystic and likely nature. Pre- and post-contrast imaging reviewed together served as the reference standard.
RESULTS: The overall sensitivity was 88% for the first reader and 90% for the second; specificity was 94% and 91%; positive predictive value was 96% and 94%; negative predictive value was 82% and 84%; accuracy of pre-contrast imaging for lesion detection as compared to the reference standard was 90% for both readers. The difference between mean number of lesions detected on pre-contrast imaging and reference standard was not significant for either reader (reader 1, P = 0.072; reader 2, P = 0.071). There was substantial agreement (kappa values of 0.76 and 0.72 for readers 1 and 2) between pre-contrast imaging and reference standard for determining solid vs. cystic lesion and likely nature of the lesion. The addition of post-contrast imaging increased confidence of both readers significantly (P
METHODS: A systematic search was performed in the PubMed, Scopus, and Web of Science (WoS) databases in June 2022. Patients with head and neck cancer treated with radiotherapy and periodic rs-fMRI assessments were included. A meta-analysis was performed to determine the potential of rs-fMRI for detecting brain changes.
RESULTS: Ten studies with a total of 513 subjects (head and neck cancer patients, n = 437; healthy controls, n = 76) were included. A significance of rs-fMRI for detecting brain changes in the temporal and frontal lobes, cingulate cortex, and cuneus was demonstrated in most studies. These changes were reported to be associated with dose (6/10 studies) and latency (4/10 studies). A strong effect size (r = 0.71, p
METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.