ArticleContrast media & molecular imaging2022
Artificial Intelligence Algorithm-Based Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) in the Treatment of Glioma Biopsy.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Pooled it
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.International journal of molecular sciences · 2025Review
- T2-FLAIR imaging-based radiomic features for predicting early postoperative recurrence of grade II gliomas.Future oncology (London, England) · 2024Article
- Assessment of the emerging role of AI in diagnosing gliomas using MRI: Systematic review and meta-analysis.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was aimed at exploring the application value of positron emission tomography (PET) + magnetic resonance imaging (MRI) technology based on convolutional neural network (CNN) in the biopsy and treatment of intracranial glioma. 35 patients with preoperatively suspicious gliomas were selected as the research objects. Their imaging images were processed using CNN. They were performed with the preoperative head MRI, fluorodeoxyglucose (FDG) PET, and ethylcholine (FECH) PET scans to construct the cancer tissue contours. In addition, the performance of CNN was evaluated, and the postoperative pathology of patients was analyzed. The results suggested that the CNN-based PET + MRI technology showed a recognition accuracy of 97% for images. Semiquantitative analysis was adopted to analyze the standard uptake value (SUV). It was found that the SUV
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Registered trials
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