A5068406739- Radiomics and Machine Learning in Medical Imaging
- Glioma Diagnosis and Treatment
- Medical Imaging Techniques and Applications
- Circular RNAs in diseases
- Sarcoma Diagnosis and Treatment
- Advanced Neural Network Applications
- Machine Learning in Materials Science
- Pelvic and Acetabular Injuries
- MRI in cancer diagnosis
- Pelvic floor disorders treatments
- Molecular Biology Techniques and Applications
- Anorectal Disease Treatments and Outcomes
Guangdong Provincial People's Hospital
2023-2024
Southern Medical University
2023-2024
Guangdong Academy of Medical Sciences
2023-2024
The Seventh Affiliated Hospital of Sun Yat-sen University
2020
Sun Yat-sen University
2020
Studies have shown that magnetic resonance imaging (MRI)-based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status patients with glioblastoma (GBM) remains unclear.To evaluate value of (DL) multiparametric MRI-based identifying TERT mutations GBM preoperatively.Retrospective.A total 274 isocitrate dehydrogenase-wildtype were included study. The training and external validation...
Purpose: To evaluate the value of intra- and peritumoural deep learning (DL) features based on multi-parametric magnetic resonance imaging (MRI) for identifying telomerase reverse transcriptase (TERT) promoter mutation in glioblastoma (GBM). Methods: In this study, we included 229 patients with GBM who underwent preoperative MRI two hospitals between November 2016 September 2022. We used four 2D Convolutional Neural Networks (GoogLeNet, DenseNet121, VGG16, MobileNetV3-Large) to extract DL...
Background Studies have shown that deep‐learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic‐MRI and DLR features can more accurately distinguish GBM SBM remains uncertain. Purpose To construct validate a nomogram (DDLRN) signatures to preoperatively. Study Type Retrospective. Population Two hundred thirty‐five patients with (N = 115) or 120), randomly divided into training cohort (90 98 SBM) validation...
Abstract: The 2021 World Health Organization (WHO) Classification of Tumors the Central Nervous System has brought a transformative shift in categorization adult gliomas. Departing from traditional histological subtypes, new classification system is guided by molecular genotypes, particularly Isocitrate Dehydrogenase (IDH) mutation. This alteration reflects pivotal change understanding tumor behavior, emphasizing importance profiles over morphological characteristics. Gliomas are now...
Radiomics uses computer software to mine massive quantitative image features from medical imaging images and then screens the most valuable radiomics using statistical and/or machine learning methods. Furthermore, it is used parse clinical information for disease characterisation, tumour grading staging, efficacy evaluation, prognosis prediction. In our study, we demonstrated that multiparametric MRI-based fusion model an effective preoperative non-invasive method predict telomerase reverse...