GliomaAI-Oligo: Non-Invasive MRI-Based Detection of IDH Mutant Oligodendroglioma Using Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 1,372
- 试验地点
- 1
- 主要终点
- Diagnostic performance of GliomaAI-Oligo for identification of IDH mutant Oligodendroglioma from MRI, measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC
研究概览
简要总结
The goal of this observational study is to learn whether an artificial intelligence system called GliomaAI-Oligo can help detect a specific molecular type of brain tumour called Oligodendroglioma using routine MRI scans. The study uses previously collected and fully anonymised MRI data from 1,372 patients from 13 institutions in the Cancer Imaging Archive (TCIA).
The main questions it aims to answer are:
- How accurately can GliomaAI-Oligo identify Oligodendroglioma from MRI scans?
- How well does the system perform across data from different hospitals and patient groups?
Researchers will use existing MRI scans and clinical information to train and test the AI system. No new scans, treatments, or hospital visits are required for participants, and all data used is fully anonymised and obtained from an existing research database.
Participants will not be asked to do anything, as this study only uses previously collected imaging data.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adult (>=18 years of age)
- •Having pre op MRI scan
- •Having biopsy / surgery
- •Having post biopsy/ surgery histology diagnosis and genetic analysis.
排除标准
- •MRI scan significantly degraded by motion or other artefact
- •Incomplete genetic analysis
- •Prior treatment (e.g., radiotherapy or chemotherapy) before baseline MRI
结局指标
主要结局
Diagnostic performance of GliomaAI-Oligo for identification of IDH mutant Oligodendroglioma from MRI, measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC
时间窗: Perioperative
The diagnostic performance of the GliomaAI-Oligo artificial intelligence model will be assessed by comparing pre-operative MRI-based predictions of IDH mutant Oligodendroglioma status against post-operative (biopsy or surgery) molecular/genetic profiling results as the reference standard. Performance metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC will be calculated.
次要结局
未报告次要终点