Meta AnalysisID 4027
光活检中使用原卟啉IX联合基于机器学习的脑组织表征对胶质母细胞瘤切除范围的应用：一项系统评价
CRD42022324593
Population: Patient with Glioma with a focus on glioblastoma Intervention: Receiving Ppix before surgery and being monitoring during surgery with intraoperative methods to detect it and post op machine learning analysis
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Record Fields
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- Meta Analysis Id
- 4027
- Evidence Id
- 12585
- Core Evidence Id
- 12585
- Source Meta Analysis Id
- 3985
- Herb2 Meta Analysis Id
- HBMA003985
- Crd Id
- CRD42022324593
- Title
- A systematic review of the use of Protoporphyrin IX during optical biopsies with machine learning based brain tissue characterisation for extent of glioblastoma resection
- Review Question
- Population: Patient with Glioma with a focus on glioblastoma Intervention: Receiving Ppix before surgery and being monitoring during surgery with intraoperative methods to detect it and post op machine learning analysis of their ability to characterise extent of tumour resection Comparison: Conventional Methods of detecting tumour resection margins using other imaging like MRI Outcome: Sensitivity, Specificity, NPV, PPV, Area under the curve or Accuracy Study Type: Primary original research
- Study Type Included
- All studies randomised control trials, preclinical and cohort studies. As this is such an early field we anticipate there to be very little available.
- Condition Being Studied
- High grade gliomas focusing on glioblastoma multiforme undergoing surgical resection
- Participant
- Inclusion Criteria: All patients with intrinsic Brain Gliomas undergoing intraoperative resection using fluorophores such as 5-ALA-guided imaging modalities characterised by Machine learning to differentiate tumour margins from normal tissue. Inclusively for optical biopsies Exclusion Criteria: All other tumours and studies not addressing machine learning in fluorophore-guided brain tumour excision. In-vitro studies without any in-vivo component.
- Animal
- Human Disease Modelled
- Intervention
- Intervention Use of machine learning to classify the differences between high grade gliomas on multimodality imaging using dyes such as protoporphyrin IX
- Comparator Control
- Conventional techniques for tumour excision and margin identification not involving Fluorophores such as 5-ALA
- Main Outcome
- Sensitivity, Specificity, Negative Predictive, Positive Predictive Value, Accuracy of 5-ALA/protoporphyrin IX derivatives and machine learning models for intraoperative excision of Glioblastoma.
- Outcome Measure
- Additional Outcome
- Not Applicable
- Study Method
- Meta-analysis, Systematic review
- Keyword
- Biopsy; Brain; Glioblastoma; Humans; Machine Learning; protoporphyrin IX
- Contact
- Joseph Davids [email protected]
- Organisational Affiliation
- Imperial College London
- Funding Source
- Imperial Biomedical Research Centre
- Other Selection Criteria
- Final Publication
- Same Topic Review
- Published Protocol
- Review Type
- Language
- English
- Country
- England
- Review Stage
- Review Ongoing
- First Submission Date
- 2022-04-24
- Registration Date
- 2022-04-28
- Anticipated Start Date
- 2021-08-04
- Anticipated Completion Date
- 2022-07-01
- Title Cn
- 光活检中使用原卟啉IX联合基于机器学习的脑组织表征对胶质母细胞瘤切除范围的应用：一项系统评价
- Title En
- A systematic review of the use of Protoporphyrin IX during optical biopsies with machine learning based brain tissue characterisation for extent of glioblastoma resection
- Bilingual Status
- complete