Clinical trial · Observational
MRI-based Computer Aided Diagnosis Software (V1) for Glioma
The Clinical Trial 01 to Evaluate the Effectiveness of MRI-based Computer Aided Diagnosis Software (V1) for Glioma Segmentation, Gene Prediction and Tumor Grading
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this multi-center clinical trial is to evaluate the effectiveness of MRI-based computer-aided diagnosis software (V1) for glioma segmentation, gene prediction, and tumor grading. Machine learning methods such as high-precision tumor segmentation and classification and discrimination modeling can further optimize the non-invasive molecular diagnosis and prognosis prediction. The main question it aims to answer is whether the software can predict the molecular type and the prognosis quickly and correctly. The results will be compared with the real-world clinical data double-blindly. Finally, form a set of user-friendly automatic glioma diagnosis and treatment systems for clinics.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Glioma | Glioma | ONTOLOGY_EXACT | 0.98 |
| Primary Brain Tumor | Primary Brain Tumor | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Accuracy rate
- timeFrame
- end of the study (one year after the surgery of the last participants).
- description
- describing the number of correct cases predicted by the software as a proportion of the total participants. The accuracy rate has a value between 0 and 1, with higher values indicating a more reliable tool.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 70 Years
Show eligibility criteria text
Inclusion Criteria: 1. Age front 18 to 70 years old (not including threshold), gender is not limited; 2. Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment; 3. Preoperative cranial MRI (T1, T2, T2 Flair, T1 enhanced GE company magnetic resonance package), tumor pathological examination (H\&E section, Kuoran Gene Company package), acceptable follow-up and brain MRI scan; 4. The patient himself voluntarily participated and signed the informed consent in writing. Exclusion Criteria: 1. Patients who only underwent biopsy rather than surgical tumor resection; 2. Postoperative pathologically confirmed non-glioma patients; 3. Patients with multiple glioma metastases or multiple gliomas; 4. Patients who died of complications in the early postoperative period; 5. The researcher believes that this researcher should not be included.
References
Publications (4)
- BACKGROUNDGarcia CR, Slone SA, Dolecek TA, Huang B, Neltner JH, Villano JL. Primary central nervous system tumor treatment and survival in the United States, 2004-2015. J Neurooncol. 2019 Aug;144(1):179-191. doi: 10.1007/s11060-019-03218-8. Epub 2019 Jun 28. PMID 31254264
- BACKGROUNDReardon DA, Wen PY. Glioma in 2014: unravelling tumour heterogeneity-implications for therapy. Nat Rev Clin Oncol. 2015 Feb;12(2):69-70. doi: 10.1038/nrclinonc.2014.223. Epub 2015 Jan 6. PMID 25560529
- BACKGROUNDGillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18. PMID 26579733
- BACKGROUNDYu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y, Wang Y, Chen L, Mao Y. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol. 2017 Aug;27(8):3509-3522. doi: 10.1007/s00330-016-4653-3. Epub 2016 Dec 21. PMID 28004160