GliomaNCT05739500
MRI-based Computer Aided Diagnosis Software (V1) for Glioma
An observational study of Glioma and Primary Brain Tumor, run by Mingge LLC at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Glioma, Primary Brain Tumor
- How many people
- 250 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2022to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment
Who may be able to join
- Age front 18 to 70 years old (not including threshold), gender is not limited;
- Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment;
- 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;
- The patient himself voluntarily participated and signed the informed consent in writing.
Who cannot join
- Patients who only underwent biopsy rather than surgical tumor resection;
- Postoperative pathologically confirmed non-glioma patients;
- Patients with multiple glioma metastases or multiple gliomas;
- Patients who died of complications in the early postoperative period;
- The researcher believes that this researcher should not be included.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The Clinical Trial 01 to Evaluate the Effectiveness of MRI-based Computer Aided Diagnosis Software (V1) for Glioma Segmentation, Gene Prediction and Tumor Grading
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.
Read the detailed description
BACKGROUND:
The molecular type is crucial for surgical planning and post-operative treatment of glioma. MRI-based radiomics is an emerging technique that extracts unrevealed information including pathology, biomarkers, and genomics by using automated high-throughput extraction of a large number of quantitative features. With the help of artificial intelligence, MRI-based radiomics could be a promising noninvasive method to reveal molecular type by using a quantitative radiomics approach for glioma.
AIM:
MRI-based computer-aided diagnosis software (V1) is an MRI-based radiomics tool with machine learning methods such as high-precision tumor segmentation and classification and discrimination modeling that 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.
PROCESS:
Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. They will undergo preoperative multimodal magnetic resonance imaging, which is the routine neuro-images of preoperative evaluation. After surgery, the patient's tumor tissue samples will undergo specialist genetic testing to obtain multiple molecular diagnostic results, such as isocitrate dehydrogenase (IDH), telomerase reverse transcriptase promoter (TERTp), the short arm chromosome 1 and the long arm of chromosome 19 (1p/19q), et al. The participants need to be followed up for 1-year after surgery. Also, their imaging data, genotype data, clinical history data, pathology data, and clinical follow-up data will be analyzed for the study.
The preoperative Multimodality imaging will be input to the software (V1), and glioma segmentation, gene prediction, tumor grading, and lifetime will be analyzed by the software. The results will be compared with the real-world clinical data double-blindly. In order to evaluate the estimation performance of the software, several indexes will be calculated including accuracy (ACC), sensitivity (SENS), and specificity (SPEC). Finally, form a promising set of user-friendly automatic glioma diagnosis and treatment systems for clinics.
- Conditions
- Glioma, Primary Brain Tumor
- Medical subject headings
- Glioma, Brain Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Other
no intervention, just observation
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy rate
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.
Measured: end of the study (one year after the surgery of the last participants).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Feb 2023
- Study start1 Dec 2022
- Primary completion23 Nov 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for GliomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case crossover
- Time perspective
- Prospective
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mingge LLC
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Huashan Hospital, Fudan University
- Responsible party
- Principal investigator, Zhifeng Shi, Huashan Hospital
- Investigators
- Zhifeng Shi, MD., Principal investigator, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05739500
- Sponsor's protocol ID
- MINGGE-SW-00001-V1-01
- Status last verified
- Feb 2023
- First submitted
- 28 Dec 2022
- Last updated
- 22 Feb 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Garcia 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.
PubMed 31254264Reardon 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.
PubMed 25560529Gillies 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.
PubMed 26579733Yu 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.
PubMed 28004160
Study at a glance
From the registry record, last updated by the sponsor on 22 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Clinical Trial 01 to Evaluate the Effectiveness of MRI-based Computer Aided Diagnosis Software (V1) for Glioma Segmentation, Gene Prediction and Tumor Grading
- ClinicalTrials.gov ID
- NCT05739500
- Acronym / sponsor ID
- MINGGE-SW-00001-V1-01
- Conditions
- Glioma, Primary Brain Tumor
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Mingge LLCIndustry (pharma, biotech, devices)
- Collaborators
- Huashan Hospital, Fudan University
- Study start
- 1 Dec 2022 (actual)
- Primary completion
- 23 Nov 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 22 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
BACKGROUND:
The molecular type is crucial for surgical planning and post-operative treatment of glioma. MRI-based radiomics is an emerging technique that extracts unrevealed information including pathology, biomarkers, and genomics by using automated high-throughput extraction of a large number of quantitative features. With the help of artificial intelligence, MRI-based radiomics could be a promising noninvasive method to reveal molecular type by using a quantitative radiomics approach for glioma.
AIM:
MRI-based computer-aided diagnosis software (V1) is an MRI-based radiomics tool with machine learning methods such as high-precision tumor segmentation and classification and discrimination modeling that 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.
PROCESS:
Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. They will undergo preoperative multimodal magnetic resonance imaging, which is the routine neuro-images of preoperative evaluation. After surgery, the patient's tumor tissue samples will undergo specialist genetic testing to obtain multiple molecular diagnostic results, such as isocitrate dehydrogenase (IDH), telomerase reverse transcriptase promoter (TERTp), the short arm chromosome 1 and the long arm of chromosome 19 (1p/19q), et al. The participants need to be followed up for 1-year after surgery. Also, their imaging data, genotype data, clinical history data, pathology data, and clinical follow-up data will be analyzed for the study.
The preoperative Multimodality imaging will be input to the software (V1), and glioma segmentation, gene prediction, tumor grading, and lifetime will be analyzed by the software. The results will be compared with the real-world clinical data double-blindly. In order to evaluate the estimation performance of the software, several indexes will be calculated including accuracy (ACC), sensitivity (SENS), and specificity (SPEC). Finally, form a promising set of user-friendly automatic glioma diagnosis and treatment systems for clinics.
Study design
- Observational model
- Case crossover
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment
Key inclusion criteria (4 listed)
- Age front 18 to 70 years old (not including threshold), gender is not limited;
- Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment;
- 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;
- The patient himself voluntarily participated and signed the informed consent in writing.
Key exclusion criteria (5 listed)
- Patients who only underwent biopsy rather than surgical tumor resection;
- Postoperative pathologically confirmed non-glioma patients;
- Patients with multiple glioma metastases or multiple gliomas;
- Patients who died of complications in the early postoperative period;
- The researcher believes that this researcher should not be included.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no intervention, just observation | Other | – |
Primary outcome
Accuracy rate
Time frame: end of the study (one year after the surgery of the last participants).
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Feb 2023.
- Background: PubMed 31254264Garcia 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.
- Background: PubMed 25560529Reardon 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.
- Background: PubMed 26579733Gillies 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.
- Background: PubMed 28004160Yu 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.
- Individual participant dataSharing planned: No
Related trials for Glioma
RecruitingPhase 1
A Feasibility Study to Evaluate the Safety of the TheraSphere Glioblastoma (GBM) Device in Patients With Recurrent GBM
Glioblastoma Multiforme, Recurrent Glioblastoma
Not yet recruitingEarly Phase 1
Multiparametric Metabolic and Hypoxic PET/MRI Imaging Substudy in Gliomas
Glioma
RecruitingPhase 2
A Study of VRT106 in Combination With Radiotherapy in Adult Patients With Diffuse Midline Glioma / Diffuse Intrinsic Pontine Glioma
Diffuse Midline Glioma, H3 K27M-Mutant, Diffuse Intrinsic Pontine Glioma
RecruitingPhase 1
A Study of RNA-lipid Particle (RNA-LP) Vaccines for Newly Diagnosed Pediatric High-Grade Gliomas (pHGG) and Adult Glioblastoma (GBM)
Adult Glioblastoma, High Grade Glioma
Not yet recruitingPhase 1
IL-8 Receptor-modified CD70 CAR T Cell Therapy in CD70+ Newly Diagnosed and Recurrent Pediatric High-grade Glioma (pHGG) and Newly Diagnosed Diffuse Intrinsic Pontine Glioma (ndDIPG)
High-grade Glioma, Diffuse Intrinsic Pontine Glioma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Zhifeng Shi, MD. principal investigator
Huashan Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Glioma and Primary Brain Tumor, run by Mingge LLC at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy rate (time frame: end of the study (one year after the surgery of the last participants).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05739500, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Feb 2023.