GliomaNCT06454097
Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas
A study testing Assess the response glioma to radiochemotherapy using radiogenomics-based AI model in people with Glioma, run by Beijing Tiantan Hospital at 1 site in China.
Trial summary
The MRI data were collected from patients with gliomas before surgery, 2 weeks before initiating radiochemotherapy, 1 month after completing the radiotherapy (for lower-grade gliomas, LGG), or 4 and 10 months after completing the radiochemotherapy (for high-grade gliomas, HGG). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Glioma
- What is being tested?
- Assess the response glioma to radiochemotherapy using radiogenomics-based AI model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 23 Jan 2024to 31 Dec 2024
- Phase
- Interventional
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged 18 or older
- Histologically confirmed glioma
- No history of other brain tumors or previous cranial surgeries
- No history of preoperative radiotherapy or chemotherapy
- Available preoperative, pre-radiotherapy(postoperatively), and post-radiotherapy magnetic resonance imaging (MRI) data
Who cannot join
- Those who do not meet any of the inclusion criteria
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Beijing Tiantan Hospital
Beijing, Beijing Municipality, 100071
Yinyan Wang, MD and PhD, +86 13581698953, tiantanyinyan@126.com
About this study
The MRI data were collected from patients with gliomas before surgery, 2 weeks before initiating radiochemotherapy, 1 month after completing the radiotherapy (for lower-grade gliomas, LGG), or 4 and 10 months after completing the radiochemotherapy (for high-grade gliomas, HGG). Radiochemotherapy sensitivity labels were constructed based on the MRI images obtained before and after radiochemotherapy, following the RANO criteria. Radiomics features were extracted from preoperative MRI images and combined with transcriptomic information obtained from tumor tissue sequencing. This process allowed the construction of a radiogenomics model capable of predicting the response of gliomas to radiochemotherapy.
In this prospective cohort study, we will recruit patients with gliomas who have undergone craniotomy and received postoperative radiotherapy or radiochemotherapy (in cases of LGG and HGG, respectively). MRI images of the same sequences will be collected at corresponding time points, and transcriptomic sequencing will be performed on tumor tissue obtained during surgery. The established model will be applied to predict radiochemotherapy sensitivity and compared with the 'true' radiochemotherapy sensitivity labels, which are constructed based on the RANO criteria, to evaluate the predictive performance of the model.
Read the detailed description
This trial aims to recruit 100 cases of LGG and 100 cases of HGG based on statistical calculations. MRI data, including T1-weighted, T2-weighted, T1 contrast-enhanced, and T2-Fluid Attenuated Inversion Recovery (FLAIR) sequences, will be collected before surgery, 2 weeks before initiating radiochemotherapy, 1 month after completing the radiotherapy (LGG), or 4 and 10 months after completing the radiochemotherapy (HGG).
The collected MRI images before and after radiochemotherapy will be used to assess changes in tumor volume. The RANO criteria will be employed to determine the tumor's sensitivity to radiochemotherapy: a complete response and partial response will be classified as sensitive, while stable disease and disease progression will be considered insensitive.
Radiomics features will be extracted using the open-source 'PyRadiomics' python package after performing image preprocessing and segmentation. Transcriptomic data will be obtained by conducting RNA sequencing analysis on tumor samples collected during surgery. Selected radiogenomic features will be incorporated into a pre-constructed machine learning model to predict the sensitivity of gliomas to radiochemotherapy. The model's performance will be evaluated using metrics such as classification accuracy (ACC), area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV).
- Conditions
- Glioma
- Being tested
- Assess the response glioma to radiochemotherapy using radiogenomics-based AI model
- Medical subject headings
- Glioma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Assess the response glioma to radiochemotherapy using radiogenomics-based AI modelDiagnostic test
Predict the radiochemotherapy sensitivity of patients with glioma using an established radiogenomics-based artificial intellegent mode
| Study group | Role | What they receive |
|---|---|---|
| Evaluate the response of patients with glioma to radiochemotherapy | Other | The response of patients with glioma to radiochemotherapy will be assessed by the RANO criteria and the established radiogenomics-based artificial intellegent model. Receives: Diagnostic Test: Assess the response glioma to radiochemotherapy using radiogenomics-based AI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of the AI model in predicting radiochemotherapy respone
Sensitivity = TP/(TP+FN)
Measured: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Specificity of the AI model in predicting radiochemotherapy respone
Specificity = TN/(TN+FP)
Measured: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Area under the Receiver Operating Characteristic curve (AUC)
AUC measures the entire two-dimensional area underneath the entire ROC curve
Measured: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Secondary outcomes (3)
Accuracy of the AI model in predicting radiochemotherapy respone
Accuracy of radiotherapy sensitivity prediction AI model = (TP+TN)/ (TP+TN +FP+FN)
Measured: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Positive predictive value (PPV) of the AI model in predicting radiochemotherapy respone
PPV of radiotherapy sensitivity prediction AI model = [TP/(TP+FP)]*100
Measured: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Negative predictive value (NPV) of the AI model in predicting radiochemotherapy respone
NPV of radiotherapy sensitivity prediction AI model = [TN/(FN+TN)]*100
Measured: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jun 2024
- Study start23 Jan 2024
- Primary completion30 Nov 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GliomaStudies of the same condition, open and under way.
- More trials run by Beijing Tiantan HospitalThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Tiantan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yinyan Wang, MD and PhD, Principal investigator, Beijing Tiantan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06454097
- Sponsor's protocol ID
- 82072786
- Status last verified
- Jun 2024
- First submitted
- 6 Jun 2024
- Last updated
- 12 Jun 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas
- ClinicalTrials.gov ID
- NCT06454097
- Acronym / sponsor ID
- 82072786
- Conditions
- Glioma
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Beijing Tiantan HospitalUniversity, hospital or other
- Study start
- 23 Jan 2024 (actual)
- Primary completion
- 30 Nov 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 12 Jun 2024 (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 MRI data were collected from patients with gliomas before surgery, 2 weeks before initiating radiochemotherapy, 1 month after completing the radiotherapy (for lower-grade gliomas, LGG), or 4 and 10 months after completing the radiochemotherapy (for high-grade gliomas, HGG). Radiochemotherapy sensitivity labels were constructed based on the MRI images obtained before and after radiochemotherapy, following the RANO criteria. Radiomics features were extracted from preoperative MRI images and combined with transcriptomic information obtained from tumor tissue sequencing. This process allowed the construction of a radiogenomics model capable of predicting the response of gliomas to radiochemotherapy.
In this prospective cohort study, we will recruit patients with gliomas who have undergone craniotomy and received postoperative radiotherapy or radiochemotherapy (in cases of LGG and HGG, respectively). MRI images of the same sequences will be collected at corresponding time points, and transcriptomic sequencing will be performed on tumor tissue obtained during surgery. The established model will be applied to predict radiochemotherapy sensitivity and compared with the 'true' radiochemotherapy sensitivity labels, which are constructed based on the RANO criteria, to evaluate the predictive performance of the model.
Detailed description
This trial aims to recruit 100 cases of LGG and 100 cases of HGG based on statistical calculations. MRI data, including T1-weighted, T2-weighted, T1 contrast-enhanced, and T2-Fluid Attenuated Inversion Recovery (FLAIR) sequences, will be collected before surgery, 2 weeks before initiating radiochemotherapy, 1 month after completing the radiotherapy (LGG), or 4 and 10 months after completing the radiochemotherapy (HGG).
The collected MRI images before and after radiochemotherapy will be used to assess changes in tumor volume. The RANO criteria will be employed to determine the tumor's sensitivity to radiochemotherapy: a complete response and partial response will be classified as sensitive, while stable disease and disease progression will be considered insensitive.
Radiomics features will be extracted using the open-source 'PyRadiomics' python package after performing image preprocessing and segmentation. Transcriptomic data will be obtained by conducting RNA sequencing analysis on tumor samples collected during surgery. Selected radiogenomic features will be incorporated into a pre-constructed machine learning model to predict the sensitivity of gliomas to radiochemotherapy. The model's performance will be evaluated using metrics such as classification accuracy (ACC), area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV).
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Evaluate the response of patients with glioma to radiochemotherapy | Other | Diagnostic Test: Assess the response glioma to radiochemotherapy using radiogenomics-based AI modelThe response of patients with glioma to radiochemotherapy will be assessed by the RANO criteria and the established radiogenomics-based artificial intellegent model. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Patients aged 18 or older
- Histologically confirmed glioma
- No history of other brain tumors or previous cranial surgeries
- No history of preoperative radiotherapy or chemotherapy
- Available preoperative, pre-radiotherapy(postoperatively), and post-radiotherapy magnetic resonance imaging (MRI) data
Key exclusion criteria (1 listed)
- Those who do not meet any of the inclusion criteria
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 |
|---|---|---|
| Assess the response glioma to radiochemotherapy using radiogenomics-based AI modelPredict the radiochemotherapy sensitivity of patients with glioma using an established radiogenomics-based artificial intellegent mode | Diagnostic test | Evaluate the response of patients with glioma to radiochemotherapy |
Primary outcome
Sensitivity of the AI model in predicting radiochemotherapy respone
Time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Sensitivity = TP/(TP+FN)
Specificity of the AI model in predicting radiochemotherapy respone
Time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Specificity = TN/(TN+FP)
Area under the Receiver Operating Characteristic curve (AUC)
Time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
AUC measures the entire two-dimensional area underneath the entire ROC curve
3 secondary outcomes
Accuracy of the AI model in predicting radiochemotherapy respone
Time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Positive predictive value (PPV) of the AI model in predicting radiochemotherapy respone
Time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Negative predictive value (NPV) of the AI model in predicting radiochemotherapy respone
Time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jun 2024.
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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.
Yinyan Wang, MD and PhD principal investigator
Beijing Tiantan Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Assess the response glioma to radiochemotherapy using radiogenomics-based AI model in people with Glioma, run by Beijing Tiantan Hospital 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 5 inclusion and 1 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 12 Jun 2024. 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?
Contact Yinyan Wang, MD and PhD (+86 13581698953, tiantanyinyan@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of the AI model in predicting radiochemotherapy respone (time frame: 1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 12 Jun 2024.