Ignite Creation Date:
2024-06-16 @ 11:52 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06454097
Status:
RECRUITING
Last Update Posted:
2024-06-12
First Post:
2024-06-06
Brief Title:
Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas
Sponsor:
Beijing Tiantan Hospital
Organization:
Beijing Tiantan Hospital
Study Overview
Official Title:
Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 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
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 tumors 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 models 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
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 tumors 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 models 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None