Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas

Trial Title: Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas

NCT ID: NCT06454097

Condition: Glioma

Conditions: Official terms:
Glioma

Study type: Interventional

Study phase: N/A

Overall status: Recruiting

Study design:

Allocation: N/A

Intervention model: Single Group Assignment

Primary purpose: Diagnostic

Masking: None (Open Label)

Intervention:

Intervention type: Diagnostic Test
Intervention name: Assess the response glioma to radiochemotherapy using radiogenomics-based AI model
Description: Predict the radiochemotherapy sensitivity of patients with glioma using an established radiogenomics-based artificial intellegent mode
Arm group label: Evaluate the response of patients with glioma to radiochemotherapy

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.

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).

Criteria for eligibility:
Criteria:
Inclusion Criteria: - 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 Exclusion Criteria: - Those who do not meet any of the inclusion criteria

Gender: All

Minimum age: 18 Years

Maximum age: N/A

Healthy volunteers: No

Locations:

Facility:
Name: Beijing Tiantan Hospital

Address:
City: Beijing
Zip: 100071
Country: China

Status: Recruiting

Contact:
Last name: Yinyan Wang, MD and PhD

Phone: +86 13581698953
Email: tiantanyinyan@126.com

Start date: January 23, 2024

Completion date: December 31, 2024

Lead sponsor:
Agency: Beijing Tiantan Hospital
Agency class: Other

Source: Beijing Tiantan Hospital

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06454097