Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas

Glioma Clinical Trial

Official title:

Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06454097
• Other study ID #: 82072786
• Status: Recruiting
• Phase: N/A
• Start date: January 23, 2024
• Est. completion date: December 31, 2024

Study information

• Verified date: June 2024
• Source: Beijing Tiantan Hospital
• Contact: Yinyan Wang, MD and PhD
• Phone: +86 13581698953
• Email: tiantanyinyan[@]126.com
• Is FDA regulated: No
• Study type: Interventional

Clinical 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). 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.

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).

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: December 31, 2024
• Est. primary completion date: November 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Glioma

Intervention

Diagnostic Test:
• Assess the response glioma to radiochemotherapy using radiogenomics-based AI model
Predict the radiochemotherapy sensitivity of patients with glioma using an established radiogenomics-based artificial intellegent mode

Locations

Country	Name	City	State
China	Beijing Tiantan Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Beijing Tiantan Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sensitivity of the AI model in predicting radiochemotherapy respone	Sensitivity = TP/(TP+FN)	1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Primary	Specificity of the AI model in predicting radiochemotherapy respone	Specificity = TN/(TN+FP)	1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Primary	Area under the Receiver Operating Characteristic curve (AUC)	AUC measures the entire two-dimensional area underneath the entire ROC curve	1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Secondary	Accuracy of the AI model in predicting radiochemotherapy respone	Accuracy of radiotherapy sensitivity prediction AI model = (TP+TN)/ (TP+TN +FP+FN)	1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Secondary	Positive predictive value (PPV) of the AI model in predicting radiochemotherapy respone	PPV of radiotherapy sensitivity prediction AI model = [TP/(TP+FP)]*100	1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)
Secondary	Negative predictive value (NPV) of the AI model in predicting radiochemotherapy respone	NPV of radiotherapy sensitivity prediction AI model = [TN/(FN+TN)]*100	1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)