Study on Radiogenomics Features Associated With Radiochemotherapy Sensitivity in Gliomas

NCT06454097 | Recruiting DMC

• 1 other identifier: 82072786
• Study Type: interventional
• Target: 200
• Locations: 1 country
• Sites: 1

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.

Conditions

Glioma

Outcome Measures

Primary Outcomes (3)

• 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)

• 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)

• 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 Outcomes (3)

• Accuracy of the AI model in predicting radiochemotherapy respone

  1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)

• Positive predictive value (PPV) of the AI model in predicting radiochemotherapy respone

  1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)

• Negative predictive value (NPV) of the AI model in predicting radiochemotherapy respone

  1 month after radiotherapy (LGG); 4 and 10 months after radiochemotherapy (HGG)

Study Arms (1)

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.

Diagnostic Test: Assess the response glioma to radiochemotherapy using radiogenomics-based AI model

Interventions

Assess the response glioma to radiochemotherapy using radiogenomics-based AI model DIAGNOSTIC_TEST

Predict the radiochemotherapy sensitivity of patients with glioma using an established radiogenomics-based artificial intellegent mode

Evaluate the response of patients with glioma to radiochemotherapy

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

Sponsors & Collaborators

• Beijing Tiantan Hospital: lead

Study Sites (1)

Beijing Tiantan Hospital

Beijing, Beijing Municipality, 100071, China

RECRUITING

MeSH Terms

Conditions

Glioma

Condition Hierarchy (Ancestors)

Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue

Study Officials

• Yinyan Wang, MD and PhD

  Beijing Tiantan Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yinyan Wang, MD and PhD

CONTACT

+86 13581698953; tiantanyinyan@126.com

Tao Jiang, MD and PhD

CONTACT

+86 10 67021832; taojiang1964@163.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 6, 2024
• First Posted: June 12, 2024
• Study Start: January 23, 2024
• Primary Completion: November 30, 2024
• Study Completion: December 31, 2024
• Last Updated: June 12, 2024

Record last verified: 2024-06

Locations

CN (1)