Summary of the Project
MRI Based Diagnostic Algorithm Powered By Artificial Intelligence
Treatment planning for patients with low-grade glioma tumor involves initial diagnosis using clinical and imaging data, predicting the degree of infiltration, localization and segmentation, surgery or biopsy, molecular analysis, and radiation (maybe with adjunct chemotherapy).
traditionally, this process is performed by skilled physicians which is time-consuming, subjective and prone to errors.The overall goal of this study is to provide a more accurate diagnostic insight about genomics of diffuse gliomas which may help in improving treatment outcomes.We propose to utilize artificial intelligence (AI) methods to predict the genomics of diffuse low-grade gliomas based on pre-operative multi-parametric MRI scans.
Big Data Collection
120 patients with suspected low-grade Glioma according to pre-operative MRI scans will be recruited in the study.
The patiens will undergo pre-operative multi-parametric MRI (including convential pre- and post-contrast T1-weighted, T2-weighted and T2-FLAIR images, and T2-FLAIR images, and advaced MRI comprising of DSC-MRI, DTI, IVIM, MRS, and CEST) on a 3T MRI scanner located at Imam Khomeini Hospital, Tehran, MRI scans will be pre-processed, quantified, and integrated for creation of predictive models.