In this study, the authors aimed to evaluate whether MRI-based radiomic features were able to improve the accuracy of survival predictions for lower grade gliomas over clinical isocitrate dehydrogenase (IDH) status. The authors extracted radiomic features from the preoperative MRI data of 296 lower grade glioma patients from their institution as well as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archives (TCIA). The study resulted in the finding that radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas.
Key points
- Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.
Article: Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction
Authors: Yoon Seong Choi, Sung Soo Ahn, Jong Hee Chang, Seok-Gu Kang, Eui Hyun Kim, Se Hoon Kim, Rajan Jain & Seung-Koo Lee
